text:
Reynaldo López
Carlos Rodón
The Next Step: White Sox starters
Carlos Rodón, Reynaldo López, and Lucas Giolito lead a young core group that has underwhelmed thus far. What do we need to see from each in 2019?
By Darren Black Updated Mar 27, 2019, 9:05pm CDT
Share All sharing options for: The Next Step: White Sox starters
No. 1 in waiting: The former 3rd overall pick has been tabbed a future ace for years, but hasn't realized his potential yet.
Brad Rempel-USA TODAY Sports
When the Sox embarked on the teardown, trading Chris Sale, Adam Eaton and Jose Quintana, fans and experts loved the haul they received for each trade. Along with a young controllable starter they already had in Carlos Rodon, the additions of Lucas Giolito, Reynaldo Lopez, Michael Kopech, and Dylan Cease looked like a great pitching foundation for the rebuild. However, Rodon, Giolito, and Lopez have not yet realized their potential, while Kopech had Tommy John surgery in 2018 and Cease is still in the minors.
We know that all of the above pitchers need to take steps forward this year. But what exactly should fans look for — what defines a "step forward?" Well, let's find out.
Carlos Rodón: Regain a fastball or sinker
Carlos Rodón made his season debut this afternoon in Boston, striking out seven batters over 5.0 IP. pic.twitter.com/QhVhl9zhTV
— Chicago White Sox (@whitesox) June 10, 2018
The point of this series is to avoid clichés, and Carlos Rodon has an obvious one: With a healthy offseason, he should be off and running in 2019.
Of course this is somewhat true, but it does not really tell the whole story. With using Pitch Info's pitch values per 100 pitches, Rodon has had only one season where he ended the year when more than one pitch was a positive value — his rookie season in 2015. Every other season, Rodón has had only one positive pitch. That inconsistency really caught up to him in 2018, especially late in the year.
Pitch Info says that in 2018, Rodon had the lowest percentage of pitches inside the zone since his rookie season (46.9%). That mark was the 40th-lowest in baseball last year (min. 120 innings pitched). Now, a low total of pitches inside the zone does not indicate how bad a pitcher was; for instance, Patrick Corbin had the lowest percentage of pitches inside the zone in all of baseball, and he just signed a six-year, $140 million deal.
What is bad for Rodon is that his outside the zone swing rate (27%) is exceptionally low for the amount of pitches he left outside the zone. In the same leaderboard page, from 31st to 60th in terms of zone percentage, only two players had a lower mark than Rodon in outside-the-zone swing rate (one of them is Lucas Giolito). On top of that, Rodón had the lowest swinging strike rate of his career, at 9%. No wonder his 2018 K/9 and K% were the lowest of his career.
Yes, Rodón seems to have a control issue and was not able to force bad swings often, especially on pitches that were not his slider. However, the biggest area Rodón needs to fix, and what should be the easiest for him to fix, is his success against left-handed hitters. Rodón clearly did not have his best stuff last season, and lefties were able to take advantage of that. With 112 left-handed batters faced, Rodón had a 6.79 FIP and a 5.4% K/BB rate. That is terrible even if Rodon was a righty — and heat maps explain everything.
Rodon's 2018 sinker was not the same as it was in previous years: It did not move as much horizontally or vertically, and it was crushed. That led to a significant decrease in its usage, but when used against lefties, Rodon put his sinker right where batters wanted it:
Carlos Rodón's sinker against left-handed batters: On the left is ISO/p and on the right is pitch % in each zone.
Besides the oddity of Rodón throwing more sinkers up in the zone than down, he put his sinker right where left-handed batters were looking last season — so he stopped throwing them as much. However, Rodón did the same thing with his fastball:
Rodón's fastball against left-handed hitters in 2018: ISO/P is on the left, and pitch% per zone is on the right.
The overlap is not as stark as his sinker, but it's clear that Rodón put his fastball in places easy for batters to hit. Just for contrast, here is Rodón's slider in 2018, which was terrific:
Rodón's slider against left-handed batters: ISO/p is on the left, and pitch% per zone is on the right.
That slider is impossible to hit for lefties the majority of the time, unlike Rodón's fastball and sinker. Now, this all could be a sequencing issue (which would place some of the blame on catchers and the coaching staff). However, Rodón's sinker was not moving as much, and his fastball's spin rate rose almost 100 RPMs and was a touch slower compared to 2017. Those factors clearly did not help him.
So what does this mean? Well, this has all been a detailed way of saying that Rodón needs to find a good primary pitch. He has the strikeout pitch in the slider, so he just needs his fastball and sinker to be even just mildly positive, especially against lefties.
How does he do this? He needs to locate his pitches better — for instance, throwing his sinker lower in the zone. A healthy offseason should help, but health aside, if Rodón is to ascend to the role of an ace it needs to come to fruition in 2019.
Lucas Giolito: Rediscover the curve
It was another solid outing for Lucas Giolito last night vs. the Red Sox as he matched a season-high with eight strikeouts over 6.1 IP. pic.twitter.com/4mp4pcKVA2
— Chicago White Sox (@whitesox) August 31, 2018
Let's be honest here: Giolito was really bad last season, after what was a decent start to his White Sox career in 2017. It took until June 22 last year for his fastball to consistently average 93 mph-plus, and his curveball was terrible throughout the year. Because both of those pitches were so bad, Giolito had to turn to his lesser offerings. He threw a sinker, which was fine, but it's not an overwhelming pitch. He used a slider, which despite being an above-average offering he threw more than he wanted. When Giolito found his fastball near midseason, he improved, but still not as well as he should have.
Giolito and the White Sox have blamed his shortcomings last season on bad mechanics, and after his first starts this spring training, that does seem probable. In his first spring training start this year, Giolito was throwing his fastball from 93-97 mph, with a new pitching arm action. That is already much harder than what he was throwing before June 22 of last year, which is a great sign because his fastball getting back to form is a key for Giolito. However, Giolito's biggest improvement needs come from his curveball, his one pitch that was supposed to be elite.
Lucas Giolito, 93mph Two Seamer and 81mph Curveball, Overlay. pic.twitter.com/rDIl1M7mV2
— Rob Friedman (@PitchingNinja) August 31, 2018
Let's assume Giolito's fastball is back, because the velocity certainly has been in spring. If so, that in itself will help Giolito's curve. However, saying Giolito's curve was bad last season is an understatement. Among qualified starting pitchers last season, Giolito had the seventh-worst curveball according to Pitch Info's pitch value/100 pitches metric, at -2.82. According to FanGraphs, Giolito only had a 21.8% outside-the zone swing rate and a 6.3% swinging strike rate on the curve — that is not a good sign for a supposed out pitch. On top of that, batters have swung less and less often at Giolito's curveball, from his first stint in 2016 all the way down to 32.9% in 2018.
Compare Giolito's rate to Zack Grienke in 2018, who had the best curveball among starters. Grienke had a 41.7% o-swing rate, 43.9% overall swing rate, and a 15.1% swinging strike rate — numbers that are all a lot better than Giolito's.
There are reasons for that. According to Baseball Savant, Giolito's spin-rate on the curve fell 180 RPMs. That placed him at 198th for spin rate among MLB pitchers with at least 500 pitches. If Giolito's 2017 RPMs on his curveball could be magically imported into 2018, his curve would have rated 121st. While RPMs do not necessarily determine whether a curveball is good or not, such a significant drop is a bad sign. If Giolito can find his curveball, then the league will need to watch out — especially if his fastball is back at mid 90s. If not, well, it is time to abandon the curveball for good, and along with that any hope of Giolito being a top-of-the-rotation starter.
Reynaldo López: Grounder ball
Reynaldo López turned in another strong performance yesterday, striking out six over a career high 7.1 innings vs. the Pirates. pic.twitter.com/ld5DGgEE2Y
— Chicago White Sox (@whitesox) May 10, 2018
On the surface, Reynaldo Lopez had a great season in 2018, with a 3.91 ERA in 188 2⁄3 innings pitched. However, as I detailed back in October, it wasn't as it appeared.
By this point, everyone should know that Lopez finished on a tear last season; his command improved, though his strikeout numbers were still low for a player with a 70 grade fastball and a supposed 60 grade curveball. Like Giolito, Lopez lost the feel for his curveball. However, the way López differed from Giolito was that López actually was able to find another good pitch (his slider) as his curve disappeared late in the season.
In the months of August and September, when López was much improved in just about every metric, he primarily was a three-pitch pitcher. It was his fastball, changeup, and slider, with his curve sprinkled in when he had the feel for it. However — and this is where López needs to improve in 2019 — he was throwing his fastball 63% of the time. That is not necessarily a bad thing, especially when López's fastball velocity was in the 86th percentile according to Baseball Savant. But a starting pitcher cannot rely on a fastball as heavily as López does. He did lose his curve, so it would make sense that López relied on his fastball more heavily. But in 2019, his repertoire needs to be more balanced.
The biggest reason why people are not overly excited about López hangs on his strange batted ball data, as Jeff Zimmerman also acknowledged on FanGraphs for fantasy baseball purposes. Among qualified starters, López had the lowest ground ball rate, and the fifth-highest fly ball rate.
While that alone does not indicate if a pitcher is bad or good, but in the home run park that is Guaranteed Rate, one would assume López had one of the higher home run rates. However, López's home run rate was in the middle of the pack, because his HR/FB rate was ninth-lowest among qualified starters. López, to put it mildly, got a little lucky, especially since Lopez's infield fly rate was the fourth highest in baseball.
Launch angle heat maps should not as red as this:
López's launch angle, by zone
Baseball Savant
Here is where a pitching arsenal comes to play; Lopez's curve, which he should abandon, had a 53.8% fly ball rate to just a 15.4% ground ball percentage. This is downright terrible, but at least he only used that pitch 5.5% last season, according to Pitch Info. López's fastball, which was used 60.5% of the time, had a fly ball rate at 43.9% and a ground ball rate at 30.7%. Meanwhile, his change had a 42.3% ground ball and 29.8% fly ball rate. The slider, his best pitch in 2018, saw a 36.7% fly ball rate along with a 32.2% ground ball. Almost half (45.5%) of those fly balls against Lopez's slider were infield flies. However, the slider and changeup only accounted for 32.2% of pitches.
So to increase ground balls, López needs to drop the curve and reduce his fastball use by around 10%. Theoretically, he'll see a bump in his K-rate as well.
As you can see, the White Sox's core three pitchers are not perfect, and have things to work on. The point of this "next step" piece is to identify something everyone can actually watch out for with each pitcher, beyond the vague and typical, "Oh, he just has to put it together" or "just give him a healthy season."
Those are not constructive suggestions — but hopefully this article is. Let's hope all these guys get better. Stay tuned for more.
South Side Sox Top Prospect No. 79: Gunnar Troutwine After a rough 2019, this backstop roared back into prospect consideration with a superb 2021.
Lockout Cryptosoxery #6 Advice that goes totally unheeded today.


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Балвский район () — бывший административный район на востоке Латвии в области Латгалия. Граничил с Алуксненским, Гулбенским, Мадонским, Резекненским и Лудзенским районами Латвии, а также с Псковской областью России.

История
11 ноября 1959 года к Балвскому району была присоединена часть территории упразднённого Абренского района, а 17 апреля 1962 года — часть территории упразднённого Карсавского района.

Население

СМИ
В советское время в районе с 1950 года издавалась газета «Balvu Taisnība» (Балвская правда).

Ссылки
 Балвский район
 www.balvi.lv Балвский район
 Балвский район

Примечания

Районы, образованные в 1949 году
Районы, упразднённые в 2009 году
Районы Латвии

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
\label{sec:introduction}
\input{sections/introduction.tex}

\section{Disclaimers}
\label{sec:disclaimers}
\input{sections/disclaimers.tex}

\section{Troubling Trends}
\label{sec:troubling_trends}
\input{sections/troubling_trends.tex}

\section{Speculation on Causes Behind the Trends}
\label{sec:causes}
\input{sections/causes.tex}

\section{Suggestions}
\label{sec:suggestions}
\input{sections/suggestions.tex}

\section{Discussion}
\label{sec:discussion}
\input{sections/discussion.tex}

\section{Acknowledgments}
\label{sec:acknowledgments}
\input{sections/acknowledgments.tex}

\bibliographystyle{plainnat}

\subsection{Complacency in the Face of Progress}

The apparent rapid progress in ML has at times engendered an
attitude that \emph{strong results excuse weak arguments}.
Authors with strong results may feel licensed to insert arbitrary
unsupported stories (see \S \ref{sec:exp})
regarding the factors driving the results,
to omit experiments aimed at disentangling those factors  (\S \ref{sec:contrib}),
to adopt exaggerated terminology (\S \ref{sec:language}),
or to take less care to avoid
mathiness (\S \ref{sec:mathiness}).

At the same time, the single-round nature of the reviewing process may cause reviewers to feel they have no choice but
to accept papers with strong quantitative findings.
Indeed, even if the paper is rejected, there is no guarantee the flaws
will be fixed or even noticed in the next cycle,
so reviewers may conclude that accepting a flawed paper is the best option.

\subsection{Growing Pains}

Since around 2012, the ML community has expanded rapidly due to
increased popularity stemming from
the success of deep learning methods.
While we view the rapid expansion of the community
as a positive development,
it can also have side effects.

To protect junior authors,
we have preferentially referenced our own papers
and those of established researchers.
However, newer researchers may be more susceptible
to these patterns.
For instance, authors unaware of previous terminology
are more likely to mis-use or re-define language
(\S \ref{sec:language}).
On the other hand, experienced researchers fall into these patterns as well.

Rapid growth can also thin the reviewer pool,
in two ways---by increasing the ratio of submitted papers to reviewers,
and by decreasing the fraction of experienced reviewers.
Less experienced reviewers may be
more likely to demand architectural novelty,
be fooled by spurious theorems,
and let pass serious but
subtle issues like misuse of language,
thus either incentivizing or enabling
several of the trends described above.
At the same time, experienced but over-burdened reviewers may revert to a ``check-list'' mentality,
rewarding more formulaic papers
at the expense of more creative
or intellectually ambitious work
that might not fit a preconceived template.
Moreover, overworked reviewers
may not have enough time to fix---or even to notice---all of the issues in a submitted paper.

\subsection{Misaligned Incentives}

Reviewers are not alone in providing poor incentives for authors.
As ML research garners increased media attention
and ML startups become commonplace,
to some degree incentives are provided
by the press (``What will they write about?'')
and by investors (``What will they invest in?'').
The media provides incentives for some of these trends.
Anthropomorphic descriptions of ML algorithms provide fodder for popular coverage.
Take for instance \citep{metz2014brain},
which characterizes an autoencoder as a ``simulated brain''.
Hints of human-level performance
tend to be sensationalized in newspaper headlines,
e.g. \cite{markoff2014human}, which describes a deep learning image captioning system as ``mimicking human levels of understanding''.
Investors too have shown a strong appetite
for AI research, funding startups sometimes on the basis of a single paper.
In our (ZL) experience working with investors,
they are sometimes attracted to startups
whose research has received media coverage, a dynamic
which attaches financial incentives to media attention.
We note that recent interest in chatbot startups
co-occurred with anthropomorphic descriptions
of dialogue systems and reinforcement learners
both in papers and in the media,
although it may be difficult to determine
whether the lapses in scholarship
caused the interest of investors or vice versa.

\subsection{Countervailing Considerations}
Several readers of earlier drafts of this paper noted
that \emph{stochastic gradient descent
tends to converge faster than gradient descent}---in other words, perhaps a faster noisier process
that ignores our guidelines for producing ``cleaner'' papers
results in a faster pace of research.
For example, the breakthrough paper
on ImageNet  classification \citep{krizhevsky2012imagenet}
proposes multiple techniques without ablation studies,
several of which were subsequently determined
to be unnecessary.
However, at the time the results were so significant
and the experiments so computationally expensive to run
that waiting for ablations to complete
was perhaps not worth the cost to the community.

A related concern is that high standards might impede the publication of original ideas, which are more likely to be unusual and speculative.
In other fields such as economics,
high standards result in a publishing process
that can take years for a single paper,
with lengthy revision cycles consuming resources
that could be deployed towards new work.

Finally, perhaps there is value in \emph{specialization}:
the researchers generating new conceptual ideas
or building new systems
need not be the same ones who carefully collate and distill knowledge.

We recognize the validity of these considerations, but contend that while the practices advocated here may take training to master, in many cases they are straightforward to implement, requiring only a few extra days of experiments and more careful writing.
Moreover, we present these as strong heuristics rather than unbreakable rules---if an idea cannot be shared without violating these heuristics, we prefer the idea be shared and the heuristics set aside.
In short, we do not believe that the research community has achieved a Pareto optimal state on the growth-quality frontier.

\subsection{Misleading the public}

\subsection{Countervailing Considerations}
There are a number of countervailing considerations to the suggestions set forth above.
Several readers of earlier drafts of this paper noted
that \emph{stochastic gradient descent
tends to converge faster than gradient descent}---in other words, perhaps a faster noisier process
that ignores our guidelines for producing ``cleaner'' papers
results in a faster pace of research.
For example, the breakthrough paper
on ImageNet  classification \citep{krizhevsky2012imagenet}
proposes multiple techniques without ablation studies,
several of which were subsequently determined
to be unnecessary.
However, at the time the results were so significant
and the experiments so computationally expensive to run
that waiting for ablations to complete
was perhaps not worth the cost to the community.

A related concern is that high standards might impede the publication of original ideas, which are more likely to be unusual and speculative.
In other fields, such as economics,
high standards result in a publishing process
that can take years for a single paper,
with lengthy revision cycles consuming resources
that could be deployed towards new work.

Finally, perhaps there is value in \emph{specialization}:
the researchers generating new conceptual ideas
or building new systems
need not be the same ones who carefully collate and distill knowledge.

We recognize the validity of these considerations,
and also recognize that these standards are at times exacting.
However, in many cases they are straightforward to implement,
requiring only a few extra days of experiments and more careful writing.
Moreover, we present these as strong heuristics
rather than unbreakable rules---if an idea cannot be shared
without violating these heuristics,
we prefer the idea be shared and the heuristics set aside.
Additionally, we have almost always found attempts
to adhere to these standards to be well worth the effort.
In short, we do not believe that the research community
has achieved a Pareto optimal state on the growth-quality frontier.

\subsection{Historical Antecedents}
The issues discussed here are neither unique to machine learning
nor to this moment in time;
they instead reflect
issues that recur cyclically throughout academia.
As far back as 1964, the physicist John R.~Platt  discussed related concerns in his paper on strong inference \citep{platt1964strong}, where he identified adherence to specific empirical standards as responsible for the rapid progress of molecular biology and high-energy physics relative to other areas of science.

There have also been similar discussions in AI.
As noted in \S \ref{sec:introduction},
Drew McDermott \citep{mcdermott1976artificial} criticized
a (mostly pre-ML) AI community in 1976 on a number of issues,
including suggestive definitions
and a failure to separate out speculation from technical claims.
In 1988, Paul Cohen and Adele Howe \cite{cohen1988evaluation}
addressed an AI community
that at that point ``rarely publish[ed] performance evaluations''
of their proposed algorithms and instead only described the systems.
They suggested establishing sensible metrics
for quantifying progress,
and also analyzing ``why does it work?'',
``under what circumstances won't it work?''
and ``have the design decisions been justified?'',
questions that continue to resonate today.
Finally, in 2009 Armstrong and co-authors \cite{armstrong2009improvements}
discussed the empirical rigor
of information retrieval research,
noting a tendency of papers to compare
against the same weak baselines,
producing a long series of improvements
that did not accumulate to meaningful gains.

In other fields, an unchecked decline in scholarship has led to crisis.
A landmark study in 2015 suggested
that a significant portion of findings
in the psychology literature
may not be reproducible \cite{open2015estimating}.
In a few historical cases,
enthusiasm paired with undisciplined scholarship
led entire communities down blind alleys.
For example, following the discovery of X-rays,
a related discipline on N-rays emerged \citep{nye1980n}
before it was eventually debunked.

\subsection{Concluding Remarks}

The reader might rightly suggest
that these problems are self-correcting.
We agree.
However, the community self-corrects
precisely through recurring debate about
what constitutes reasonable standards for scholarship.
We hope that this paper
contributes constructively to the discussion.

\subsection{Improving Academic Standards}
Three practices that are common in
the strongest empirical papers are
\emph{error analysis},
\emph{ablation studies},
and \emph{robustness checks}
(to e.g. choice of hyper-parameters,
as well as ideally to choice of dataset).
These practices can be adopted by everyone and we advocate their wide-spread use. For some papers that follow these practices we refer the reader to \citep{zhang2017tacred,liang2016neural}.

Broadly, we encourage authors to ask ``what worked?''~and ``why?'', rather than just ``how well?''.
Except in extraordinary cases \citep{krizhevsky2012imagenet},
raw headline numbers provide limited value
for scientific progress
absent insight into what drives them.
We stress that insight does not necessarily mean theory.
Notable examples of empirical work aimed at insight
include an exploration of loss surfaces
of neural networks \citep{goodfellow2014qualitatively},
and a recent paper that demonstrated
the ability of neural networks to fit
randomly-assigned labels \citep{zhang2016understanding},
challenging the ability of established theory
to explain the ability of neural networks to generalize to unseen data. \js{Maybe add Szegedy et al. \citep{szegedy2013intriguing} paper here?}

Finally, for reviewers, we recommend a heuristic to help create better incentives---\emph{ask, ``Would I have accepted this paper
if the authors had done a worse job?''} \js{TODO: this heuristic confused some readers}
For instance, a paper describing a simple idea
that leads to improved performance,
together with two negative results,
should be judged more favorably
than a paper that combines three ideas together
(without ablation studies)
yielding the same improvement.

\subsection{Modifications to the Review Process}

\js{This section was most controversial; I think we should remove most of it, and if we keep stuff acknowledge that many people have thought about this already and not clear there are easy answers.

Some thoughts on what we could say if we wanted to:
\begin{itemize}
\item Keep some version of desk rejection but phrase less negatively. For instance: ``It may help to include an \emph{author checklist} of easily actionable guidelines that authors should ensure they follow prior to submitting a paper. These might include for instance avoiding gaps between claims made in the abstract/intro and the rest of the paper. If a paper fails to follow the checklist, reviewers have the option of recommending rejection without a full review.''
\item Talk about recent efforts to improve things and how one might go even further (incentivizing good reviews).
\item Highlight some of the issues like the downward spiral.
\item Incorporate some suggestions from feedback e-mails?
\end{itemize}}

Even before the rapid growth of ML as a field,
the conference review process was under stress; it is now approaching a breaking point.
There are simply too many papers submitted
relative to the number of experts
qualified to review them.

To address these issues, our first recommendation is to \emph{purge weak reviewers}.
It is tempting to include everyone who is willing and even plausibly able as a reviewer,
often by inviting anyone who has published
at least $X$ papers in the past few years.
However, this has the insidious effect
that compromised standards
lead to a downward spiral---once a subpar paper is accepted,
the authors enter the pool of reviewers.
As a first-pass approach, one could only invite authors to review whose average score in clarity and technical merit is above a sufficiently high threshold (demonstrating by example that they can recognize good papers),
and ask area chairs to evaluate reviewers
at the end of the reviewing cycle
(dis-inviting reviewers with
consistently low evaluations).

One obvious problem with this approach
is that it further thins an already-stretched reviewer pool.
To compensate for this,
we recommend instituting \emph{desk rejections}.
Papers with clear unacceptable defects
can be rejected with the review of only a single expert reviewer (together with the area chair).
Such problems could include:
excessive typos or obvious lack or proofreading;
gap between claims made in the abstract/intro
and the rest of the paper;
or serious flaws in the theory or experimental results.
Even if a paper is otherwise strong,
these are clear flaws
that should be fixed before final publication,
and it's fine to ask authors
to resubmit in the next cycle.

An important aspect of our proposal
is that issues leading to desk rejection
are clearly articulable
and objectively addressable;
this means that all authors should be capable
of eventually bringing a paper into a state
that is ready for full peer review.

\subsection{Setting Better Incentives for Authors}

\js{We already talk about incentives above; should merge it with this part}

We focus here on positively incentivizing work
that pushes back against the negative trends
that we identified.
We feel the current system under-values \emph{retrospective surveys} and
discuss changes to address this.
Current literature moves fast at the expense of accepting flawed works for conference publication. One remedy could be to emphasize  authoritative retrospective surveys
that strip out exaggerated claims and extraneous material, change anthropomorphic names to sober alternatives, standardize notation, etc.
We recommend creating proper tracks for these authoritative syntheses of recent research.
While these surveys would be published and presented at conferences, we advocate subjecting them to journal-type reviewing, including a revision cycle and conditional acceptance, in order to ensure consistently high quality.

\js{should probably cite Distill (although it is slightly different)}

\subsection{Suggestions for Authors}

We encourage authors to ask ``what worked?''~and ``why?'', rather than just ``how well?''.
Except in extraordinary cases \citep{krizhevsky2012imagenet},
raw headline numbers provide limited value
for scientific progress
absent insight into what drives them.
Insight does not necessarily mean theory.
Three practices that are common in
the strongest empirical papers are
\emph{error analysis},
\emph{ablation studies},
and \emph{robustness checks}
(to e.g.~choice of hyper-parameters,
as well as ideally to choice of dataset).
These practices can be adopted by everyone
and we advocate their wide-spread use.
For some examplar papers, we refer the reader
to the preceding discussion in \S \ref{sec:contrib}. \citep{langley1991experimental} also provides
a more detailed survey of empirical best practices.

Sound empirical inquiry need not be confined to tracing the sources of a particular algorithm's empirical gains; it can yield new insights
even when no new algorithm is proposed.
Notable examples of this include
a demonstration that neural networks trained by stochastic gradient descent
can fit randomly-assigned labels \citep{zhang2016understanding}.
This paper questions the ability
of learning-theoretic notions of model complexity
to explain why neural networks
can generalize to unseen data.
In another example, \citep{goodfellow2014qualitatively}
explored the loss surfaces of deep networks,
revealing that straight-line paths in parameter space
between initialized and learned parameters typically had
monotonically decreasing loss.

When writing, we recommend asking the following question:
\emph{Would I rely on this explanation for making predictions or for getting a system to work?}
This can be a good test
of whether a theorem is being included to please reviewers
or to convey actual insight.
It also helps check whether concepts and explanations
match our own internal mental model.
On mathematical writing,
we point the reader to Knuth, Larrabee, and Roberts'
excellent guidebook \citep{knuth1987mathematical}.

Finally, being clear about
which problems are open and which are solved
not only presents a clearer picture to readers,
it encourages follow-up work
and guards against researchers neglecting questions presumed (falsely) to be resolved.

\subsection{Suggestions for Publishers and Reviewers}

Reviewers can set better incentives by asking: \emph{``Might I have accepted this paper
if the authors had done a worse job?''}
For instance, a paper describing a simple idea that leads to improved performance,
together with two negative results,
should be judged more favorably
than a paper that combines three ideas together
(without ablation studies)
yielding the same improvement.

Current literature moves fast at the expense of accepting flawed works for conference publication. One remedy could be to emphasize  authoritative retrospective surveys
that strip out exaggerated claims and extraneous material, change anthropomorphic names to sober alternatives, standardize notation, etc.
While venues such as \emph{Foundations and Trends in Machine Learning}
already provide a track for such work,
we feel that there are still not enough strong papers in this genre.

Additionally, we believe (noting our conflict of interest)
that  critical writing ought to have a voice
at machine learning conferences.
Typical ML conference papers
choose an established problem
(or propose a new one),
demonstrate an algorithm and/or analysis,
and report experimental results.
While many questions can be addressed in this way,
for addressing the validity of the problems
or the methods of inquiry themselves,
neither algorithms nor experiments
are sufficient (or appropriate).
We would not be alone in embracing greater critical discourse:
in NLP, this year's COLING conference included
a call for position papers ``to challenge conventional thinking'' \citep{coling2018}.

There are many lines of further discussion
worth pursuing regarding peer review.
Are the problems we described mitigated or exacerbated by open review?
How do reviewer point systems align with the values that we advocate?
These topics warrant their own papers and have indeed been discussed at length elsewhere \citep{langford_pr, lecun_pr, zoubin_pr}.

\subsection{Explanation vs. Speculation}
\label{sec:exp}
Research into new areas often involves exploration
predicated on intuitions
that have yet to coalesce
into crisp formal representations.
We recognize the role of speculation
as a means for authors to impart intuitions
that may not yet withstand the full weight
of scientific scrutiny.
However,
papers often offer speculation in the guise of \emph{explanations},
which are then interpreted as authoritative
due to the trappings of a scientific paper
and the presumed expertise of the authors.

For instance, \citep{ioffe2015batch}
forms an intuitive theory around a concept
called \emph{internal covariate shift}.
The exposition on internal covariate shift,
starting from the abstract,
appears to state technical facts.
However, key terms are not made crisp enough
to conclusively assume a truth value.
For example, the paper states
that batch normalization offers improvements
by reducing changes in the distribution of hidden activations
over the course of training.
By which divergence measure is this change quantified?
The paper never clarifies, and some work suggests that this explanation of batch normalization may be off the mark \citep{santurkar2018does}.
Nevertheless, the speculative explanation given in \citep{ioffe2015batch}
has been repeated as fact,
e.g.~in \cite{noh2015learning}, which states,
``It is well-known that a deep neural network is very hard
to optimize due to the internal-covariate-shift problem.''

We ourselves have been equally guilty
of speculation disguised as explanation.
In \citep{steinhardt2017certified},
JS writes that ``the high dimensionality and abundance of irrelevant features\ldots give the attacker more room
to construct attacks'',
without conducting any experiments
to measure the effect of dimensionality on attackability.
And in \citep{steinhardt2015reified},
JS introduces the intuitive notion of \emph{coverage}
without defining it,
and uses it as a form of explanation,
e.g.: ``Recall that one symptom of a lack of coverage
is poor estimates of uncertainty
and the inability to generate high precision predictions.''
Looking back, we desired
to communicate insufficiently fleshed out intuitions
that were material to the work described in the paper,
and we were reticent to label a core part of our argument as speculative.

In contrast to the above examples,
\citep{srivastava2014dropout}
separates speculation from fact.
While this paper, which introduced dropout regularization,
speculates at length on connections between dropout
and sexual reproduction,
a designated ``Motivation'' section
clearly quarantines this discussion.
This practice avoids confusing readers
while allowing authors to express informal ideas.

In another positive example, \citep{bengio2012practical}
presents practical guidelines for training neural networks.
Here, the authors carefully convey uncertainty.
Instead of presenting the guidelines as authoritative,
the paper states:
``Although such recommendations come\ldots from years
of experimentation and to some extent mathematical justification,
they should be challenged.
They constitute a good starting point\ldots
but very often have not been formally validated,
leaving open many questions
that can be answered either by theoretical analysis
or by solid comparative experimental work''.

\subsection{Failure to Identify the Sources of Empirical Gains}
\label{sec:contrib}

The machine learning peer review process
places a premium on technical novelty.
Perhaps to satisfy reviewers,
many papers emphasize both complex models (addressed here)
and fancy mathematics (see \S \ref{sec:mathiness}).
While complex models are sometimes justified,
empirical advances often come about in other ways:
through clever problem formulations, scientific experiments,
optimization heuristics, data preprocessing techniques,
extensive hyper-parameter tuning,
or by applying existing methods to interesting new tasks.
Sometimes a number of proposed techniques
together achieve a significant empirical result.
In these cases, it serves the reader to elucidate
which techniques are necessary
to realize the reported gains.

Too frequently, authors propose many tweaks
absent proper ablation studies,
obscuring the source of empirical gains.
Sometimes just one of the changes
is actually responsible for the improved results.
This can give the false impression
that the authors did more work
(by proposing several improvements),
when in fact they did not do enough
(by not performing proper ablations).
Moreover, this practice misleads readers
to believe that all of the proposed changes are necessary.

Recently, \citet{melis2018state} demonstrated
that a series of published improvements,
originally attributed to complex innovations
in network architectures,
were actually due to better hyper-parameter tuning.
On equal footing, vanilla LSTMs, hardly modified since 1997 \citep{hochreiter1997long},
topped the leaderboard.
The community may have benefited more
by learning the details of the hyper-parameter tuning
without the distractions.
Similar evaluation issues have been observed
for deep reinforcement learning \cite{henderson2017deep}
and generative adversarial networks \cite{lucic2017gans}.
See \citep{sculley2018winner}
for more discussion of lapses in empirical rigor and resulting consequences.

In contrast, many papers perform good ablation analyses \citep{lake2015human,liang2016neural,zagoruyko2016multipath,zhang2017tacred},
and even retrospective attempts
to isolate the source of gains
can lead to new discoveries
\citep{chatfield2014return,santurkar2018does}.
Furthermore, ablation is neither necessary nor sufficient
for understanding a method,
and can even be impractical given computational constraints.
Understanding can also come from robustness checks
(as in \citep{cotterell2018all},
which discovers that existing language models
handle inflectional morphology poorly)
as well as qualitative error analysis \citep{kwiatkowski2013scaling}.

Empirical study aimed at understanding
can be illuminating even absent a new algorithm.
For instance, probing the behavior of neural networks led to identifying their susceptibility to adversarial perturbations
\cite{szegedy2013intriguing}.
Careful study also often reveals limitations of challenge datasets while yielding stronger baselines.
\citep{chen2016thorough} studies
a task designed for reading comprehension
of news passages and finds that
$73\%$ of the questions can be answered
by looking at a single sentence,
while only $2\%$ require looking at multiple sentences
(the remaining $25\%$ of examples were either ambiguous
or contained coreference errors).
In addition,
simpler neural networks and linear classifiers
outperformed complicated neural architectures
that had previously been evaluated on this task.
In the same spirit, \citep{zellers2018neural} analyzes and constructs a strong baseline for the Visual Genome Scene Graphs dataset.

\subsection{Mathiness}
\label{sec:mathiness}
When writing a paper early in PhD,
we (ZL) received feedback from an experienced post-doc
that the paper needed more equations.
The post-doc wasn't endorsing the system,
but rather communicating a sober view of how reviewing works.
More equations, even when difficult to decipher,
tend to convince reviewers of a paper's technical depth.

Mathematics is an essential tool for scientific communication,
imparting precision and clarity
when used correctly.
However, not all ideas and claims
are amenable to precise mathematical description,
and natural language is an equally indispensible tool for communicating,
especially about intuitive or empirical claims.

When mathematical and natural language statements are mixed without a clear accounting of their relationship,
both the prose and the theory can suffer:
problems in the theory can be concealed by vague definitions,
while weak arguments in the prose
can be bolstered by the appearance of technical depth.
We refer to this tangling of formal and informal claims as \emph{mathiness},
following economist Paul Romer who described
the pattern thusly: ``Like mathematical theory, mathiness uses a mixture of words and symbols, but instead of making tight links, it leaves ample room for slippage
between statements in natural language versus formal language'' \citep{romer2015mathiness}.

Mathiness manifests in several ways:
First, some papers abuse mathematics
to convey technical depth---to bulldoze rather than to clarify.
\emph{Spurious theorems} are common culprits,
inserted into papers to lend authoritativeness
to empirical results,
even when the theorem's conclusions
do not actually support the main claims of the paper. We (JS) are guilty of this in \citep{steinhardt2015learning},
where a discussion of ``staged strong Doeblin chains'' has limited relevance
to the proposed learning algorithm,
but might confer a sense of theoretical depth to readers.

The ubiquity of this issue is evidenced
by the paper introducing the Adam optimizer  \citep{kingma2014adam}.
In the course of introducing an optimizer
with strong empirical performance,
it also offers a theorem regarding convergence in the convex case,
which is perhaps unnecessary in an applied paper focusing on non-convex optimization.
The proof was later shown to be incorrect
in \citep{reddi2018convergence}.

A second issue is claims that are neither clearly formal nor clearly informal. For example, \citep{dauphin2014identifying} argues
that the difficulty in optimizing neural networks stems not from local minima but from saddle points.
As one piece of evidence, the work cites a statistical physics paper \citep{bray2007statistics} on Gaussian random fields
and states that in high dimensions ``all local minima [of Gaussian random fields] are likely to have
an error very close to that of the global minimum'' (a similar statement appears in the related work of \citep{choromanska2015loss}).
This appears to be a formal claim, but absent a specific theorem it is difficult to verify the claimed result
or to determine its precise content. Our understanding is that it is partially a numerical claim that the
gap is small for typical settings of the problem parameters, as opposed to a claim that the gap vanishes
in high dimensions. A formal statement would help clarify this. We note that the broader interesting point in \citep{dauphin2014identifying}
that minima tend to have lower loss than saddle points is more clearly stated and empirically tested.

Finally, some papers invoke theory in overly broad ways, or make passing references to theorems
with dubious pertinence.
For instance, the \emph{no free lunch theorem}
is commonly invoked as a justification
for using heuristic methods without guarantees,
even though the theorem does not formally preclude
guaranteed learning procedures.

While the best remedy for mathiness is to avoid it, some papers go further with exemplary exposition. A recent paper \citep{bottou2013counterfactual} on counterfactual reasoning covers a large amount of mathematical ground in a down-to-earth manner, with numerous clear connections to applied empirical problems. This tutorial, written in clear service to the reader, has helped to spur work in the burgeoning community studying counterfactual reasoning for ML.

\subsection{Misuse of Language}
\label{sec:language}
We identify three common avenues of language misuse in machine learning: \emph{suggestive definitions}, \emph{overloaded terminology}, and \emph{suitcase words}.

\subsubsection{Suggestive Definitions}
In the first avenue, a new technical term is coined
that has a suggestive colloquial meaning,
thus sneaking in connotations without the need to argue for them.
This often manifests in
anthropomorphic characterizations of tasks (\emph{reading comprehension} \citep{hermann2015teaching}
and \emph{music composition} \citep{mozer1994neural}) and techniques (\emph{curiosity} \citep{schmidhuber1991possibility}
and \emph{fear} \citep{lipton2016combating}).
A number of papers name components of proposed models
in a manner suggestive of human cognition,
e.g.~``thought vectors'' \citep{kiros2015skip}
and the ``consciousness prior'' \citep{bengio2017consciousness}.
Our goal is not to rid the academic literature of all such language;
when properly qualified, these connections might communicate
a fruitful source of inspiration.
However, when a suggestive term is assigned technical meaning, each subsequent paper has no choice but to confuse its readers,
either by embracing the term or by replacing it.

Describing empirical results with loose claims of ``human-level'' performance
can also portray a false sense of current capabilities.
Take, for example, the ``dermatologist-level classification of skin cancer''
reported in \cite{esteva2017dermatologist}.
The comparison to dermatologists conceals the fact that classifiers and dermatologists
perform fundamentally different tasks.
Real dermatologists encounter a wide variety of circumstances
and must perform their jobs despite unpredictable changes.
The machine classifier, however,
only achieves low error on i.i.d.~test data.
In contrast, claims of human-level performance in \citep{he2015delving}
are better-qualified to refer to the ImageNet classification task
(rather than object recognition more broadly).
Even in this case, one careful paper (among many less careful \citep{esteva2017dermatologist, mnih2015human, taigman2014deepface})
was insufficient to put the public discourse back on track.
Popular articles continue to characterize modern image classifiers
as ``surpassing human abilities and effectively proving
that bigger data leads to better decisions'' \citep{gershgorn2017quartz},
despite demonstrations that these networks rely on spurious correlations,
e.g.~misclassifying ``Asians dressed in red'' as ping-pong balls \citep{stock2017convnets}.

Deep learning papers are not the sole offenders; misuse
of language plagues many subfields of ML.
\citep{lipton2017fair} discusses
how the recent literature on fairness in ML
often overloads terminology borrowed from complex legal doctrine,
such as \emph{disparate impact},
to name simple equations expressing
particular notions of statistical parity.
This has resulted in a literature
where ``fairness'', ``opportunity'', and ``discrimination''
denote simple statistics of predictive models,
confusing researchers who become oblivious
to the difference,
and policymakers who become misinformed
about the ease of incorporating ethical desiderata into ML.

\subsubsection{Overloading Technical Terminology}
A second avenue of misuse
consists of taking a term
that holds precise technical meaning
and using it in an imprecise or contradictory way.
Consider the case of \emph{deconvolution},
which formally describes the process of reversing a convolution,
but is now used in the deep learning literature
to refer to transpose convolutions
(also called up-convolutions) as commonly found in auto-encoders and generative adversarial networks.
This term first took root in deep learning
in \citep{zeiler2010deconvolutional},
which does address deconvolution,
but was later over-generalized to refer to
any neural architectures using upconvolutions
\citep{zeiler2014visualizing,long2015fully}.
Such overloading of terminology can create lasting confusion.
New machine learning papers referring to deconvolution might be
(i) invoking its original meaning,
(ii) describing upconvolution, or
(iii) attempting to resolve the confusion,
as in \citep{hazirbas2017deep},
which awkwardly refers to ``upconvolution (deconvolution)''.

As another example, \emph{generative models}
are traditionally models of either
the input distribution $p(x)$
or the joint distribution $p(x,y)$.
In contrast, discriminative models
address the conditional distribution
$p(y \mid x)$
of the label given the inputs.
However, in recent works, ``generative model''
imprecisely refers to any model that produces realistic-looking structured data.
On the surface, this may seem consistent
with the $p(x)$ definition,
but it obscures several shortcomings---for instance,
the inability of GANs or VAEs to perform conditional inference
(e.g.~sampling from $p(x_2 \mid x_1)$
where $x_1$ and $x_2$ are two distinct input features).
Bending the term further,
some discriminative models
are now referred to as generative models
on account of producing structured outputs \citep{yin2015neural},
a mistake that we (ZL) make in \citep{lipton2015generative}.
Seeking to resolve the confusion
and provide historical context, \citep{mohamed2016learning} distinguishes between \emph{prescribed} and \emph{implicit} generative models.

Revisiting batch normalization,
\citep{ioffe2015batch}
describes {covariate shift}
as a change in the distribution of model inputs.
In fact, covariate shift
refers to a specific type of shift
where although the input distribution $p({x})$
might change, the labeling function $p(y|{x})$
does not \citep{gretton2009covariate}.
Moreover, due to the influence of \citep{ioffe2015batch},
Google Scholar lists batch normalization as the first reference on searches for ``covariate shift''.

Among the consequences of mis-using language
is that (as with generative models)
we might conceal lack of progress
by redefining an unsolved task to refer to something easier.
This often combines with suggestive definitions
via anthropomorphic naming. \emph{Language understanding}
and \emph{reading comprehension},
once grand challenges of AI,
now refer to making accurate predictions on specific datasets \cite{hermann2015teaching}.

\subsubsection{Suitcase Words}
Finally, we discuss the overuse of \emph{suitcase words} in ML papers.
Coined by Minsky in the 2007 book
\emph{The Emotion Machine} \cite{minsky2007emotion},
suitcase words pack together a variety of meanings.
Minsky describes mental processes such as \emph{consciousness}, \emph{thinking}, \emph{attention}, \emph{emotion},
and \emph{feeling} that may not share ``a single cause or origin''.
Many terms in ML fall into this category.
For example, \cite{lipton2016mythos} notes that \emph{interpretability}
holds no universally agreed-upon meaning,
and often references disjoint methods and desiderata.
As a consequence, even papers that appear
to be in dialogue with each other
may have different concepts in mind.

As another example, \emph{generalization}
has both a specific technical meaning
(generalizing from train to test)
and a more colloquial meaning that is
closer to the notion
of transfer (generalizing from one population to another)
or of external validity (generalizing from an experimental setting to the real world) \citep{schram2005artificiality}.
Conflating these notions leads to overestimating
the capabilities of current systems.

Suggestive definitions and overloaded terminology
can contribute to the creation of new suitcase words.
In the fairness literature, where legal, philosophical,
and statistical language are often overloaded,
terms like \emph{bias} become suitcase words
that must be subsequently unpacked \citep{danks2017algorithmic}.

In common speech and as aspirational terms,
suitcase words can serve a useful purpose.
Perhaps the suitcase word reflects an overarching concept that unites the various meanings.
For example, \emph{artificial intelligence}
might be well-suited as an aspirational name
to organize an academic department.
On the other hand, using suitcase words in technical arguments can lead to confusion.
For example, \cite{bostrom2017superintelligence}
writes an equation (Box 4)
involving the terms \emph{intelligence}
and \emph{optimization power},
implicitly assuming that these suitcase words
can be quantified with a one-dimensional scalar.

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Well - we made it home, again. This time we think we will plan on staying here for a bit before the next trip. Ben had a great time seeing most of his relatives in Texas. So now to the unpacking, and Christmas preparing.
FINALLY! I've been waiting for you to come home!! Welcome back!


2017 is upon us, and while many things are changing, some things remain the same. One is the need for National Writers Union members to complete Author Coalition of America surveys!
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Your participation has helped NWU accrue thousands in non-title specific royalty payments. This money goes toward organizing efforts such as launching webinars, reserving booths are major conferences, and planning large-scale recruitment campaigns.
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'The Traitors' Season 1 Winner Has Been Crowned – Deadline
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February 12, 2018 moll News 0
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Marco Belinelli will play for his eighth NBA team, agreeing to join the Philadelphia 76ers following his buyout from the Atlanta Hawks. The Spurs were reportedly interested in signing the veteran shooting guard, as he won a championship with them in 2014.
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Other potential buyout candidates include Vince Carter and Zach Randolph on the Kings and Tony Allen, who was recently waived by the Chicago Bulls.
Cleveland Cavaliers Debut New Roster vs Boston Celtics


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101 — La Mer
In celebration of their first UK spa location, we take an in-depth look at the mythical brand that is La Mer. At the serene space below the new Beauty Halls at Harrods, trademarked ingredients pair with exclusive application techniques in a peaceful environment. Enter looking tired, and emerge anew—here's how.
Shrouded in mystery and conspiracy theories, La Mer is perhaps the most famous skin cream in the world. Created by the late Dr Max Huber, an aerospace physicist, the recipe for the celebrated cream came to fruition after 12 years of research and more than 6,000 experiments of trial and error. The cream is formulated around a base of Giant Sea Kelp, which is sustainably harvested by hand off of Canada's Vancouver Island in the Pacific Ocean. The magic in the recipe is oft attributed to the fact real seaweed is used—most skincare ingredients come from various extracts, not the actual plant itself. This particular sea plant is exceptional, one of the fastest-growing in the world, and has unparalleled self-regenerating powers.
After harvesting, it is shipped on ice immediately to the lab, where for three to four months it undergoes fermentation. During this process, the natural strength of the ingredients is enhanced with the assistance of sound and light. Vitamins and minerals in the kelp undergo a transformative metabolic process, fine-tuning their potency and efficacy. This elaborate and quite exact process eventually leads to the creation of La Mer's signature Miracle Broth™, which defines the brand and remains at the heart of every product.
When Huber died, he hadn't left any formal notes or an actual recipe. His daughter, Marley, had observed and worked with him in his lab, and eventually—after a year and a half of working solely from memory—she was able to recreate the proper recipe for the cream. The new owners, Estée Lauder (the beauty conglomerate had tried to purchase La Mer several times during Huber's life, but he had resisted— it was only posthumously that the sale finally went through), set up a lab mirroring Huber's to continue to produce the cream precisely as intended.
Each product in the entire La Mer line contains Miracle Broth™, and each strain of Miracle Broth™ contains some of the previous batch. A thread continues throughout, connecting all new formulations to the one which came before, maintaining consistent potency. Said to cure everything from burns to diaper rash to premature ageing, the lauded cream is expensive—but most devotees say (there are many), worth it. Visibly transformative, before and after images show skin morph from dull to vibrant, healthy and radiant.
The line has expanded significantly, and still, every product from the Sheer Pressed Powder to the Reparative Body Lotion to the Eye Concentrate contains Miracle Broth™. Science and physics remain at the heart of the brand and are incorporated into every new formulation. The Mist, for example, is a lightweight spray that remains ultra-hydrating while in the bottle thanks to the inclusion of a magnet on the inner tube, giving it a continuous magnetic charge that enables it to moisturise the skin more effectively upon application.
The new spa
As part of Harrods massive renovation project, expanded beauty offerings have taken centre stage. All aspects of beauty and wellness—both inside and out—have been considered in the store's refurb. In addition to the beautifully-designed and diversified Beauty Halls, new features such as a Moroccan Hammam and a Wellness Clinic offering aesthetic injections, acupuncture, cryotherapy, and IV vitamin drips establish the department store as the capital's most comprehensive beauty destination.
The La Mer treatment room is the first of its kind in the UK. One of only a few around the globe, the tranquil space on the lower ground level of the store is exclusive to La Mer. Offering both Harrods-exclusive treatments and signature facials, each experience highlights La Mer's signature massage techniques that provide an instant—and natural—lift to the skin.
The Harrods-exclusive Jet Lag Facial is an eight-step therapy that aims to revitalise skin that may have taken a beating from long haul travel, or simply from everyday city living. A double cleanse provides a clean slate for the redemptive products to come, two of which include the Lifting Contour Serum and the Lifting and Firming Mask, which can be left on the skin or delicately removed following the treatment. 'We recommend you leave them on and let everything slowly absorb,' therapist Jess Baturi says. 'The products will continue to soften and treat your skin the whole time they are there.'
Each step of the Jet Lag Facial (and their other facials) is applied using signature, bespoke massage techniques. Created by celebrity aesthetician Joanna Czech, the lifting massage, in particular, helps to enhance the efficiency of the products by applying them in a way that further lifts and contours the face. 'You can see a visible difference from one side of the face to the other when you apply products this way,' Baturi says. 'The eyebrow on the massaged side will literally be higher than the other.'
To book a La Mer facial at Harrods, or for other recommendations, contact your luxury lifestyle manager.


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Last minute changes in Saam Daam: Shubha Khote steps out, Swaroopa Ghosh steps in
By user1 3 years ago
MUMBAI: business mortgage earlier in the morning had reported that the TV series Saam Daam Dand Bhed will witness the entry of a new character, which will be essayed by veteran actress Shubha Khote. However, due to last minute sudden changes, Shubha Khote has stepped out of the show.
Despite all the contracts in place, due to unknown reasons, the much experienced Shubha won't be entering the show. The makers have roped in another talented actress to play the Grandmother's character.
Vicky Donor fame Swaroopa Ghosh has been finalised to play the character. Ghosh will play Vijay (Bhanu Uday)'s grandmother and will add the fun element in the political thriller. The actress was seen in Amitabh Bachchan and Deepika Padukone starrer Piku.
Ghosh will start shooting from tomorrow. The reason behind the last minute changes is still unknown.
Well, this is indeed sad that our wait to see Khote on TV screen will be elongated. Nonetheless, we are elated to see talented actress like Swaroopa on screen. This will be her TV debut.
We couldn't get in touch with any of the above mentioned names.
Stay hooked to business mortgage for more interesting stories.
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We got to do lunch with the gals from Dr. Tavel today. Your office could be our next Lunch Bunch Office, too! Check out our web page for details!
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Ask the easy-going locals, and chic bohemian ex-pats which hotel in Galle has the most atmosphere, and their answer will almost certainly be the Galle Fort Hotel. A 17th century merchant's mansion, this is one of the finest boutique hotels in Galle, promising real character, exceptional cuisine, and a wonderfully sleepy feel within its courtyard pool area.
Perfect forCouples, honeymooners, and Sri Lanka-romantics who want a character-laden boutique hotel.
In the knowJust one of its many accolades, the Galle Fort Hotel was allotted a UNESCO award of distinction for its excellent heritage conservation.
Right in the heart of Galle's Dutch Colonial fort, on Sri Lanka's south coast.
There are 13 rooms and suites at Galle Fort Hotel, plus 9 villas, many of which face into the central colonnaded courtyard with its pool and blossom scattered gardens. We love the soothing minimalist feel of the rooms (exactly what you'll want after the heat of the day) whilst the canopied four poster beds, teak floors, and mahogany cabinets filled with 18th century china transport you to the days of the Dutch East India company.
Many will say that the Galle Fort Hotel has best food in Galle – if not Sri Lanka – but we'll leave it to you to decide having sampled the delicate mango salads with blue swimmer crab meat, Thai curry with yellow fin tuna, and other exceptional Asian fusion cuisine inspired by Thailand, Malaysia, and Vietnam. We also love the relaxed breakfasts here which are served on the peaceful veranda.
During the heat of the day, the coffee shop and bar in front of Galle Fort Hotel have a real buzz about them, with locals heading here to relax and sip an ice-cold beer or faluda (traditional drink made with rose petal syrup and tapioca pearls). The evening is the best time to take a tour with a resident, amongst the colonial buildings, craft markets, and colourfully painted bullock carts, before a sunset walk along the fort ramparts itself.


text:
BACHELORETTE, THE
Air Date: Monday, June 24, 2019
Episode Title: (#1506) "Episode 1506"
VIEW ALL LISTINGS FOR BACHELORETTE, THE
HANNAH'S BOLD MOVE ON ONE PIVOTAL DATE SETS THE TONE FOR HER ROMANTIC RESET WITH THE MEN, AS THEY EAGERLY ATTEMPT TO WIN HER HEART IN RIGA, LATVIA, ON ABC'S 'THE BACHELORETTE,' MONDAY, JUNE 24
"1506" - Emotionally exhausted from last week's drama but resolved to get her journey to find love back on track, Hannah looks forward to taking her nine remaining men to beautiful Riga, Latvia, on the Baltic Sea. One handsome suitor, who has been crushing hard on Hannah, is awarded the first one-on-one date. It turns out to be a heart-stopping but liberating experience as the couple bungee jump wearing little clothing; however, when the man kisses and tells about his lusty date, how will the other men react? On a group date to Riga Central Market, Hannah rewards another bachelor who presents her with flowers and passionate kisses. Two archrivals face off in a spirited wrist wrestling match that definitely grabs the Bachelorette's attention. On the final one-on-one for the week, Hannah and her date learn about a Latvian spa ritual, which mixes sauna and spiritual elements, including massage techniques. The temperature rises both literally and figuratively as the two connect with hot physical intensity, but will they be able to relate on a deeper, emotional level? Another man surprises Hannah with a late-night visit to proclaim his feelings for her. But before the rose ceremony, the Bachelorette is all business and needs to clarify what she expects in a future soul mate on "The Bachelorette," MONDAY, JUNE 24 (8:00-10:01 p.m. EDT), on ABC. Episodes can also be viewed the next day on ABC.com, the ABC app and Hulu.
The nine remaining men are the following:
Connor S., 24, an investment analyst from Dallas, TX
Dustin, 30, a real estate broker from Chicago, IL
Dylan, 24, a tech entrepreneur from San Diego, CA
Garrett, 27, a golf pro from Birmingham, AL
Jed, 25, a singer/songwriter from Nashville, TN
Luke P., 24, an import/export manager from Gainesville, GA
Mike, 31, a portfolio manager from San Antonio, TX
Peter, 27, a pilot from Westlake Village, CA
Tyler C., 26, a general contractor from Jupiter, FL
Hosted by Chris Harrison, "The Bachelorette" is a production of Next Entertainment in association with Warner Horizon Unscripted Television. Mike Fleiss, Martin Hilton, Nicole Woods and Bennett Graebner are the executive producers.
BACHELORETTE, THE (ABC)
Returns in Summer 2020 (Exact Date TBA)
16 (??? episodes)
renewed through the 2019-20 season (the show's 16th) on 8/5/19
(from ABC's press release, August 2019) ABC announced today at the network's Television Critics Association presentation that the No. 1 show of summer, "The Bachelorette," was picked up for its 16th season, and summer's guiltiest pleasure, "Bachelor in Paradise," will return for its seventh season. Last week, the dramatic 15th season of "The Bachelorette," featuring Hannah Brown, aired its shocking season finale with a surprise breakup and potential rekindled romance. The season finale telecast grew year over year by 17% among Adults 18-49 to post the show's highest-rated telecast in two years and delivered a season-high 2.2 million total social interactions. The highly anticipated sixth season of "Bachelor in Paradise" will premiere tonight, MONDAY, AUG. 5 (8:00-10:01 p.m. EDT), on ABC. (TV-PG, DL) Episodes can also be viewed the next day on ABC.com, the ABC app and Hulu. Last summer, "Bachelor in Paradise" led its Monday night time period and stood as the night's No. 1 series across all key Adult demos (A18-34/A18-49/A25-54). In fact, "Bachelor in Paradise" was Monday's No. 1 series on 4 of its 5 telecasts with Adults 18-49. The series' Tuesday-night run stood as last summer's No. 1 program of the night among Women 18-34. These "Bachelor" franchise properties join the already announced 24th season of "The Bachelor," which will also return in 2020. During its 2019 run, featuring Colton Underwood, "The Bachelor" ranked as Monday's No. 1 TV program in Adults 18-34, Women 18-34 and Women 18-49, standing as the night's top-rated series on each of its 10 telecasts of the season.
· Chris Harrison as Host
· Bennett Graebner as EP
· Martin Hilton as EP
· Mike Fleiss as EP
· Nicole Woods as EP
· reality (all)
· reality (competition)
· Next Entertainment


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Stone Poets » The gift that keeps on giving…happy holidays.
The gift that keeps on giving…happy holidays.
A wonderful gift to give someone you love this Christmas is a private concert from Stone Poets. We will give you a certificate to give to the person you are gifting us to … plus they will receive a cd package.


CHESTERFIELD TOWNSHIP — ALC Hosted Telecommunications has introduced operator assisted conference calling.
"As operating budgets continue to be cut, our clients continue looking for ways to reduce costs, and we continue to introduce services that help our clients accomplish this goal," said ALC owner Bettyanne Molitor. Operator assisted conference calls allow for a large number of participants to be on the same call. Even large calls can conduct a Q&A session seamlessly.
Other benefits include the ability to record the call, a list of call participants, and a follow-up questionnaire being sent to all participants. Autumn Land provides hosted telecommunication services, eliminating the need for expensive on-site equipment and technical support. Hosted applications help to reduce cost, provides a simpler product to use, and assists organizations in keeping up to date with current technology.
The Michigan based company provides audio, video, and Web conferencing, hosted auto dialing (Phone Tree), message on hold services (custom productions to inform and entertain clients while on hold, increasing call retention) and E911 Enable (will ensure that your organization is E911 compliant).