text:
The 68th Best Director of All-Time: Terry Gilliam
Gilliam. There's no way he's on here ahead of many deserving auteurs if I didn't think Brazil was much closer to the 50th best film of all-time than the 183rd best where TSPDT consensus has it. Man, I wish he made both more overall films and more archiveable films- and without an archiveable film since 1998 I fear it may be over for Gilliam. Unmistakably, his case is hurt by having only 6 archiveable films but although there may be some misses along the way in his career there is no compromise in Gilliam. I adore his individual creative signature.
production design bliss in The Adventures of Baron Munchausen
Best film: Brazil. It's not only a massive "M" masterpiece and a top 10 film of the 1980's (I've got it at #8) but an unbelievably 'Gilliamesque' film. It's simultaneously one of the greatest fantasy films, sci-fi films, and comedy films of all-time.
Brazil blends surrealism fantasy, sci-fi dystopia, and savage comedy through a layered and transcendent production design effort
one of the most beautiful images in the 1985 masterpiece
top 100 films: 1 (Brazil)
top 500 films: 2 (Brazil, 12 Monkeys)
top 100 films of the decade: 4 (Brazil, 12 Monkeys, Fear and Loathing in Las Vegas, The Fisher King)
so much of Gilliam's work is shot at canted angles or manipulated lenses– check out the tilt here in The Fisher King
most overrated: none- The Imaginarium of Doctor Parnassus has decent enough reviews for a Gilliam film (he, like Lynch and von Trier have really poor metacritic average scores for their level of genius) but I have it out of the archives— I still need to give it another look.
most underrated: 12 Monkeys. It's been long enough and there's no good reason this fantastic film hasn't emerged on the top 1000 of all-time. Definitely underrated.
another dutch angle here in 12 Monkeys– effective and consistent– both in the film and Gilliam's oeuvre
meticulously designed decor– a sort of retro-fitted, garbage-art clutter — here in 12 Monkeys
gem I want to spotlight: Fear and Loathing in Las Vegas – speaking of underrated- somehow this is still "rotten" on RT. It's one of Depp and Del Toro's best performances and the drug use as surrealism is really a perfect marriage of Gilliam and Hunter S. Thompson.
wall-art gorgeous, married to the narrative (and Thompson's material), and wholly in Gilliam's custom worldview– here in Fear and Loathing
stylistic innovations/traits: Gilliam is both gothic and romantic. His films are directed with eccentric fervor and clearly a cartoonish fantasy. Typically his stories revolve around outsiders repelling against a rigid organization. There is surrealism and insanity in all of his films. These are showcased by brilliant editing, unconventional acting, costume, and a busy and deliciously hyperbolic mise-en-scene. He uses canted angles, wide fisheye lens, and a sort of retro-fitted, garbage-art clutter for his production design. Gilliam is a unique maverick.
fisheye lens manipulation to great effect here in Brazil
The Fisher King
The Adventures of Baron Munchausen
fantasy and surrealism through style and decor in Baron Munchausen
1981- Time Bandits R
1985- Brazil MP
1988- The Adventures of Baron Munchausen HR
1991- The Fisher King HR
1995- Twelve Monkeys MS
1998- Fear and Loathing In Las Vegas HR
m January 9, 2020 at 6:14 am - Reply
its not my fvavrotie movie, i need to watch it again though but im surprised monty python and the holy grail isnt on here because it is a super loved film and quite orignal
Drake January 9, 2020 at 1:07 pm - Reply
@m – Thanks for visiting the page here. Yeah it is a little surprising. I saw it once and didn't think it was very good. I'm probably due for a rewatch.
The 250 Best Directors of All-Time - The Cinema Archives August 17, 2020 at 9:34 pm - Reply
[…] 68. Terry Gilliam […]
James Robbins September 2, 2020 at 2:23 am - Reply
I'd move him up like 20 spots, I know he's inconsistent but man, when he's on he's really on. I mean he is just so visually creative and unique. I'd love to see him and David Lynch make a movie together.
Dylan November 20, 2020 at 1:55 am - Reply
I hope I like Brazil on my next rewatch of it soon, I never liked it every time I tried it. 12 Monkeys is the only film of his I love, I rate it as one of the best movies of the 90s, 3rd best in 1995 after Seven/Heat. Absolutely hated his Holy Grail movie, couldn't get through that garbage. I've never seen Brazil all the way through, I always stop, but I will try to watch it all the way through.
Malith March 19, 2021 at 5:38 am - Reply
Are The Zero Theorem(2013) and The Brothers Grimm(2005) the only unarchivable films of Terry Gilliam?
Drake March 19, 2021 at 1:16 pm - Reply
@Malith- The Zero Theorem was just added.
Harry June 2, 2021 at 7:42 pm - Reply
Next time you update this list will Gilliam jump up 5-7 spots? I see you've added 6 more of his films to the archives and upgraded Fisher King, Fear & Loathing and Baron Munchausen to MS's. That's a solid change in how his films are seen.
Malith June 2, 2021 at 8:12 pm - Reply
Not 6. 7 I think. The Meaning of Life is regarded as co directed by Gilliam because he directed the opening prologue The Crimson Permanent Assurance segment. So he has 13 archivable films.
Drake June 2, 2021 at 9:09 pm - Reply
@Harry- Yes, I think that sounds about right.


The Deprivation of Liberties Safeguards (DOLS) are part of the Mental Capacity Act (MCA) and ensure the safety and support of vulnerable adults in care. This online training is designed for staff in the care sector, and raises their awareness of the MCA and DOLS guidelines so that they can remain legally complaint. As well as an understanding of this legislation, delegates will be informed of the process of assessing mental capacity and making informed decisions based on their findings.
Comply with the Mental Capacity Act (MCA).
Understanding the Deprivation of Liberties Safeguards (DOLS).


Pam fell in love with interiors and scale drawing at a young age. Her grandfather, a civil engineer, designed bridges and towers and her grandmother had a passion for antiques and interior design. Her grandfather would stop at antique stores on his business trips to find treasures for Pam's grandmother. Pam grew to greatly appreciate beautiful furniture and accessories and made it into her career.
With almost 20 years of experience in interior design, Pam finds joy in creating welcoming spaces for her clients to come home to that represent who they are. She believes a home should lift you up! Her key to great design is selecting pieces her clients love and that are comfortable for their lives.
In her free time, Pam enjoys gardening, reading and spending time with her husband, Hank, and daughter, Jordan.
Contact Pam today for a consultation.


text:
The rector of the University of Oslo is the university's highest officer, who serves as both its chief executive, its ceremonial head and as chairperson of the university board. The rector is directly elected among the (full) professors by all the members of the university community, that is academic employees, students and technical-administrative staff, who received the right to vote in this order. Until 1989, only professors were eligible to stand for election, although so far all rectors have been professors at the university. The rector's deputy and the university's second highest official, the pro-rector, is also directly elected at the same time. Both the rector and the pro-rector are elected for four-year terms and may stand for reelection to the same office once. The rectorate may also include one or more appointed vice-rectors, who rank below both the rector and the pro-rector. The rectorate forms the university's senior leadership and has the overall responsibility for the management of the university; they are assisted by the director of the university, who heads the technical-administrative staff, but who is subordinate to the rectorate (the rector, pro-rector and vice-rectors).

The office of rector was instituted by law in 1905, and made effective in 1907. From 1845 to 1907, the Collegium academicum, the highest body of the university, elected a chairman among its members to serve for one year. From 1814 to 1845, the university had a Chancellor, which was only a ceremonial office held by the Lord Lieutenant (stattholder), and a Pro-Chancellor, who was the deputy of the Chancellor and ex officio chairman of the Collegium academicum.

Chancellors, 1814–1844

Pro-Chancellors, 1814–1845

Chairmen of the Collegium academicum, 1845–1907

Rectors, 1907–present

1: Fridtjof Nansen was elected rector of the university for the term 1919–1921, but never assumed office.
2: Following the arrest of Didrik Arup Seip during the occupation of Norway by Nazi Germany, the occupying powers appointed the polar researcher Adolf Hoel as rector of the university.

Pro-Rectors
 1941–1943 Professor Adolf Hoel (Mathematics and Natural Sciences)1
 1957 Professor Harald Ulrik Sverdrup (Mathematics and Natural Sciences)
 1957–1960 Professor Alf Sommerfelt (Humanities)
 1961–1963 Professor Johs. Andenæs (Law)
 1964–1966 Professor Axel Strøm (Medicine)
 1967–1969 Professor Alf Brodal (Medicine)
 1970–1972 Professor Otto Bastiansen (Mathematics and Natural Sciences)
 1973–1976 Professor Knut Kolsrud (Humanities)
 1977–1980 Professor Jacob Jervell (Theology)
 1981–1984 Professor Knut Erik Tranøy (Humanities)
 1985–1988 Professor Bjørn Pedersen (Mathematics and Natural Sciences)
 1989–1992 Professor Jens Erik Fenstad (Mathematics and Natural Sciences)
 1993–1995 Professor Arild Underdal (Social Sciences)
 1996–1998 Professor Knut Fægri (Mathematics and Natural Sciences)
 1999–2001 Professor Rolv Mikkel Blakar (Social Sciences)
 2002–2005 Professor Anne-Brit Kolstø (Mathematics and Natural Sciences)
 2006–2009 Professor Haakon Breien Benestad (Medicine)
 2009–2013 Associate Professor Inga Bostad (Humanities)
 2013–2014 Professor Ruth Vatvedt Fjeld (Humanities)2
 2014–2017 Professor Ragnhild Hennum (Law)

1: Following the arrest of Didrik Arup Seip during the occupation of Norway by Nazi Germany, the occupying powers appointed the polar researcher Adolf Hoel as prorector, and later rector of the university.
2: Ruth Vatvedt Fjeld resigned from her position in 2014.

References
 University of Oslo's list

Oslo, University of

redpajama_set_name: RedPajamaWikipedia


text:
The 2010 season was the 19th full year of competitive football in Estonia as an independent nation.

Matches
The team played total of thirteen – four UEFA Euro 2012 qualifying, two Baltic Cup and seven friendly – matches, of which they won three, drew four and lost six. They scored thirteen goals, averaging one goal per match, and conceded eighteen in these games.

Georgia vs Estonia

Estonia vs Finland
It was the farewell match for Indrek Zelinski, who acquired total of 103 caps and 27 goals. Sander Post scored his first international goal, which helped Estonia overcome Finland for the first time in 73 years. Karl Palatu was capped for the first time.

Estonia vs Croatia

Latvia vs Estonia
Sergei Mošnikov was capped for the first time.

Lithuania vs Estonia

Estonia vs Faroe Islands

Estonia vs Italy

Estonia vs Uzbekistan

Serbia vs Estonia

Estonia vs Slovenia

Estonia vs Liechtenstein
Siim Luts was capped for the first time.

China vs Estonia
Rauno Alliku, Markus Jürgenson and Marko Meerits were capped for the first time.

Qatar vs Estonia

Players
These 38 players were capped during the 2010 season (listed alphabetically):

 Mihkel Aksalu
 Rauno Alliku
 Alo Bärengrub
 Aleksandr Dmitrijev
 Alo Dupikov
 Enar Jääger
 Markus Jürgenson
 Gert Kams
 Tarmo Kink
 Ragnar Klavan

 Oliver Konsa
 Artur Kotenko
 Dmitri Kruglov
 Pavel Londak
 Siim Luts
 Marko Meerits
 Igor Morozov
 Sergei Mošnikov
 Andres Oper
 Karl Palatu

 Sergei Pareiko
 Raio Piiroja
 Sander Post
 Eino Puri
 Sander Puri
 Ats Purje
 Taavi Rähn
 Kaimar Saag
 Andrei Sidorenkov
Andrei Stepanov

 Tihhon Šišov
 Vjatšeslav Zahovaiko
 Indrek Zelinski
 Sergei Zenjov
 Taijo Teniste
 Konstantin Vassiljev
 Vladimir Voskoboinikov
 Martin Vunk

Goalscorers
4 goals
 Konstantin Vassiljev (two penalties)

2 goals
 Ats Purje

1 goals
 Tarmo Kink
 Andres Oper
 Raio Piiroja
 Sander Post
 Kaimar Saag
 Sergei Zenjov

Own goal
  Aleksandar Luković

Debutants
 Karl Palatu (#214) – on 21 May in a friendly match against Finland
 Sergei Mošnikov (#215) – on 19 June in a Baltic Cup match against Latvia
 Siim Luts (#216) – on 17 November in a friendly against Liechtenstein
 Rauno Alliku (#217–218) – on 18 December started in a friendly against China
 Markus Jürgenson (#217–218) – on 18 December started in a friendly against China
 Marko Meerits (#219) – on 18 December came on as a substitute in a friendly against China
All six were FC Flora Tallinn players at the time of their debuts.

References
All national team games

2010
2010 national football team results
National


text:
\subsection{Related Work}
\noindent \textbf{Submodular Maximization:} Combinatorial optimization is the process of searching for the maxima (or minima) of an objective function whose domain is a discrete space. Among these discrete optimization scenarios, submodular functions are attractive for a wide variety of problems because of their diminishing return property, which yields polynomial time algorithms with optimal approximation rates. In contrast with monotone functions, non-monotone functions fit well to the underlying mechanics of real-world systems. For the maximization of non-monotone submodular functions, \cite{feige2011maximizing} has proved that for the unconstrained setting, it is NP-hard to obtain better than a $\frac{1}{2}$-approximation in the value oracle sense. \cite{6375344} provides a randomized algorithm that achieves exactly this rate. \cite{gillenwater2014maximization} summarizes the recent developments in the maximization of non-monotone submodular functions. Similar to our work, \cite{9186137} has applied submodular optimization with cardinality constraints to feature selection on regression tasks.

\noindent
\textbf{Low-bit width neural networks:} Substantial research efforts have been devoted in recent years to optimizing BNNs as measured by latency and energy-efficiency \cite{Chang}. \cite{qin2020binary} has summarized binarization methods into two categories: \begin{inparaenum}[(i)] \item naive BNNs, which are based on quantization over the weights and activations, \item optimization-based approaches, e.g.\ minimizing the quantization error, improving the network loss function, and reducing the gradient error\end{inparaenum}. Additionally, there have been several attempts to ternarize the weights e.g.\ $\{+\alpha, 0 , -\alpha\}$ \cite{lin2015neural,li2016ternary}.

Our work operates in the same solution space of binary, ternary, and higher fixed bit width weights, but approaches the problem from the perspective of combinatorial optimization for the first time.

\section{Problem Statement} \label{section: network_model}
We begin by presenting the network model and summarizing notation used in this paper. For the binary classification task, we consider the following bias-free two-layer fully connected feedforward neural network $f:\mathbb{R}^{d} \rightarrow \mathbb{R}$ with d-dimensional input $x\in \mathbb{R}^{d}$ and $h$ hidden units
\begin{equation}\small
    f(x) = a \sigma(W^Tx)= \sum_{j=1}^{h} a_j \sigma(\langle  w_j, x \rangle) \label{eq:2_layer_NN}
\end{equation}
where $w_j \in \{\alpha, \beta\}^{d}$ is the weights corresponding to neuron $j$, $a_j \in \mathbb{R}$ is the $j$-th coefficient of the second layer, and $\sigma(\cdot)$ is the ReLU function.
\begin{figure}[htb]
\vspace{-0.2cm}
\centering
\includegraphics[width=0.35\textwidth]{figs/visualize/2layernetwork.png}
\caption{Bias-free two-layer network}
\label{fig:2LayerNetwork}
\vspace{-0.5cm}
\end{figure}
For concise notation, we write $a = [a_1, \cdots, a_h] \in \mathbb{R}^{h}$ for the coefficients of the second layer, $W = [w_1, \cdots, w_h] \in \{\alpha, \beta\}^{h\times d}$ for the weight matrix, $X = [ x_1, \cdots, x_n]$ for the data matrix, $y\in \{-1,+1\}^{n}$ for the labels.

The approximation of the desired prediction function $f: \mathcal{X} \to \mathcal{Y}$ $(\mathbb{R}^{d} \to \mathbb{R})$ is made based on a training set of $n$ observations $D_n = \{(x_i,y_i)\}_{i =1}^n$ that are assumed to satisfy the independent and identically distributed (i.i.d.) sampling assumption \cite{vapnik2013nature}. The weights of this model are learned by minimizing the empirical risk of the available data:
\begin{equation}\small \label{eq:empiricalRisk}
    \mathcal{R}_{emp}(f):=\frac{1}{n} \sum_{i=1}^{n} \ell \left(y_{i}, f\left(x_{i}\right)\right)
\end{equation}

where $\ell(y,f(x))$ is the loss incurred by outputting $f(x)$ when $y$ is the true label. Typically, this loss is most often convex in its second argument i.e. squared error or logistic loss, etc, and it can be viewed as a set function where we map from a set indicating which elements of $w$ are equal to $\alpha$, to the empirical risk of the weights represented by that set. This is a key observation that allows us to analyze the problem from the perspective of submodular analysis. We are interested in the underlying structure of this empirical risk such that it can be solved efficiently via combinatorial algorithms.
\section{Method}
We begin this section with a brief introduction to submodularity and convexity, and then give our main theorems and outline the general arguments. We first consider a single-layer neural network and introduce the theorems and algorithms for learning this model.

\subsection{Submodularity and Convexity}
Submodular functions play an important role in combinatorial optimization when minimizing (or maximizing) a set function defined on the power set $\mathcal{P}(V)$, similar to convex functions on continuous spaces.
\begin{definition}[Submodularity] \label{DefinitionSubmodularity}
A set function $f: \mathcal{P}(V) \rightarrow \mathbb{R}$ is submodular if and only if for all $B \subseteq A \subset V$ and $x \in V \backslash B,$ it holds
\begin{align}\small
f(B \cup\{x\})-f(B) \geq f(A \cup\{x\})-f(A) .
\end{align}
\end{definition}
A function $f$ is supermodular if $-f$ is submodular.
\begin{definition}[Convexity] \label{DefinitionConvexity}
A function $g : \mathbb{R}^p \rightarrow \mathbb{R}$ is convex if for all $x_1,x_2 \in \mathbb{R}^p$
\begin{align*}\small
    g\left(\frac{1}{2} (x_1+x_2) \right) \leq \frac{1}{2} \left( g\left(x_1\right) + g\left(x_2\right) \right)  .
\end{align*}
\end{definition}

\subsection{Learning Linear Functions}
 We can express the output of the single-layer network as $f(x) = \langle w, x\rangle$, where $w$ is in the discrete domain $\{\alpha, \beta \}^{d}$ (without loss of generality, we may assume that $\alpha< \beta$). Let $\Omega=\{1,\cdots,d\}$ and $S=\{i: w_i =\beta \}$ indicate the entries of $w$ that equal $\beta$, the loss function $L(S):2^{\Omega}\rightarrow \mathbb{R}$
\begin{equation}\small
\begin{aligned}
L(S) =\sum_{i=1}^n \log\left(1+\exp\left(-y_i \langle w, x_i\rangle\right)\right)=:g(w) \label{eq:logistic_reg}
\end{aligned}
\end{equation}
is a set fuction of $S$ where $\ell(\cdot)$ is taken as logistic loss. Finding $\min_{S \subseteq \Omega} L(S)$ for this set function is a NP-hard optimization problem since it generalizes max-cut.

\subsubsection{Greedy Coordinate Descent}
 Inspired by \cite{khuller1999budgeted}, we present a naive greedy coordinate descent (GCD) procedure to learn single-layer binary neural networks via coordinate descent (Algorithm ~\ref{algForwardGreedy}, supplementary material). Essentially, it starts with a weight vector $w^0= \{\alpha,\beta\}^d$ corresponding to a set $S_0$, and greedily makes change to its coordinate one by one by comparing their feed-forward evaluation loss on the training set, and stops after iterating through all the coordinates of $w$. Noticing that the change in $g(w)$ upon changing $w[i]$ from $\alpha$ to $\beta$ is actually is incurred by $\langle \Delta w, x\rangle = (\beta-\alpha)x[i]=\mathcal{O}(1)$ where $w[i]$ represents the $i$th entry of $w$. Therefore, this algorithm can be employed in the minimum time complexity $\mathcal{O}(nd)$ even in the absence of any underlying structure of the objective function, yet it does not necessarily provide any theoretical guarantees.

\subsubsection{Supermodular Minimization}

\begin{proposition} \label{prop:1}
Given an input vector $x\in \mathbb{R} ^d$, if $g : \mathbb{R} \rightarrow \mathbb{R}$ is convex and $x \geq \mathbf{0}$ or $x \leq \mathbf{0}$ element wise, then $g(\langle w, x\rangle)$ is supermodular for $w \in \{0,1\}^d$.
\end{proposition}
\vspace{-0.1cm}
\begin{proof}
We have $s(A) = \langle w, x\rangle$ with a set $A=\{i: w_i\neq 0\}$. Hence, if $x \geq \mathbf{0}$, we have $s(A)\in\mathbb{R}_{+}$ for all set $A$. From  \cite[Proposition~6.1]{bach2011learning}, if $s\in\mathbb{R}_{+}^{p}$, and $g : \mathbb{R}_{+} \rightarrow \mathbb{R}$ is a concave function, then $F:A\mapsto g(s(A))$ is submodular.  Hence, for $x \geq \mathbf{0}$, $F:A \mapsto -g(s(A)) $ is submodular, thus the set function $g(\langle w, x\rangle)$ is supermodular. In the case of $x \leq \mathbf{0}$, take $s(A) = -\langle w, x\rangle \in \mathbb{R}_{+}$ and using the first result, we have that the set function $g(\langle w, x\rangle)$ is supermodular.
\end{proof}
\noindent Moreover, one can show that if $x\in \mathbb{R}^d$ is mixed (its entries contain both positive and negative values), then $g(\langle w, x\rangle)$ is neither submodular nor supermodular in $w$. The following proposition shows that, one can find a convex upper bound of the linear classifier $f(x) = \langle w, x\rangle $ that is supermodular in its weight vector $w \in \{0,1\}^d$. For a vector $x\in \mathbb{R}^d$, we define
$ \operatorname{pos}(x) := \max(0,x)$ where the $\max$ is applied element wise.
We also define $\operatorname{neg}(x) = - \operatorname{pos}(-x).$
\vspace{-0.1cm}
\begin{proposition} \label{prop:mixedX}
Consider a simple linear classifier $f(x) = \langle w, x\rangle $ with a convex surrogate $\ell$ that is an upper bound on the 0-1 loss: $\ell(y\langle w, x\rangle) \geq [y\langle w, x\rangle \leq 0]$ ($[\cdot]$ being Iverson bracket notation), then
\begin{align*}\small
    \frac{1}{2}\left( \ell(y\langle w, \operatorname{pos}(x)\rangle) + \ell(y\langle w,\operatorname{neg}(x)\rangle) \right) \geq [y\langle w, x\rangle \leq 0]
\end{align*}
and the left hand side is supermodular in $w$.
\end{proposition}
\begin{proof}
Since $\ell$ is convex, then by the definition of convexity (Def.~\ref{DefinitionConvexity}), we have
\begin{equation*}\small
\begin{aligned}
 &\frac{\ell \left(y \langle w,\operatorname{pos}(x)\rangle\right) + \ell\left(y\langle w,\operatorname{neg}\left(x\right)\rangle \right) }{2} \\ & \geq \ell \left( \frac{y \langle w,\operatorname{pos}(x)\rangle+ y \langle w,\operatorname{neg}(x)\rangle }{2} \right)= \ell\left(\frac{y\langle w,x\rangle}{2}\right) \\ \label{eq:pos_neg}
 &\geq \left[ \frac{y\langle w,x\rangle}{2} \leq 0\right]= \left[ y\langle w,x\rangle \leq 0\right],
\end{aligned}
\end{equation*}
where the definition of convexity implies the first inequality. Then we show the left hand side is supermodular in $w$. Given $\ell(\cdot)$ is a convex with $\operatorname{pos}(x)\geq 0$ and $\operatorname{neg}(x)\leq 0$, both $\ell(y\langle w, \operatorname{pos}(x)\rangle)$ and $\ell(y\langle w, \operatorname{neg}(x)\rangle)$ are supermodular in $w$ (because of Prop.~\ref{prop:1}). The left hand side is a positive linear combinations of supermodular functions, hence it is supermodular in $w$ by closedness properties \cite[Section~2.1]{bach2011learning}.
\end{proof}
\vspace{-0.3cm}

\noindent We now turn to the basic conditions we impose on $w$ such that the loss function can be considered as a real-valued set function. Obviously, this is not a minimal condition: below we show that Prop.~\ref{prop:1} holds for any binary value $w\in \{\alpha,\beta\}^d$ ($\alpha \neq\beta$).

\begin{proposition} \label{prop:alphabeta}
Given $g: \mathbb{R} \rightarrow \mathbb{R}$ is a concave function and $x \geq \mathbf{0}$ or $x \leq \mathbf{0}$ element wise, the optimization for $g(\langle w, x\rangle)$ is also submodular for $w\in \{\alpha,\beta\}^d$ ($\alpha < \beta$).
\end{proposition}
\vspace{-0.1cm}
\begin{proof}
 When $w\in \{\alpha,\beta\}^d$, we can express $ g\left(\langle w, x\rangle\right)$ as a set function $g\left(s\left(B\right)\right)$ with $s(B) = \langle w, x\rangle$ related to the set $B=\{i: w_i = \beta \}$. We define a vector $w'$ that satisfies $w_i^{'} =0 $ if $i\in B$ and $w_i^{'} =1 $ if $i \neq B$ and a related set $A=\{i: w_i^{'} = 0 \}$. Thus we have $A=B$
 \begin{equation*}\small
     \begin{aligned}
         s(A) &= \langle w^{'}, x\rangle = \sum_{j\in A}[x]_j \\
         s(B) & =\langle w^{}, x\rangle = \alpha  \sum_{j\in V\backslash A}[x]_j+\beta \sum_{k\in A }[x]_k  \\
         &= \alpha \sum_{j\in V }[x]_j + \left(\beta-\alpha\right) \sum_{k\in A}[x]_k.
     \end{aligned}
 \end{equation*}
 Thus, $g\left(s\left(B\right)\right)$ can be rewritten as:
 \begin{equation*}\small
 \begin{aligned}
     g( s(B)) =g \left( \alpha \sum_{j\in V }[x]_j + \left(\beta-\alpha\right) \sum_{k\in A}[x]_k \right)
 \end{aligned}
 \end{equation*}
 where the first term $\alpha \sum_{j\in V }[x]_j$ can be viewed as a constant $c$. Thus given $g(t): \mathbb{R} \rightarrow \mathbb{R}$ is concave, the function $g  \left(c + \left(\beta-\alpha\right)t\right)$ is also a concave function of $t$. We can apply $x \geq \mathbf{0}$ to the condition in Prop.~\ref{prop:1}, hence the submodularity of $g(\langle w, x\rangle)$ for $w\in \{\alpha,\beta\}^d$.
\end{proof}
\vspace{-0.3cm}

\noindent The logistic loss \eqref{eq:logistic_reg} of the single-layer binary neural network, because of Proposition~\ref{prop:alphabeta}, is supermodular in $w\in \{\alpha,\beta\}^d$. Algorithm~\ref{algNaiveSubmodular} proposed by \cite{6375344} is adopted for minimizing this supermodular loss, which leads to exactly an expected $\frac{1}{2}$-approximation guarantee. Notice that the change in $g(\cdot)$ upon replacing the $i$th element of $w_E$ with $\beta$ is incurred by $\Delta w^Tx = (\beta-\alpha)[x]_i=\mathcal{O}(1)$, leading to a running time $\mathcal{O}(nd)$.
\begin{algorithm}[H]\small
 \SetAlgoLined
\SetKwFor{With}{with}{do}{}
\SetKwInOut{Input}{input}
\SetKwInOut{Output}{output}
\caption{Randomized Supermodular Minimization}
\Input{Dataset $D_n: \mathbb{R}^{d}\times \mathbb{R}$; binary value \{$\alpha$, $\beta$\}; supermodular function: $g(w)$}
\Output{weight vector $w_{E}^{d}(\text{or }w_{F}^{d}) \in \{\alpha,\beta\}^d$}
\BlankLine
\emph{$E \leftarrow \emptyset \text{; } F \leftarrow \Omega$; $w_{E}^0\in \{\alpha\}^d$; $w_{F}^{0}\in \{\beta\}^d$}\\
\For{$i\leftarrow 1$ \KwTo $d$}{
$w_E^{i}[i]\leftarrow \beta $ and $w_F^{i}[i]\leftarrow \alpha$ \\%\tcp*{update the $i$th element of $w_E$ and $w_{F}$ }
$a_i = g(w_{E}^{i-1})- g(w_{E}^i)$, $b_i = g(w_{F}^{i-1}) -g(w_{F}^i)$\\
$a_i^{'} = \max(0, a_i) \text{, } b_i^{'} = \max(0, b_i)$\\
\With{probability $\frac{a^{\prime}_i}{a^{\prime}_i+b^{\prime}_i}$}{
$w_F^i \leftarrow w_F^{i-1}$
} \Other{  $w_E^i\leftarrow w_E^{i-1}$ }
}
\label{algNaiveSubmodular}
\end{algorithm}
\vspace{-0.5cm}

\subsubsection{Learning ternary and low-bit width weights}
\begin{proposition} [Ternary weights] \label{Prop:ternaryweights}
Given an input vector $x\in \mathbb{R} ^d$, if $g : \mathbb{R} \rightarrow \mathbb{R}$ is a convex function and $x \geq \mathbf{0}$ or $x \leq \mathbf{0}$ element wise, We define $w = w_1-w_2 \in \{-1,0,1\}^d$ with $w_1, w_2\in\{0,1\}^d$, then
$$\hat{g}(\langle w, x\rangle) = \frac{g(\langle w_1, 2x\rangle) + g(\langle w_2, -2x\rangle)}{2} $$
is a convex upper bound of $g(\langle w, x\rangle)$ that is supermodular in $w_1, w_2\in\{0,1\}^d$.
\end{proposition}
\begin{proof}
Applying Definition~\ref{DefinitionConvexity} of convexity, we have
\begin{equation*}\small
\begin{aligned}
   \underbrace{g\left( \frac{\langle w_1, 2x\rangle - \langle w_2, 2x\rangle }{2}\right)}_{g(\langle w, x\rangle)} & \le  \underbrace{\frac{g(\langle w_1, 2x\rangle) + g(\langle w_2, -2x\rangle)}{2}}_{\hat{g}(\langle w, x\rangle)}
\end{aligned}
\end{equation*}
where Prop.~\ref{prop:1} implies the right hand side is supermodular in $w_1, w_2\in\{0,1\}^d$.
\end{proof}
\vspace{-0.5cm}

\noindent Proposition~\ref{Prop:ternaryweights} implies that one can construct a supermodular upper bound for ternary weights which can be viewed as the difference between two binary vectors $w_1, w_2\in\{\alpha,\beta\}^d$. When fixing $w_1$, the loss function~\eqref{eq:logistic_reg} is supermodular in $w_2$, and vice versa. Thus, coordinate descent can be incorporated with Algorithm~\ref{algNaiveSubmodular} to learn this weight. Moreover, we could further split $w_1$ into $w_{11}$, $w_{12} \in \{\alpha,\beta\}^d$, thus quantizing the models with more bits.

\begin{comment}
\begin{algorithm}[H]\small
\DontPrintSemicolon
\SetAlgoLined
\SetKwInOut{KwInit}{Initialization}
\KwIn{$n\_iter$, $w_2^{0}\in\{\alpha, \beta\}^d$, supermodular function: $g(w)$}
\KwOut{$w = w_1^{n\_iter}-w_2^{n\_iter}$ }
\BlankLine
\caption{RSM for ternary weights}
\For{$i = 1, \cdots, n\_iter$}{
$w_1^{i}\leftarrow $ RSM($D_n,\{\alpha, \beta\}, g(w_1-w_2^{i-1})$)  while fixing $w_2^{i-1}$\\
$w_2^{i}\leftarrow$ RSM($D_n,\{\alpha, \beta\}, g(w_1^{i}-w_2)$) while fixing $w_1^{i}$ \;
}
\label{algcoordinatedescent}
\end{algorithm}
\end{comment}

\section{Multi-Layer Networks}
In this section, we summarize the construction of a convex upper bound of the loss function for the multi-layer networks. In particular, we are interested in the case when this upper bound is supermodular such that it can be solved efficiently via various supermodular minimization algorithms.
\vspace{-0.2cm}
\subsection{Two-Layer Networks} \label{Section:2_layer_NN}
\vspace{-0.2cm}
For the bias-free two-layer fully connected feedforward neural network with a ReLU activation in Section \ref{section: network_model}, it aims to minimize the empirical risk with respect to the weight matrix $W$:
\begin{align}
    \ell(y,f(X)) = \sum_{i=1}^n \log\left(1+\exp\left(-y_if\left(x_i\right)\right)\right) \label{eq:logisticLoss}
\end{align}
where $\ell(\cdot)$ is taken as logistic loss. Based on Equation~\eqref{eq:2_layer_NN} and \eqref{eq:logisticLoss}, when fixing $a$, we can view the loss function as a set function of the entries of $W$. In particular, we are interested in the loss for observation $(x_i,y_i)$ with respect to the weights $w_k$ corresponding to the neuron $k$ while keeping other weights in the first layer being fixed:
\begin{equation}\small \label{eq:twoLayerLoss}
\begin{aligned}
    L(S_k) &
      = \underbrace{\log\left(1+\exp\left(-y_i a_k \sigma\left( \langle  w_k, x_i \rangle\right) -  C   \right)\right) }_{=: g(\langle  w_k, x_i \rangle)}
\end{aligned}
\end{equation}
which can be viewed as a real-value set function of a set $S_k =\{i: [w_k]_i=\beta\}$ with a constant $C = y_i \sum_{j \neq k }^{h} a_j \sigma\left(\langle  w_j, x_i \rangle\right)$. For the sake of simplicity, we assume $x_i$ is non-negative, thus the characteristic of this function can be analyzed by considering the two following two cases.
\begin{inparaenum}[(i)]\small
    \item $y_ia_k \leq 0:$  $g(t)=\log\left(1+\exp\left(p\max\left(0,t\right)\right)\right)$($p>0$) is convex in $t$. Then by directly applying Prop.~\ref{prop:alphabeta}, this loss function is supermodular for $w_k\in \{\alpha, \beta\}^{d}$.
    \item $y_ia_k > 0:$ $g(t)=\log\left(1+\exp\left(p\max\left(0,t\right)\right)\right) (p<0)$ is not convex due to the flattened part where $t<0$. As depicted in Fig.~\ref{fig:approximateLoss},  we can replace the non-convex flat portion of the loss curve with a convex upper bound $\tilde{g}(t)$: the tangent line at $t=0$ (red line) or the function without ReLU activation (blue line), which leads to supermodularity for $w_k\in \{\alpha, \beta\}^{d}$ from Prop.~\ref{prop:1}.
\end{inparaenum}

\begin{figure}[H]
\vspace{-0.2cm}
\begin{center}
\includegraphics[width=0.23\textwidth]{figs/visualize/gt_approx.png}
\caption{Two convex upper bounds for $g(t)=\log\left(1+\exp\left(p\max\left(0,t\right)\right)\right) (p<0)$.}
\label{fig:approximateLoss}
\end{center}
\vspace{-0.6cm}
\end{figure}

\noindent Then the approximated empirical risk with $\tilde{g}(t)$, as a sum of supermodular functions $G(w_k) = \sum_{i=1}^{n} \tilde{g}(\langle  w_k, x_i \rangle)$, is thus supermodular in $w_k$. Coordinate descent can be incorporated with supermodular optimization in order to optimize each row of the weight matrix $W$ (Algorithm ~\ref{algo_disjdecomp}, supplementary material). Likewise, Greedy coordinate descent can be extended in the same way without imposing supermodularity on the objective function. Thus the empirical risk can be used as the objective function instead.
\vspace{-0.2cm}
\subsection{Deep Neural Networks} \label{Section:deepNeuralNetworks}
\vspace{-0.2cm}
Given the input of one conv layer $l$, we can represent a neural network with L hidden layers by a series of functions of the form:
\begin{equation}
\begin{aligned}
        \mathbf{y}_{l+1} &= \sigma(W_l^T\mathbf{y}_l+b_l) \text{ for } l\in 1\cdots L-1\\
        f(x) &= a_L\mathbf{y}_L +b_L
\end{aligned}
\end{equation}
where $\mathbf{y}_l $, the output from all previous layers to layer $l$, is a $k_{l}^2c_{l}$-by-1 vector that represents co-located $k_l\times k_l$ pixels in $c_l$ channels. With $n_l = k_{l}^2c_{l}$ denoting the number of connections of a response, $W_{l} \in \{\alpha, \beta\}^{n_{l+1}\times n_{l}}$ is a $n_{l+1}$-by-$n_{l}$ matrix, where $n_{l+1}$ is the number of filters and each row of $W_{l}$ represents the weights of a filter. The central idea of our approach is to approximate $f(x)$ via a two-layer neural network with layer $l$ as the first layer
\begin{equation}
\begin{aligned}
    \hat{f}(x) &= \hat{a} \sigma \left(W_{l}  \mathbf{y}_l+b_l \right) + \hat{b} \label{eq:linearization}
\end{aligned}
\end{equation}
where $\hat{a} = \frac{\partial f(x)}{\partial \mathbf{y}_{l+1}} $ is the gradient of $f(x)$ with respect to $\mathbf{y}_{l+1}$ at observation $x$. A bias term $\hat{b}=f(x)-\hat{a}\sigma \left(W_{l}  x_l+b_l\right)$ is estimated based on the difference between the true output and the approximate output. Then, we can use the approach in Section \ref{section: network_model} to find a convex surrogate for the empirical risk. Finally, a layerwise training strategy is applied to optimize the weights of the network (Algorithm~\ref{algoMultiLayerNetworks}, supplementary material). Given $n$ is the sample size, $L$ is the depth of the network, and $|W|$ is the number of weights in total, the time complexity for this algorithm is: $\mathcal{O}(nL^2+n|W|)$. The first term $nL^2$ measures the cost of linear approximation of the network using the gradient: linearization~\eqref{eq:linearization} is performed on all $(L-1)$ layers and all $n$ samples. Once we have the linearization (Algorithm~\ref{algo_disjdecomp}, supplementary material), the marginal gain of every entry of $W_i$ gives a cost of $n|W|$. Notice that here we ignore the cost of optimization for the last layer via stochastic average gradient. Likewise, GCD can also be extended in the same way, leading to a cost of $\mathcal{O}(nL |W|)$.

\vspace{-0.2cm}
\section{Experimental Study}
\vspace{-0.2cm}
\noindent \textbf{[Single-layer Network on MNIST]} In our first experiment, we used the single-layer network with logistic loss as the binary classifier to detect the first three digits $0-2$ among the first six digits $0-5$ of the MNIST dataset. We set $\beta=-\alpha=0.5$ for RSM.

\vspace{-5pt}
\begin{table}[H]\small
\caption{Comparison of the performance of RSM for low-bit width weights, and stochastic average gradient (SAG) for full precision weights. This series of experiments was re-run 20 times with different seeds. The loss and accuracy are measured on the testing set. The standard deviation of the loss and accuracy shows the randomness of RSM. RSM for high-bit weights, as expected, outperforms networks with low-bit weights and underperforms networks with full-precision weights.}
\centering
\begin{tabular}{@{}c|c|c@{}}
\hline
Bit-width      & Accuracy(\%) & Loss \\ \hline
1              &     $77.7\pm 4.36$          &   $0.602\pm 0.0161$   \\
2              &    $88.7\pm 1.29$                 &   $0.556\pm 5.92\times 10^{-3}$    \\
3              &      $89.1\pm 0.831$            &    $0.559\pm 6.69\times 10^{-3}$  \\
Full precision &     $95.2\pm 0.0109$           &    $0.361\pm 5.92\times 10^{-5}$    \\ \hline
\end{tabular}
\label{tb:singlelayer}
\end{table}
\vspace{-3pt}

\begin{figure}[h]\footnotesize
\vspace{-0.3cm}
\begin{minipage}[H]{0.33\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/binary_weight.png}
\caption*{(a) 1 bit}
\end{minipage}%
\begin{minipage}[H]{0.33\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/ternary_weight.png}
\caption*{(b) 2 bits}
\end{minipage}
\begin{minipage}[H]{0.33\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/quinary_weight.png}
\caption*{(c) 3 bits}
\end{minipage}
\\
\begin{minipage}[H]{0.32\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/novenray_weight.png}
\caption*{(d) 5 bits}
\end{minipage}
\begin{minipage}[H]{0.33\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/septenary_weight.png}
\caption*{(e) 9 bits}
\end{minipage}
\begin{minipage}[H]{0.33\linewidth}
\centering
\includegraphics[width=1in]{figs/result/SingleLayer/fp_weight.png}
\caption*{(f) Full precision}
\end{minipage}
\caption{Visualization of varying bit-width weight vectors for single layer network}
\label{fig:visulizeWeights}
\end{figure}

\begin{comment}
\begin{figure}[H]\footnotesize
\begin{minipage}[H]{0.45\linewidth}
\centering
\includegraphics[width=1.3in]{figs/result/SingleLayer/Loss.png}
\caption*{(a) Loss}
\end{minipage}%
\begin{minipage}[H]{0.45\linewidth}
\centering
\includegraphics[width=1.3in]{figs/result/SingleLayer/Accuarcy.png}
\caption*{(b) Accuracy}
\end{minipage}
\caption{Performance of RSM for weights with varying bit precision.}
\label{fig:performanceSingleLayer}
\end{figure}
\end{comment}

\noindent \textbf{[MLP on MNIST]} We apply our proposed methods on several networks, including a 2-layer fully connected network (FC2) with 100 hidden units, a 3-layer fully-connected network (FC3), a CNN6-d network~\cite{zhu2020r}, and a LeNet5~\cite{lecun1989backpropagation} with logistic loss as the binary classifiers to detect the first five digits among all the ten digits of the MNIST dataset. All these models are trained from scratch with weights that were initialized using the technique described in \cite{he2015delving}: $\beta=-\alpha=\sqrt{\frac{2}{n_l}}$. In addition, we implemented a hybrid learning strategy of these two approaches that uses GCD on the first half of the layers, and RSM on the second half.
\begin{table}\footnotesize
\caption{Comparisons of the performance on different model architectures using GCD, RSM, and a hybrid of them on MNIST dataset. }
\centering
\begin{tabular}{@{}c|c|c|c|c@{}}
\hline
                  & Model  & Acc.(\%) & Loss & Time(s)/Iter \\ \hline \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}c@{}}Greedy \\ Coordinate \\ Descent\end{tabular}} & FC2    &    94.2 & 0.165      &     83         \\ & FC3    &  96.8       & 0.135     &  20728            \\ & CNN6   &    96.4      &0.100 & 8339              \\ & LeNet5 &   96.5       &0.0987      &     172         \\ \hline \hline
\multirow{2}{*}{Hybrid}                                                                  & CNN6   &    94.4      &0.145      &4525              \\ & LeNet5 &      96.0    &0.110      &102              \\ \hline \hline
\multirow{4}{*}{\begin{tabular}[c]{@{}c@{}}Supermodular \\ \\ Minimization\end{tabular}} & FC2    &    74.9      &  0.514    &       475       \\ & FC3    &   85.1       &0.373      &7298              \\ & CNN6   &   88.6       &0.289      & 1438             \\ & LeNet5 &   82.7       &0.384      & 258             \\ \hline
\end{tabular}
\label{tb:resultsMNIST}
\end{table}
\footnotetext[1]{The Acc. in the table refers to classification accuracy on the testing set.}
\footnotetext[2]{The time in the Tables was measured on Intel Core i7-7820X CPU with NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] GPUs.}

The results in Table \ref{tb:resultsMNIST} shows that RSM fails to attain comparable accuracy with binary weights especially for small models i.e.\ FC2. But it can be more computationally efficient than GCD, especially for large models with millions of parameters i.e.\ CNN6 and FC3.  Comparing the results of hybrid training to those of GCD and RSM, we see that the lower performance of RSM optimization can be at least in part attributed to the increased non-convexity of earlier layers, meaning that the supermodular approximation less closely fits the true loss landscape.  However, for the second half of the layers, RSM performs on par with GCD, indicating a comparatively good fit with accompanying computational benefits. This hybrid approach combines the strengths of these two optimization approaches, and provides a trade-off between model performance and time consumption.\\
\noindent \textbf{[MLP on CIFAR10]} We also used the LeNet5 and CNN6 models for a binary classification task where the two classes are taken as the union of classes $0-2$ and $3-5$ from the CIFAR10 dataset, respectively. Between these two classes, we have 30,000 $32 \times 32 \times 3$ images for training and 6000 for testing. Using gradient descent, the CNN6 model with full-precision weights achieves an accuracy of $80.4\%$ with a loss $0.422$ on the testing set while the full-precision LeNet5 model attains an accuracy of $80.9\%$ with a loss $0.409$ on this task.

\begin{table}[H]\scriptsize
\caption{Testing performance using GCD, RSM with binary and ternary weights on CIFAR10 dataset.}
\begin{tabular}{l|c|c|c|c|c}
\hline
Model                   & Bit                      & Method & Acc.(\%) & Loss & Time(s)/Iter \\ \hline
\multirow{4}{*}{CNN6}   & \multirow{2}{*}{Binary}  & GCD          &   77.4   &   0.473   &   4216             \\
                        &                          & RSM       &   71.9       &   0.550   &  445             \\ \cline{2-6}
                        & \multirow{2}{*}{Ternary} & GCD  &     78.7     &  0.456    &    6315               \\
                        &                          & RSM  & 72.5      &   0.540    &     477             \\ \hline
\multirow{4}{*}{LeNet5} & \multirow{2}{*}{Binary}  & GCD          &   74.5   &   0.526   &   114                    \\
                        &                          &  RSM       &   69.4       &   0.585   &  213           \\ \cline{2-6}
                        & \multirow{2}{*}{Ternary} & GCD          &     74.9     &  0.502    &    165                \\
                        &                          &  RSM         &     69.9      &   0.577    &     386              \\ \hline
\end{tabular}
\label{tb:resultsCifar10}
\end{table}

\begin{comment}
\begin{table}[H]
\begin{tabular}{@{}c|c|c|c|c@{}}
\hline
                                 & Optimization & Acc.(\%) & Loss & Time(s)/Iter \\ \hline \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Binary \\ Weights\end{tabular}}  & GCD          &   77.4   &   0.473   &   4216          \\
                                 &   RSM       &   71.9       &   0.550   &  445            \\ \hline \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Ternary \\ Weights\end{tabular}} & GCD          &     78.7     &  0.456    &    6315          \\
                                 & RSM         &      72.5      &   0.540    &     477         \\ \hline
\end{tabular}
\caption{Testing performance of CNN6 using greedy coordinate descent (GCD), randomized supermodular minimization (RSM) with binary and ternary weights on CIFAR10 dataset.}
\label{tb:resultsCifar10CNN6}
\end{table}

\begin{table}[H]
\begin{tabular}{@{}c|c|c|c|c@{}}
\hline
                                 & Optimization & Acc.(\%) & Loss & Time(s)/Iter \\ \hline \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Binary \\ Weights\end{tabular}}  & GCD          &   74.5   &   0.526   &   114           \\
                                 &   RSM       &   69.4       &   0.585   &  213           \\ \hline \hline
\multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Ternary \\ Weights\end{tabular}} & GCD          &     74.9     &  0.502    &    165          \\
                                 & RSM         &     69.9      &   0.577    &     386         \\ \hline
\end{tabular}
\caption{Testing performance of LeNet5 using greedy coordinate descent (GCD), randomized supermodular minimization (RSM) with binary and ternary weights on CIFAR10 dataset.}
\label{tb:resultsCifar10Lenet5}
\end{table}
\end{comment}

\noindent The above results show that ternary weights slightly improve the model performance in terms of loss and accuracy compared with binary weights. And for deeper models i.e. CNN6, RSM provides a cheaper way to attain a comparable loss to GCD.
\vspace{-3pt}
\section{Discussion and Conclusions}
\vspace{-3pt}
 Our work has made the first step towards supermodular optimization on low bit-width neural networks and provided a framework to learn the network in a layerwise fashion with coordinate descent. For risk minimization, we build on non-monotone supermodular minimization to develop an algorithm with a global approximation guarantee (for each optimization step). We presented the randomized supermodular optimization and greedy coordinate descent for learning single and multi-layer neural networks, with extensive experiments on binary classification tasks. Greedy coordinate descent could reach comparable results to full precision but at the expense of high time complexity, especially for models with millions of parameters. However, supermodular optimization fails to attain comparable results in some cases. The hybrid of these two approaches has shown its excellent performance to attain a binary model from scratch in a resource- and time-efficient way. We also notice that coordinate descent is not an obvious candidate for massive parallelism, and it potentially relies on the initial state of the model parameters. Thus, if we could start from a well-initialized model, the performance of our approaches might be further improved.

 The mapping from the general non-convex optimization landscape of deep neural network training to a convex layer-wise objective has its parallels in continuous optimization strategies \cite{pmlr-v97-belilovsky19a,NIPS2006_5da713a6}.  Convexification and layerwise optimization are themselves highly interesting and relevant areas of research that benefit both discrete and continuous optimization strategies.

\nocite{ex1,ex2}
\bibliographystyle{latex12}

redpajama_set_name: RedPajamaArXiv


Today's opening talks will likely go deep and technical into Facebook's augmented reality moves.
Good morning! We're here in San Jose, where the second day of Facebook's F8 developer conference begins with an opening keynote. Expect to hear more details about everything Facebook announced yesterday.


text:
'use strict';

var webpack = require('webpack'),
    WebpackConfig = require('webpack-config');

module.exports = new WebpackConfig().extend('./conf/webpack.base.config.js').merge({
    filename: __filename,
    debug: true,
    devtool: '#source-map',
    output: {
        pathinfo: true,
        publicPath: 'http://localhost:8080/',
        filename: '[name].bundle.js',
        chunkFilename: '[name].bundle.js'
    },
    entry: {
        app: './src/main.js',
        vendor: [
            'rx',
            '@cycle/core',
            '@cycle/dom',
            '@cycle/isolate',
            'ramda'
        ]
    },
    plugins: [
        new webpack.optimize.CommonsChunkPlugin('vendor', 'vendor.js')
    ],
    devServer: {
        contentBase: './public',
        stats: {
            modules: false,
            cached: false,
            colors: true,
            chunk: false
        }
    }
});

redpajama_set_name: RedPajamaGithub


The Land of Little Rain. Boston and New York: Houghton, Mifflin and Company, 1903.
The Land of Little Rain, Mary Austin's first book, was a naturalist's look at the relationship between America's deserts and the Native Americans who inhabited them. It was a success, and Austin continued writing about the people and landscapes of western and southwestern America, calling for the preservation of natural areas and Native American culture.


Nursery Names design bespoke prints for children's (or adult's). Specialising in alphabet cards and designs and personalised illustrated name prints. The name prints can be full customisable and they will even colour match your room, cushions or furniture, the designs are unisex and fun.
Nursery Names was set up by illustrator Emily after she was inspired when creating a name print for her niece to celebrate her birth. She spotted a gap for contemporary, modern, bold and colourful name prints and designs and so decided to launch the company.
WHY WAS IT CREATED: After creating a name print for my niece 3 years ago and getting lots of subsequent requests from friends and family I saw a viable business to create custom name prints that bucked the trend for twee pastels and had a more contemporary unisex feel. I created the full alphabet and launched the site about 6 months after my first name print was made, I have fond memories of working on that first print late into the night just so i could take it with me to my hospital visit the next day to meet my niece for the first time. I think the adrenaline of becoming an aunty helped : ) I am still adding new letter options to the website as the project grows and develops, often taking on board suggestions for new letters through my customers which is a fun way to engage with them and gives me more motivation to create.
WHAT WE NEED TO KNOW ABOUT YOUR BRAND: I am not a mum yet, but hopefully one day ( when I hope i will be) I don't believe my tastes & style will change, i decided to create something that i would want to hang on my wall and hoped that design concious parents would feel the same way, creating something contemporary with some more unusual/quirky letter options was important to me. I was also keen to create something that had a universal appeal to boys and girls and something that could grow with the child (so nothing too babyish). I hope the prints can easily make the transition from a baby's nursery to a more grown up kids space.
WHAT WE SHOULD BUY FROM YOU: Our best sellers are the customised name prints especially for new borns, you can even now have a play around at previewing your chosen name through our website on the dedicated PLAY section (this section works best on a desktop). I always send proofs out to customers before printing custom names and although we have developed a palette that works in unison with all the characters we are happy to play with colours so people have something completely unique should they wish. In the past i have colour matched from a swatches of wallpaper, cushions and books with great results, this service does cost an additional £15 but has proved very popular with parents . We also sell full alphabet prints and flash cards straight off the shelf that make lovely educational gifts for toddlers and beyond.


"Innovative been bringing a steady stream of new Sierra customers online in 2014, and the trend is continuing," says Christopher Le Blanc, SVP of Global Sales. "This group shows the wide appeal of Sierra in multiple worldwide markets and in both public and academic libraries. We are excited to deliver a unique solution with Sierra, one that offers deep functionality right now as well as the freedom to evolve services in a rapidly changing technology landscape in the coming years."
Innovative announced that 15 libraries have gone live with the Sierra Library Services Platform in the most recent four-week period.
LTG Bibliography Record number: 19629. Created: 2014-08-15 11:50:53; Last Modified: 2014-08-15 11:52:08.


CALLING ALL FAMILIES! This beautifully presented, and immaculately maintained family home is pleasure for the Kittson Team to present. Centrally positioned in the highly sought after location of North Ballajura, opposite park lands, and within the catchment of the popular Illawarra Primary School - you can walk to school, local shopping amenities, and public transport- this could be the ideal new destination for you and your growing family.
- Spacious formal and informal living areas including; dining alcove, family and games rooms.
- Large 774sqm block (approx).


text:
Tag Archives: Legal issues
Free Handbook on Prevention of Sexual Harassment of Women at Workplace
This freely downloadable e-book is a ready reckoner of legislations and good practices related to prevention, prohibition and redressal of sexual harassment of women at workplace.
This entry was posted in Articles and tagged free ebook, Legal issues, prevention of sexual harassment on April 4, 2017 by novojuris.
Shutting Down A Company – Fast Track Exit Under The New Companies Act 2013
One of the quick ways to shut down a company, when non-operational over a period of time, was through a process called Fast Track Exit (FTE). In its place, under the Companies Act, 2013 has brought in a process called Removal of Names of Companies from Register (Section 248 of Companies Act, 2013), with effect from 26 December 2016.
On 26 December 2016, Ministry of Corporate Affairs (MCA) issued a Notification notifying Section 248, 249, 250, 251 and 252 of Companies Act, 2013 (Chapter XVIII). This chapter deals with Removal of Names of Companies from Register of Companies.
A company can apply for a shut down under the new process when:
A company has failed commence business within one year of incorporation,
The subscribers to MOA have not paid the subscription amount within 180 days and no declaration filed to this effect,
Not carrying any business or operation for a period of two years (earlier it was one year) and has not sought to call itself a dormant company,
When a company voluntarily wants to shutdown, it can, after clearing all its liabilities, by obtaining consent of atleast 75% of shareholders in terms of paidup capital.
Non-eligibility of companies to shut down under the new process:
Listed companies, not for profit (section 8) companies, vanishing companies, non-compliant companies under various statutory laws, where investigation or inspection or enquiry is ordered, where prosecution or application for compounding is pending, where the assets of the company still have a charge on them.
If three months prior to such application, a company had changed its name, shifted registered office, has disposed property or rights for value, made an application to National Company Law Tribunal (NCLT) for any compromise or arrangement or is under a winding up process.
These are some measures to ensure that there are no fraudulent applications for removal of name, including ensuring there is no intention to deceive creditors or defraud any person.
The new Rules, Companies (Removal of Names of Companies from the Register of Companies) Rules, 2016 at a high level:
Removal of name of company from Register by ROC on suo-moto basis.
Forms for making an application by the Company (Form-STK2) is notified, along with a fee of INR 5000, along with an Indemnity Bond from directors, Statement of Accounts certified by a chartered accountant, Affidavit from directors, Shareholders special resolution signed by every director, a statement that there are no pending litigations involving the company.
An application filed, will be cross-verified by ROC, by giving a notice to all the directors at the address on record, along with reason cited by company for shutting down and if the directors have any representation to make/seek.
ROC will further intimate and seek objections if any, from income tax, central excise, service tax authorities. There is a time line of 30 days within which such authorities have to respond and if no response is received, then it is presumed that such authorities do not have any objection.
Based on the company's business, such as NBFC, insurance, housing finance, collective investment schemes, asset management companies, then ROC requires a no-objection certificate from the applicable regulatory bodies.
Public Notice:
ROC on receipt of application, issues a public notice, by notifying on MCA's website. It appears that MCA will provide for a separate webpage for notifying public for all applications received by them in this regard.
Publish in Official Gazette
Publish in newspaper (English and vernacular) having wide circulation in the State where the registered office of the company is located
If a company has applied for shutdown, then the company will have to notify on its own website
Effect of company notified as dissolved:
Though the company has been dissolved and its name removed from the ROC's registers and intimation provided to tax authorities, it has to be noted that liabilities, if any, continues on every director, key officer, members of the company continues and may be enforced as if the company had not been dissolved.
This is the major difference between the process as described above and winding up through NCLT.
With detailed rules, process, forms and guidance, the Government has provided much clarity. The rules also provides for clear timelines for statutory bodies to respond. It would have been nice if timelines were established for disposal of application by the ROC as well. The expenses of opting this route of shutdown has marginally gone up, due to newspaper notification to be published.
This entry was posted in Articles and tagged companies act, company shut down, law, legal counsellors, Legal issues, ministry of corporate affairs, register of companies, removal of names on December 29, 2016 by novojuris.
Compliance Under the Prevention of Sexual Harassment Act
Sexual Harassment of Women at Workplace (Prevention, Prohibition and Redressal) Act, 2013 came into force from December 2013. Earlier, we had written a brief about the Act, constitution of the Internal Complaints Committee (ICC).
In this presentation, we are capturing the compliances that organizations should comply with.
Compliance Under the Sexual Harassment Act from Novojuris
Authors: Saumya Kakar, Associate and Sohini Mandal, Junior Partner.
This entry was posted in Articles and tagged compliance, indian law, Legal, Legal issues, sexual harassment act on September 26, 2016 by novojuris.
Commercial Courts, Commercial Division and Commercial Appellate Division of the High Courts Act, 2015: Another Attempt by the Government to Reduce Overbearing Litigation Numbers
In order to take forward and accelerate the agenda of the "Ease of Doing Business" and "Make in India", the Commercial Courts, Commercial Division and Commercial Appellate Division of the High Court's Act, 2015 (the "Act") has been promulgated, which provides for the constitution of Commercial Courts and the establishment of Commercial Divisions and Commercial Appellate Divisions in the High Courts to adjudicate Commercial Disputes for achieving the motive of swift and speedy enforcement of contracts, recovery of monetary claims and compensation for damages suffered to increase investment and economic activity in our country.
The key features of the Act are as follows:
Wide meaning of "Commercial Dispute": The term "Commercial Dispute" has been very broadly defined in the Act to encompass almost every kind of transaction that gives rise to a commercial relationship. The subject matter of disputes is very wide including but not limited to issues relating to maritime law, construction and infrastructure contracts, ordinary commercial transactions, intellectual property rights disputes, disputes involving exploitation of natural resources, insurance and re-insurance disputes. Such Commercial Disputes shall now be adjudicated upon by these Commercial Courts/Divisions.
Commercial Courts/Divisions at various levels:
The Act provides for the constitution of "Commercial Courts" in every district in all states and union territories where the High Court of that state or union territory does not have/exercise ordinary original civil jurisdiction and "Commercial Divisions" within High Courts exercising ordinary original civil jurisdiction.
Further, Commercial Appellate Divisions are being set up in every High Court to hear appeals against, (i) orders of Commercial Division of High Court, (ii) orders of Commercial Courts and (iii) appeals arising from domestic and international arbitration matters that are filed before the High Courts.
"Specified Value" of Commercial Disputes: The Act provides for the adjudication of Commercial Disputes of more than INR 1,00,00,000 (defined as "Specified Value" in the Act), by the Commercial Courts/Divisions. Further, it also prescribes the manner in which the Specified Value of a Commercial Dispute is to be determined.
Transfer of Commercial Disputes: All suits and/or applications relating to a Commercial Dispute of a Specified Value pending before any civil court are required to be transferred to constituted Commercial Courts/Divisions for fast and speedy disposal of cases.
Jurisdiction over arbitrations: In line with the Arbitration and Conciliation (Amendment) Act, 2015, all matters pertaining to international commercial arbitrations have been brought within the purview of the High Court, whether or not such High Court exercises original jurisdiction, except matters relating to the appointment of arbitrators in international commercial arbitrations. Applications and appeals arising out of domestic arbitrations involving purely local Indian parties, which would ordinarily lie before any principal civil court of original jurisdiction (not being a High Court), will now lie before a Commercial Court (where constituted) exercising territorial jurisdiction over such arbitration.
Amendments to the Civil Procedure Code, 1908 ("CPC"): The provisions of the CPC, to the extent of its application to any suit in respect of a Commercial Dispute have been amended by the Act to streamline the conduct of Commercial Disputes. Key amendments to the CPC are as follows:
The Act has introduced a new provision in the CPC, which prescribes that a Commercial Court/Division shall hold a 'case management hearing' to frame issues and fix timelines to ensure that the case is concluded in an expeditious and efficient manner.
The amended provisions of the CPC allow parties to apply for summary judgement where the court could arrive at a decision solely on the basis of written pleadings.
The Act has also introduced comprehensive provisions in the CPC dealing with award of actual costs and interest. The amended provisions of the CPC also provide the issues that Commercial Courts/Divisions may consider while imposing costs on parties. The earlier provisions under the CPC dealing with costs and interest, provided for imposition of only nominal costs (which continue to apply to matters other than Commercial Disputes).
Fixed Timelines: The Act has also introduced strict timelines to ensure prompt resolution of disputes including but not limited to all appeals to the Commercial Appellate Division must be filed within 60 days from the impugned judgement and the Commercial Appellate Division must endeavour to dispose of the case within a period of 6 months. But, there is nothing prescribed with regard to any penalty or punishment being imposed on the parties if they delay the disposal of the case.
Appointment of judges: The Act recognizes that competent judges having experience in dealing with Commercial Disputes are important for expeditious disposal and therefore, requires appointment of persons having such experience to be judges of the Commercial Courts/Divisions.
Measures to curb procedural delays: The Act acknowledges that piece by piece production of documents by parties at different stages tends to delay proceedings and therefore, requires filing of all documents relevant to the dispute at the time of filing of the suit itself or at the time of filing of the defense, as the case may be. Detailed procedures for discovery and inspection of documents of the opposite party and admission and denial of documents have been provided to shorten the scope of trial. The Act sets an outer limit of 120 days for filing defense beyond which the right to file the defense is forfeited and the Court would be bound to not take such a delayed submission on record.
In light of the key features stated above it is evident that the Act is a laudable piece of legislation and a step in the right direction but there are issues unaddressed/unclear in it, which are highlighted below:
The definition of "Commercial Disputes" is very vague and wide. The list is not exhaustive and hence it can give rise to a number of litigations to just determine whether a dispute is a "Commercial Dispute" or not..
It is extremely difficult to ascertain the value of an intellectual property right and this can give rise to a number of litigations. Also, breach of confidentiality disputes have not been included in the definition of "Commercial Disputes" which are really common in this era of competition. However, it may be difficult to determine the Specified Value of such disputes.
As regards disputes arising out of agreements relating to immovable property, the qualification that the property must be used exclusively in trade or commerce could raise debates as to whether the property must have been in use for trade or commerce before an agreement is entered into or whether it would also cover agreements entered into for the purpose of using immovable property for the first time for commercial purposes.
The present amendment to the CPC casts an onus on the parties by providing that all the documents in their power possession, control or custody relating to any matterin question in the proceedings, whether it is in support of or adverse to their claim, should be filed along with their pleadings, and further require the parties to make a declaration on oath at the time of filing of their pleadings that they do not have any other document with them pertaining to the facts and circumstances of the proceedings. No express exception has been made for such production either on the grounds of privilege or confidentiality etc. Just as has been the case in the US, it is likely that such broad requirement of discovery would become a tool for harassment by parties here and would become a factor in deciding whether to settle matters or face the pain of discovery.
Having the same pecuniary value limit for all High Courts does not take into account the variable factor in such dispute cases. It is only reasonable to believe that the Himachal Pradesh High Court will have fewer commercial dispute cases than the Bombay High Court, and also of lesser commercial value. If the pecuniary value of these cases for every High Court or even the district court were to be decided based on the relevant data of the entire state, pendency would have been addressed more effectively.
Keeping in mind the Act's objective to reduce pendency, one must recognize the need for appointment of more judges. These Commercial Courts/Divisions are being assigned to the existing batch of judges, hence they will be hearing these cases apart from the matters that are already assigned to them. This may prove to be counter-productive.
There is overlapping jurisdiction of the Commercial Divisions proposed for in the five High Courts exercising original jurisdiction and the Commercial Appellate Divisions within those High Courts in relation to the arbitration matters which can create issues.
The transfer of pending cases in civil courts to these Commercial Court/Divisions may lead to practical and logistical difficulties initially as the Courts will have to deal with transferring of a substantial number of cases, which in turn may lead to cases being delayed until the correct courts receive the proceedings in suits that now would fall within the definitions of "CommercialDisputes' being above the "Specified Value". This may lead to a chaotic situation as was seen with the passing of a similar dicta for Section 138 Negotiable Instrument Act, 1881 cases in Dashrath Roopsingh Rathod v. State of Maharashtra & Anr.
The Act also does not provide for a statutory right to appeal to the Supreme Court from an order of the Commercial Appellate Division. Accordingly, the Act limits the number of appeals allowed in Commercial Disputes to only one except in certain cases.
The Act does not provide for any new or technologically advanced method of conducting the court procedures. The suggestions of e-filing, video conferencing of witness, and use of latest technology will go a long way in making these courts at par with the systems being followed in some countries.
"Justice delayed is justice denied" – The Act is inarguably a big step to curb this inefficiency of Indian judicial system. The Delhi High Court was the first off the blocks designating four of its benches as Commercial Divisions. It added two more benches to the Division followed by the High Court of Bombay designating judges for the Commercial Divisions. Despite the drawbacks, setting up of Commercial Courts/Divisions will accelerate economic growth, improve the image of the Indian justice system and enhance investors' confidence in the country's dispute resolution culture.
Authors: Saumya Kakar, Associate and Sohini Mandal, Junior Partner
This entry was posted in Articles and tagged arbitrations, civil procedure code, commercial appellate division, commercial courts, commercial dispute, conciliation amendment, ease of doing business, indian law, Legal issues, litigation, transfer of commercial disputes, value of commercial dispute on September 12, 2016 by novojuris.
Bankruptcy Bill, 2015
The Government introduced the Insolvency and Bankruptcy Bill, 2015 in Parliament on Monday (21/12/15). The current bankruptcy code in India and contains archaic laws and is spread out over multiple legislations. Due to this, India was ranked as the 136th best country for ease of conducting insolvency proceedings, as per the World Bank ranking earlier in 2015. Further, it is ranked as the 130th best country for ease of doing business, as per the same rankings (out of 180 countries). This Bill seeks to streamline the law according to global standards, making it easier to wind up or liquidate a company (both voluntarily and involuntarily), attract investment, promote entrepreneurship and push India higher in World Bank rankings. It has also been certified as a Money Bill, preventing the Rajya Sabha from stalling it, and providing for its quicker passage (a more detailed explanation of Money Bills is given at the end of the article). The important features of the Bankruptcy Code are as follows:
1.) It will amend and consolidate all bankruptcy laws and rules prevalent in other legislations (Companies Act, 2013, Sick Industrial Companies Act, 1985, and the Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest Act, 2002) to become the overarching bankruptcy law.
2.) It covers individuals, companies, limited liability partnerships and partnership firms.
3.) It lays down a time limit within which the proceedings must be completed – 180 days from the date of admission of the application. This period can be extended by 90 days by the relevant authority, if it determines that "the case is of such complexity that an orderly corporate insolvency resolution process cannot be completed within one hundred and eighty days."
4.) This time limit can be cut down to 90 days through a fast-track procedure available for some key categories.
5.) It provides for insolvency professionals who will specialize in helping sick companies. It also provides for information utilities that will collate all information about debtors to prevent serial defaulters from misusing the system.
6.) It seeks to establish an Insolvency and Bankruptcy Fund.
7.) It also provides for the creation of an Insolvency and Bankruptcy Board (the Insolvency Regulator) for regulating insolvency professionals, insolvency professional agencies and information utilities.
8.) The above Regulator will be comprised of the following authorities:
An Insolvency Adjudicating Authority, which will have the jurisdiction to hear and dispose of cases by or against the debtor;
A Debt Recovery Tribunal, which will have jurisdiction over individuals and unlimited liability partnership firms; and
A National Company Law Tribunal, formed under the Companies Act, 2013, which will have jurisdiction over companies and limited liability entities (Note: the SC in Madras Bar Association v. Union of India and Anr. (WP(C) No. 1072/2013), decided on May 14, 2015, upheld the constitutional validity of the NCLT and NCL Appellate Tribunal. These Tribunals will replace the Company Law Board, the Board of Industrial and Financial Reconstruction, the Appellate Authority for Industrial and Financial Reconstruction and the Company Courts in the jurisdictional High Courts. However, the NCLT and NCLAT are yet to be constituted).
9.) If any of the above forums fail to complete a process of insolvency before them within the stipulated timeline (clause 3 & 4), then the President of the forum is required to record in writing the reasons for the same.
10.) The "corporate insolvency resolution process" may be initiated by the following:
A financial creditor, meaning a creditor for financial facility. In this case, the basis of filing is the fact of a default to any financial creditor. This drastically changes the basis of the current provisions of "sickness" under the Companies Act, which is based on default to a majority in value of the creditors;
An operational creditor, meaning a creditor other than a financial creditor. Here, if there is no dispute as to a debt, and it isn't paid within 10 days, the creditor can initiate an insolvency proves. This creates a level playing field between secured and unsecured creditors; and
The corporate debtor himself – the company.
11.) The Code provides for a mandatory moratorium, preventing individual creditors from taking action or selling assets, giving the courts and the company time to attempt rehabilitation. This moratorium lasts for all 180 days of the process. Previously, the Company Law Tribunal could only grant a moratorium of 120 days if it so chose – it was not mandatory.
12.) During the insolvency process, if the creditors and members resolve to voluntarily liquidate the company, then the moratorium does not apply.
Money Bill
Art. 110 of the Constitution of India, 1949, defines a Money Bill. As per this article, a Bill is a Money Bill if it contains only provisions dealing with all or any of six specific matters (Article 110 (1)(a) to (1)(f)), which are broadly related to:
Imposing, abolishing or regulating a tax;
Regulating government borrowings;
The Consolidated and Contingency Funds of India; and
Any matter incidental to any of the matters specified in the previous sub-clauses (Article 110(1)(g)).
A Money Bill can be introduced only in the Lok Sabha (Art. 109(1)), and only with the prior permission of the President. In case any question arises as to whether a Bill is a Money Bill or not, the decision of the Speaker of the Lok Sabha on this matter is final (Art. 110(3)). Once it is passed in the Lok Sabha, the Money Bill goes to the Rajya Sabha for its recommendation (along with a certificate from Speaker stating that it is a Money Bill). Unlike regular/non-money bills, Money Bills are not subject to the approval of both houses. The Rajya Sabha cannot reject or stall the Money Bill, but can only give its recommendations and return the amended Money Bill to the Lok Sabha (Art. 109). Further, as per Art. 109 (5) if the Rajya Sabha does not return the Money Bill to the Lok Sabha within 14 (fourteen) days, it is considered as passed by both houses of Parliament. If the Money Bill returns on time, the Lok Sabha can either incorporate some or all of the changes proposed by the Rajya Sabha, or reject all of them. Once the Money Bill has gone through the Lok Sabha a second time, it is deemed to be passed by both houses. Lastly, the President cannot send the Money Bill back to the Lok Sabha for consideration, as it was initially introduced with his/her permission.
This entry was posted in Articles and tagged bankruptcy bill, corporate law, Legal issues on December 28, 2015 by novojuris.
Right to be forgotten: Legal Validity and Practical Challenges
In 2010, a Spaniard, Mario Costeja González approached the Spanish Data Protection Agency with a complaint against Google and a local newspaper. A Google search of his name led to an auction notice of his repossessed home on the local newspaper. Costeja claimed that since the proceedings had been resolved many years ago, the search results still being available online was a breach of his privacy. The Spanish court referred the matter to the Court of Justice of European Union. Among numerous questions considered by the EU Court, the most notable was whether individuals have a right to request that their personal data be removed from accessibility via a search engine. In a landmark judgement, the EU Court held that where the information is 'inaccurate, inadequate, irrelevant or excessive,' individuals have the right to ask search engines to remove links with personal information about them. The court also ruled that even if the physical servers of the search engine provider are located outside the jurisdiction, the privacy rules would apply if they have branch office or subsidiary in the Member State.
Theoretical debate on Right to be Forgotten: US v. EU
Position under EU Law
The 1995 EU Data Protection Directive contains the basis for what has evolved into the much debated right to be forgotten. Article 12 of the Directive allows a person to seek deletion of personal data once it is no longer required.[1] Later the EC released a proposal in 2012 to unify data protection across Europe under a single law, General Data Protection Regulation. The regulation is still under consideration and is expected to be adopted soon. Under this regulation, a right to erasure is provided under Article 17 which would enable the data-subject to seek deletion of data. Under Article 17, consideration regarding data controller's legitimate business interests may be overridden by the fundamental rights of the data subject.
Position under US Law
Ever since the Costeja judgment there has been a furore over this issue. The ideological debate on the right to be forgotten can be seen a reflective of the divergent positions of EU and US on privacy. In EU, the right to be forgotten flows from the French law right to oblivion. On the other hand, in US, the approach has been to have sector specific privacy laws such as the HIPAA and Children's Online Privacy Protection Act rather than an all encompassing law. Further, the right to be forgotten as perceived in Costeja or the proposed regulation in EU would likely be seen as contravening on the First Amendment position on free speech in the US.
As far as search results go, the position is likely to be different in US for another reason. The Communications Decency Act, 47 U.S.C. § 230, provides immunity to search engines and other Internet access providers from any liability for linking to others' content. The theoretical criticism of the right to be forgotten also pits it against right to privacy in that it goes one step beyond. Right to privacy is only applicable to that which is private, whoever, right to be forgotten seeks to remove what is already in the public domain legitimately.
Legal basis for right to be forgotten:
There is a point of view that private information should be treated as a form of intellectual property and accordingly, individuals should be given the property rights to control their data which would include all 'personally identifiable data.' This could theoretically be a legal basis for the 'right to be forgotten' as deeming the data as intellectual property would make it applicable on all parties, not merely those with privity of contract. However, as mentioned above, this would involve significant speech restrictions and whether the publication is truthful or not would have no impact. This arrangement would run counter to First Amendment and defamation where truth is always a defence. This theory of right to be forgotten could also give rise to the notion that if data is treated as property, it can be traded in a 'private data market.'
Ambiguities: Legal and Technical
There are no precise definitions in the EU Regulation of where the right would apply. Personal data is broadly defined[2] as information that can be linked, either by itself or in combination with other available information, to uniquely identify a natural person. However, it is open to interpretation whether personal data includes information that can be used to identify a person with high probability but not with certainty, for instance, an account of a person's history, actions or performance. Neither is the Regulation clear on whether it includes information that identifies a person not uniquely, but as a member of a small set of individuals, such as a family. The difficulty is that the EU regulations and laws tend to be deliberately broad and general, to allow for a range of interpretations appropriate for many different situations. However, this poses significant technical issues without a precise definition of the data and circumstances to which the right to be forgotten shall apply.
Who can exercise the right to be forgotten?
This is another issue that needs greater clarity. Often a piece of information could concern more than one individual and in such a scenario there could be a conflict between their respective wishes on how such data or information is to be treated. A related question is how the right to be forgotten should be balanced against the public interest in accountability, journalism, history, and scientific inquiry. Would the same standards be applicable to a regular individual, a politician and a celebrity when it comes to deletion of embarrassing reports from the past? There are also no proposed regulations that could ensure neutrality by the data controllers.
What constitutes 'forgetting?'
The strictest way of looking at what constitutes 'forgetting' would be deletion of all copies of data from all sources to the point that recovering the data is not possible by any means. This may prove be impractical and a weaker way to enforce the right would involve allowing the data to survive in an encrypted form, or even allowing the data to remain unencrypted but not present in a public domain.
Impact so far:
As of July 2014, Google Inc. has begun to comply with the CJEU decision. A form that allows individuals to request the removal of their personal information from Google Inc.'s search results in the local domains is available for users. Notably, for all proper name searches within local domains of EU Member States (e.g., "google.es" or "google.co.uk"), Google Inc. has added the phrase, "Some results have been removed under data protection law in Europe" at the bottom of the search results page. It is interesting though that is the case only for the local domains of the EU member states and even within these state, one can do a search on 'google.com' to access the unedited search results.
The idea behind the right to be forgotten definitely has considerable merit, especially in the age where information is disseminated so freely and unlike the ephemeral information pre-digital era, most data continues to remain accessible. However, the right as contemplated by EU is still at a very half baked stage in terms of how exactly it would be enforced. Significant technical and conceptual challenges remain in (i) permitting a person to identify and locate personal data about them; (ii) controlling all information derived from the data in question from which the data in question could be derived; (iii) having fixed criteria for who has the right to request erasure; and, (iv) implementing the entire process of removal of all data and derived information when an authorized person exercises the right.
[1] Article 12 – Member States shall guarantee every data subject the right to obtain from the controller:….(b) as appropriate the rectification, erasure or blocking of data the processing of which does not comply with the provisions of this Directive, in particular because of the incomplete or inaccurate nature of the data; (c) notification to third parties to whom the data have been disclosed of any rectification, erasure or blocking carried out in compliance with (b), unless this proves impossible or involves a disproportionate effort.
[2] The new proposed EU regulations define personal data in art 4 as follows: "(1) 'data subject' means an identified natural person or a natural person who can be identified, directly or indirectly, by means reasonably likely to be used by the controller or by any other natural or legal person, in particular by reference to an identification number, location data, online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that person; (2) 'personal data' means any information relating to a data subject."Data protection directive, definitions in Article 2 are "(a) 'personal data' shall mean any information relating to an identified or identifiable natural person ('data subject'); an identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identification number or to one or more factors specific to his physical, physiological, mental, economic, cultural or social identity."
Image Source – Link
This entry was posted in Articles and tagged Legal, Legal issues, privacy, privacy laws, right to be forgotten on August 26, 2014 by novojuris.
A short note on the FDI in MSEs
A Micro & Small Enterprise, in terms of MSMED Act, 2006 can now go ahead and issue shares or convertible debentures to a person resident outside India (PROI), exceeding 24% of its paid -up capital. The only conditions which need to be fulfilled is w.r.t the sectors prohibited and the maximum limits in Schedule 1 of Foreign Exchange Management (Transfer or Issue of Security by a Person Resident outside India) Regulations, 2000. Nonetheless, it should comply with FDI Policy, as notified by Ministry of Commerce & Industry, G.O.I., from time to time.
Further, any Industrial Undertaking, which is not an MSE and having an industrial license under the provisions of Industries (Development & Regulation) Act, 1951 for manufacturing items reserved for MSE sector, can issue shares exceeding 24% of its paid up capital with prior approval of FIPB.
This entry was posted in Articles and tagged Legal, legal consultants, legal documents, Legal Framework, Legal issues, SME's on February 22, 2014 by novojuris.

redpajama_set_name: RedPajamaCommonCrawl


text:
The West Riding County Women's Football League is an open aged (16 plus) Women's Association Football League in England affiliated to the West Riding FA.

The Charter Standard League is run by a number of League Officials who are annually voted in by the member clubs spread across its 5 Divisions.

The League is at levels 7 to 10 of the Women's Football League Pyramid, and promotes to the North East Regional Women's Football League.

2019/20 Season
The season was brought to an abrupt end by the outbreak of the global pandemic COVID-19 and was declared null and void.

League leaders at the time Farsley Celtic Juniors Firsts were successful in their application for promotion to the next step of the Women's Football League Pyramid - The North East Regional Women's Football League, Harrogate Railway were relegated from Regional to the Premier Division.

Durkar Devils folded & Middleton Athletic departed the League for a more local competition.

Hemsworth Town, Sherburn White Rose & Leeds United added additional teams to the League, Leeds Union University added 2 teams when the University Leagues had opted not to run in 2020/21 & several new teams, Golcar United, Hall Green United, Hepworth United Development, Leeds Hyde Park, Old Centralians entered the League bringing the need for a Third Division to be reactivated to accommodate 42 teams.

Unfortunately Sherburn White Rose Reserves shortly after the start of the season 2020/21.

League & Cup history

References

External links
FA Full-time site

7


text:
Atomic Blonde Review
Published on July 30, 2017 by Just Alright Reviews
"… I thought I was going to see an action movie, but what I got was a spy thriller with a really dense story."
So yesterday I went to see Atomic Blonde which is a movie that I have been looking forward to for quite some time, and unfortunately it didn't live up to my expectations. Atomic Blonde is a movie that is about Lorraine Broughton, an MI6 spy who is sent to Cold War Berlin (just before the wall fell) to retrieve a classified document that could bring an end to the Cold War, but not for the good guys. The film follows Lorraine as she meets various connections, and essentially fights her way through Berlin one thug at a time; or at least that's what the trailer led me to believe the film was about.
Atomic Blonde is a movie by director David Leitch who, for those who don't know, is one of the guys behind the wildly popular film John Wick. Now John Wick is a movie that surprised me when I first watched it. I wasn't expecting much but what I got was a nonstop, heart-pounding action with wonderful choreography and even better cinematography. So when I started seeing trailers for Atomic Blonde, I very naively thought "Oh, this is just a spiritual successor to John Wick". I was wrong. See where John Wick presents a simple story (a man getting revenge for his dog) and punctuates it with obscene amounts of violence, Atomic Blonde presents one of the more convoluted stories I have ever seen and really only presents as many fight scenes as we see in the trailer. This is really the root of my dislike (for lack of a better term) for Atomic Blonde; I felt kind of cheated. Atomic Blonde presented itself as a kick-ass action film, and don't get me wrong the action scenes we do get are amazing. The choreography is as good, if not better than John Wick. The fights are realistic, the camerawork is amazing and really captures the weight of every hit. Even small things like the characters actually getting fatigued as the fight raged on is something that I respected; once again it made the film feel more real. But the problem is that these awesome fight scenes are few and far between. What we get instead is this really dense espionage thriller that has double and triple agents, characters with multiple code names making the story even harder to follow, and interviews with superiors and superiors of those superiors. I quickly got lost in this story and I never found my way back.
One thing I will give Atomic Blonde is that I absolutely loved the feel of the film. Atomic Blonde takes place in 1989, so the aesthetic we get is one that is comprised of neon lights and Synthwave. Now I am an absolute sucker for the whole 80's vibe, so I instantly fell in love with the world that Atomic Blonde had presented. And once again there were so many little details that made this world feel as real as possible. Things like the costume designs, which were not only very 80's, but also very refined when you looked at most of the population in East Berlin. You also had these really cool title cards that would essentially spray paint locations on the screen just so you could keep up with the story (not that it helped in my case); and the soundtrack, don't get me started on the soundtrack. Atomic Blonde features pretty much every song that was popular in the 80's, and to make it even better most of them are punctuated by amazing fight scenes. One thing I also have to mention is the amazing direction in Atomic Blonde. Now I didn't notice much about the films look (save for how stylized everything was) until a fight broke out in an apartment complex (you know the one). What followed is an amazing single shot scene that lasts for as long as I can remember. The use of handy-cam in this scene means that we get to not only move around the fight getting the best angle of every punch. but it also means that every punch hits that much harder. Instead of these perfectly framed static shots that allow for the stunt coordinator to have some play, Atomic Blonde makes sure that we are getting the most realistic view of this fight which means the fight has to look real to sell it.
Overall I didn't hate Atomic Blonde, but I'm pretty far from loving it. What it comes down to really is the fact that I thought I was going to see an action movie, but what I got was a spy thriller with a really dense story. Is that my fault? Is that the fault of the (in my opinion) misleading trailers? I don't know. What I do know is that the actual action parts of the movie are fucking incredible, and if the rest of the movie had focused around them instead of using them as promotional material I would have had a much better time.
I give Atomic Blonde a B
Previous Walk the Line Review
Next Baby Driver Review


Many customers are referred to us by other satisfied customers. Recommendations have allowed us to build a business family unique in terms of character, responsibility, and awareness.
So if you have friends or relatives who would be interested in our services, just use the email address below to let us know who they are. We're sure they would appreciate your recommendation and so would we.
Please email information@advantagebuildingdesign.com with your referral.


Citation: Bhargava, Yesoda , Saumya Choudhary, Anupam Shukla, and Siddhartha Arjaria. 2017. "Understanding the Relationship between Binge Drinking, Physical Activity, and Diabetes." The International Journal of Health, Wellness, and Society 7 (3): 65-75. doi:10.18848/2156-8960/CGP/v07i03/65-75.
Metabolic syndrome is becoming a commonly talked term these days. Due to poor lifestyle habits, humans are falling prey to debilitating diseases. One of the diseases in the metabolic syndrome is diabetes. The world is witnessing a global epidemic of diabetes. Research has shown that a sedentary lifestyle combined with poor food habits is the main cause behind this disease. In this paper, we intend to observe the effect of binge drinking on diabetes in human population and also observe how the risk associated with diabetes changes with the physical activity intervention. We observe that higher degree of binge drinking is a risk factor for diabetes and exercise lowers the risk of developing diabetes considerably. All data analysis is performed in STATA. We have used BRFSS (Behavioral Risk Factor Surveillance System) data of CDC (Centre for Disease Control and Prevention), USA.


Here, we will discuss on all necessary details of Initial Coin offerings and how it helps Companies to raise money in a different way.
There are few companies that are raising money through digital tokens in exchange of funds that is usually termed as Initial coin offerings. Here, we will discuss on all necessary details of Initial Coin offerings and how it helps Companies to raise money in a different way.
• Companies were making through by selling their shares in return of cash.
• Companies take money in the form of debt and pay back to the borrower with the said interest.
• Companies may liquidate its property or other assets that are helpful for raising money.
All of three assure money but they are very risky and not favorable for buyers as well. Here, comes the concept of Initial coin offerings and why they become so popular in small time span only.
ICO is a completely new technique for small or large sized Companies to raise money and much popular than any other traditional technique for raising money. Like any other technology, IOC also has certain risks associated with it that needs to be understood by investors deeply.
Money can be availed in the form of Bitcoin (BTC) or Ethereum (ETH). First of all, one Bitcoin or Ethereum address will be generated for the customers and same address can be used by the customers to send or receive money.
• Profits – Most of the investors expect that digital tokens that were purchased earlier can be sold out for certain profit. This is not clear how much profit can be raised by investors in total but they need to find out smart contacts to finalize the deal. There are reward based funding schemes for Initial coin offering investors and assure some return on every order.
• Liquidity – It is expected that tokens can be used as crypto currency that provides other market to investors for selling their tokens.
The secondary market does not give any kickstart to the concept as orders are generally tied to any individual and beneficiary account cannot be changed as required. Obviously, the restrictions don't worth for Companies as crypto currencies don't have any listing requirements. Only in rare cases, cryptocurrency offer schemes to exchange tokens. The main objective for cryptocurrency to exchange token is making money instead of focusing on project quality either it is going up or down.
Initial coin offerings (ICO) and Initial public offerings are two different terms that you should not be confused about. We cannot consider digital tokens as security or investment but they are pre-released amount having some legal sensitivity and other related issues. There may be the cases when ICO term is not used directly by investors but different terminology to avoid any legal matter or obligations.
SO, here you have to focus on project in which you are planning to invest. The project with high risks or reward profile may discard opportunities for better exchanges later. The experience by investors has proved that Bitcoins have high risk/rewards associated when compared to ETH scheme. This is the reason why ETH tokens are always more appreciated when compared to Bitcoins or any other related schemes.
Being investors you need to make smart decision, you have warned already!
Previous PostThe Ultimate Guide to Becoming an All-Round Mobility Solution Provider Next PostBitcoin Gambling – Why you should invest in Bitcoin Casino?