Today on the blog we are passing it over to Emily of Arnold Event Styling. Emily recently had the fun of being able to plan and style her own wedding, and below she beautifully recounts her round up of the special day.
We wanted our wedding to be a casual celebration, filled with all our favourite things; good food, good drinks, good company, fairy lights and lots of flowers!
And we were very lucky to be able to do just that.
We found a house in Yarra Junction, Victoria, with picturesque views of the mountainside and we knew it was perfect for us. We love a good view, and the house could sleep 12 people, allowed dogs and had a commercial kitchen.– Perfect for our idea of holding an intimate family cook-up.
Dramatic ceiling installations were set up in the space.
As I was working part-time in the styling industry, I was super excited about being able to style our own special day. The vibe I wanted to achieve was minimal and casual, with a colour palette of black, greenery and a touch of pink. Working with my florist (and old friend) Cassie from Edward and I was so much fun – she did an incredible job and went above and beyond. In the days leading up, Cassie was spray painting foliage black to create my dream look. The black added some dramatic depth, which was then mixed with a touch of softness with pampas grass and pink florals.
This is how you style vintage bins!
Invitations As part of my engagement surprise, James had drawn a picture of us, and drawn some stencil art which represents us (he's the lion, I'm the giraffe!) so I couldn't wait to use these on our wedding invitations!
The handmade invitations, finished off with a personalised touch.
Place Cards I found some copper glass prisms on Etsy, and fell in love. They were actually meant to be ring holders, but I thought they could make some pretty cool place cards! I created the hanging part out of a necklace pendant.
Adding that personal touch with these unique place cards.
'Best Day Ever' signage I found this nice pine board in an art store, and new I wanted to use it somehow. I ended up finding some wooden letters which I painted black. This was another simple element but effective, I think.
Food was all made from scratch, and we all pitched in on the day to help make our idea of a big family cook-up come true.
My sister made beautiful cupcakes (in my colour theme of course) which we decorated with roses. James' sister made us a lion and giraffe cake, absolutely gorgeous!
I also did all the sourcing of the final touches like the giant pool balloons, the dusty pink feathers and rugs, furniture, cutlery and crockery.
We finished the weekend up with a few friends over on the Sunday, which really topped it off. They were all so excited and happy for us. We even made an appearance for them in our suit and dress!
I'm also really glad that I got James' friends involved for him. In the build-up to our wedding, my sister organised a 'hens' celebration for me. I felt so special on this day, with all of my favourite girlfriends in one room. I wanted James to feel as special as I had! Since we weren't having friends there on our actual wedding day, I thought it was even more important to involve them for him. So, I asked a bunch of his pals to each put together a letter to James. What they came up with was awesome – drawings of the two of us, stories of him growing up, stories of how he deserves the best, and one of his friends put together a full book! He had a good laugh and felt the love, which is all I wanted. His friends said to me it was nice that they could be involved somehow, too. I put these in a box for him, which was titled "A little bit of man love on your wedding day".
I love my dress! My good friend Kristen, from Dressmaking and Such, made my dress, a cape, a surprise bridal robe for me, and James' pocket square out of my dress fabric. My dress was super simple, a tiny train and pockets (yes!). The cape was beautiful and floaty, I felt so special. I absolutely love everything Kristen made for us – the fabrics, gahhhh. There's no rule against wearing a wedding dress to work right?
The bridge being walked up the aisle by both mum and dad.


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text:
Ahmedabad : Gandhinagar is first of 125 railway stations that got redeveloped
The recently fabricated Gandhinagar rail route station, which will be essentially introduced by Prime Minister Narendra Modi on Friday, will be multiple times greater than its archetype and is the first of the 125 stations on the Indian Railways organization to be redeveloped.
Vivid works of art of Narendra Modi Cricket Stadium, projectile train and ocean plane embellish the passageway of the redeveloped station that is spread more than 26,000 square meters. Found only 50 meters from the old railroad station, the upgraded one stands on amazing alignmnet with Dandi Kutir (an advanced gallery on Mahatma Gandhi formed like a hill of salt) and the Mahatma Mandir Convention and Exhibition Center, structures that were worked under the direction of PM Modi.
Three pinnacles of a five-star lodging stand tall 22.5 meters from the station. "The Government of India is embraced the redevelopment of 125 rail route stations the nation over. Gandhinagar is the primary redeveloped station in the nation," said Sanjeev Kumar Lohia, MD and CEO of Indian Railway Station Development Corporation (IRSDC).
The Gandhinagar rail route station redevelopment project was taken up to commend designs like Mahatma Mandir Convention and Exhibition Center, which has been facilitating huge occasions like Vibrant Gujarat. It was his (PM's) vision to assemble an inn to praise the conference hall. This would help dignitaries and visitors remaining at the lodging to handily arrive at Mahatma Mandir. Also, the inn is only 30 minutes from the air terminal," he said during an exceptional visit through the task coordinated for media people on Wednesday.
There are a few things that are going on without precedent for the Indian Railways, Lohia said, adding, "Aside from the special lodging on top of the rail line station, the veneer lighting at the passage and the utilization of the whole substantial construction to portray the craftsmanship and culture of Gujarat is new endeavor to inject life and shading into our stations. The appearance and flights have been isolated. Metros associate the stages and a concourse exists that can be utilized for business purposes. The whole construction has been worked according to the green structure standards," he added.
At first, the recently assembled Gandhinagar rail route station will be associated by seven sets of traveler trains, including the new Gandhinagar-Varanasi superfast express and Gandhinagar-Varetha MEMU (Mainline Electric Multiple Unit) that PM Modi will practically signal off on Friday. While Varanasi is the Lok Sabha body electorate of Modi, the MEMU will interface Gujarat's funding to his origin, Vadnagar.
The new station will have three stages, which can be reached out to five. Two metros interlink these stages. "The old stages that were 525 meters in length have now been reached out to 650 meters, according to the current train length," Lohia said. A 10,000-square meter concourse situated over the railroad station can likewise go about as an overhead network for stages.
The old rail route station of Gandhinagar keeps on existing toward one side of stage number one. As per rail line authorities, this station can be utilized as a traveler leave when traffic increments.
The five-star lodging — Leela Gandhinagar — that possesses the airspace over the rail route tracks of the railroad station, has two selective bended inclines that are 750 meters in length and give section and leaves focuses towards Vavol town behind the station. The 318-room inn is protected against vibration and commotion from the railroad station.
As indicated by government sources, an expected Rs 790 crore is being spent on this task by Gandhinagar Railway and Urban Development Corporation (GARUD), a joint endeavor of Gujarat government and Ministry of Railways, acting through IRSDC. Of the sum spent, only nine percent or Rs 71.5 crore is spent on redeveloping the rail line station, while the rest has been spent on the lodging.
GARUD, where the state government holds a 74 percent value, has consented to an arrangement to rent out the inn to M/s Hotel Leela Ventures Ltd (presently Schloss HMA Pvt Ltd) for a very long time, authorities said. The Indian Express has effectively revealed that the expense of redeveloping the railroad station and building a five-star inn has raised by 208% in three years till 2020.
MMRDA: Multiple delays later, BMC hands over work on Mithi River bridge to MMRDA
Football: Jadon Sancho says 'nothing new' in racist abuse in apology to England fans

redpajama_set_name: RedPajamaCommonCrawl


text:
Hyundai hires German to improve sports cars
Albert Biermann
Can the nation's leading automaker Hyundai Motor produce a growling beast of a car with more than 500 horse power and high-quality driving performance?
The company expects to get an answer next year, after announcing on Monday that it has hired Albert Biermann, who has been the head of engineering at BMW M for the past seven years. Biermann will be the new vice president of Hyundai's high-performance vehicle development department at Namyang Research Institute, the automaker's main R&D center.
Hyundai said the 57-year-old will join the company on April 1 and will direct all aspects of the projects to develop high-performance cars, including performance, safety, durability and noise oscillation tests.
Because Biermann was in charge of BMW's M series, which oversees vehicles that compete in racing events, Hyundai expects him to improve the overall performance of its cars and to ensure that they are on the same level as premium European brands.
Last year, Hyundai and its sister company Kia Motors established a test center at Germany's Nurburgring circuit, one of the toughest racetracks in the world, which the company said has led to improved power and safety. But it is now looking to upgrade parts like the steering wheel, which requires elaborate technology, for which the company will turn to Biermann.
With the German onboard, Hyundai said it will also start working on a high-performance sports car, with the goal of adopting technology that can be used in its super cars as well as regular vehicles to improve their global competitiveness.
"Many premium European automakers have applied high-end technology they found while developing high-performance cars to mass production," a spokesman of Hyundai said. "Working with Biermann will definitely upgrade our vehicles' overall competitiveness."
BY KWON SANG-SOO [sakwon80@joongang.co.kr]


text:
\section{Introduction} \label{seq:intro}
Radar sensing is an integral part of many perception concepts for autonomously driving vehicles.
This is justified by a radar's ability to directly obtain a precise radial (Doppler) velocity from all observed objects within a single measurement.
It is currently the only automotive sensor to deliver this information in a single shot fashion and, therefore, is indispensable for adaptive cruise control systems \cite{winner2016}.
Moreover, state-of-the-art automotive radar sensors typically operate at a frequency range of $76-$\SI{81}{GHz}.
This makes them more robust to adverse weather conditions such as fog, snow, or heavy rain.
The drawback of radar is its low angular resolution when compared to other sensors.
This leads to sparse data representations, especially for remote objects.

Due to the high complexity of identifying object instances in these kinds of data, most of the research utilizes radar for gaining a basic first understanding about the scene at hand.
Hence, only few classes are separated from each other, e.g., \cite{Heuel2011, bartsch12, Sorowka2015}.
Excellent environmental perception requires understanding more complex scenes.
Thus, in \cite{Schumann2017} and \cite{Scheiner2018}, five classes of dynamic road users are recognized and separated from a sixth class comprising measurement artifacts and other undesired data points.
This article is based on the work presented in \cite{Scheiner2018}.
By applying different kinds of multiclass binarization techniques, the classification performance is improved, there.
Starting from a set of 50 features, a backward elimination routine is applied.
The resulting optimized set of 36 features leads to a major performance boost.
In accordance with conventional multiclass binarization strategies, the feature set is fixed for all individual classifiers composing the model (cf. Fig \ref{fig:abs_clf_proc_old}).
However, it has to be expected that a feature set optimized for the combined results on all classifiers of the overall model is inferior to the same ensemble in which each classifier is presented its own optimized feature set.
Finding an optimal feature set is an NP-hard problem, hence, even for one single feature set, estimating an optimal subset becomes infeasible with an increasing number of features \cite{Amaldi1998}.
In order to limit the computational effort, this article proposes a combined approach of a wrapper method (backward elimination) which is enriched by a heuristic originating from two filter methods (Joint Mutual Information and the Relief-based MultiSURF algorithm).
This allows to find a well performing feature set for each individual classifier in a reasonable time.

\begin{figure}[t!]
	\centering
	\includegraphics[width=1.\columnwidth]{./figures/abs_clf_process_old2}
	\caption{Conventional radar data classification process: data is first grouped and features calculated based on the clusters. In this case, a decomposed and combined one-vs-one (indicated by straight lines connecting classes) and one-vs-all (circles around classes) multiclass classifier is depicted.}
	\label{fig:abs_clf_proc_old}
\end{figure}
\begin{figure}[t!]
	\centering
	\includegraphics[width=1.\columnwidth]{./figures/abs_clf_process_new2}
	\caption{Proposed processing chain: each classifier in the ensemble is presented with its own specialized feature set. The results aggregation scheme accounts for previously unseen patterns.}
	\label{fig:abs_clf_proc_new}
\end{figure}

An additional issue, that is often neglected in publications about automotive radar classification, is the handling of previously unseen classes.
This problem is critical for the path planning of the vehicle, i.e., estimating where the car can drive within adequate safety margins from the expected positions of other traffic participants and surroundings.
Therefore, it might be of minor concern to discriminate between, e.g., buses and  trucks.
However, the dynamics of a train is less similar to those examples, and a skateboarder moves differently than a regular pedestrian, although they look similar when observed by a radar sensor.
Classifiers are often forced to make a decision about the class membership of an object.
In the worst case scenario they might even decide that a novel data pattern is most likely a measurement artifact and can, therefore, be ignored.
These few examples illustrate the importance of giving the path planning stages in a vehicle accurate information about other road users.
Indeed, this issue is also present in other areas such as data acquisition:
As the number of available vehicles with advanced sensor setups increases, the data collection process for automotive data sets becomes easier.
However, methods for selecting interesting data require measures for the criticality or -- in this case -- novelty of the data.
To this end, this article describes how the result aggregation scheme for the binarized classifiers can be altered to yield and additional \emph{hidden} class.
Final results show clear benefits of the proposed methods.
The overall classification performance can be further increased, despite the more challenging data set.
The utilized long short-term memory (LSTM) cell classifier provides a lightweight network structure which has several advantages:
First of all, the small amount of parameters result in low computational efforts during model training and inference.
The short training time is due to the small amount of model parameters which have to be adjusted.
This leads to the second advantage which is the lower amount of training data required for the learning algorithm to converge without overfitting.
Third and last, given good clustering results in the preprocessing stages, the accuracy of the LSTM approach is exceedingly high.
A recent trend in machine learning is the incorporation of preprocessing and classification steps in a single convolutional neural network (CNN).
This has also been done for automotive radar classification, e.g., \cite{Schumann2018} or \cite{Gaisser2017}.
Due to the aforementioned reasons, the LSTM approach is preferred, here.

The article is organized as follows: In Section \ref{sec:data} the data set and essential preprocessing steps are described.
The first part of Section \ref{sec:methods} explains how suitable feature sets are selected.
The second part deals with the proposed aggregation scheme for novelty detection with binary classifiers.
Section \ref{sec:results} presents the results and Section \ref{sec:conclusion} concludes the topic and gives prospects for future work.

\section{Data Set And Preprocessing}\label{sec:data}
The data used in this article originates from a set of real-world automotive radar data as previously used in \cite{Schumann2018}.
It contains more than 3 million data points on roughly 3800 instances of moving road users.
The class membership of road users is distributed as depicted in Tab. \ref{tab:data}.
While most classes are self-explanatory, the label \emph{pedestrian group} is attributed to multiple pedestrians which cannot be clearly separated in the data.
Moreover, the \emph{garbage} class consists of wrongly detected and clustered measurement artifacts.
The hidden \emph{other} class is made up from several road users which do not strictly fit into any of the aforementioned groups.
\begin{table}[tb]
\renewcommand{\arraystretch}{1.3}
	\caption{Object Instance (Upper Row) And Sample (Lower Row) Distribution In Data Set.}
	\label{tab:data}
	\centering
	\begin{tabular}{ccccccc}
		\hline
 		Pedestrian & Group & Bike & Car & Truck & Garbage & Other \\
		\hline\hline
		1215 & 1063 & 90 & 1310 & 154 & 38176 & 22 \\
		\hline
		30623 & 46053 & 5541 & 33920 & 6648 & 69597 & 790 \\
		\hline
	\end{tabular}
\end{table}

All data was acquired with four radar sensors distributed over the front half of a test vehicle.
Due to data sparsity, overlapping regions in the sensors' field of view do not get any special treatment in the accumulation process.
The sensors operate at \SI{77}{\giga\hertz} carrier frequency.
Their beams span from \SI{-45}{\degree} to \SI{+45}{\degree} and up to a range of \SI{100}{\meter} away from the sensor.
The radars deliver points on a detection level which are already resolved in range, angle, and radial Doppler velocity.
They are prefiltered by an internal constant false alarm rate filter and are clustered in space, time, and Doppler using a customized DBSCAN \cite{Ester1996} algorithm.
In an annotation process, each cluster is assigned a class label. Cluster refinement ensures that all detections associated with the object are being considered.
Uncorrected and intentionally deteriorated versions of the clustered data are used for data augmentation purposes during training.
For feature extraction, all labeled cluster sequences are first sampled in time using a window of $\SI{150}{ms}$.
Then, features are extracted from each of the cluster samples.
Up to eight feature vectors are being concatenated in a sliding window fashion in order to form input sequences to the neural network used for classification.
The preprocessing stage is summarized in Fig. \ref{fig:clf_chain}.
More details on all involved steps are given in \cite{Scheiner2018}.
\begin{figure}[t!]
	\centering
	\includegraphics[width=1.\columnwidth]{./figures/preproc2}
	\caption{Data preprocessing overview: data is first transformed to common coordinate system, then it is clustered and labeled before extracted features are presented to the classification model.}
	\label{fig:clf_chain}
\end{figure}

\section{Methods}\label{sec:methods}
The underlying classifier units used for this article are long short-term memory (LSTM) cells.
LSTMs are a special kind of recurrent neural network which introduce gating functions in order to avoid the vanishing gradient problem during training \cite{hochreiter97}.
They perform well on time series of all kinds and provide a straightforward way to utilize the time information in the data.
A fixed configuration of 80 LSTM cells followed by a softmax layer is used for all classifiers of the ensemble which will be described in the following subsection.
Experiments with larger LSTM layers of 120 and 200 cells did not indicate any relevant improvements.

\subsection{Ensemble Creation With Classifier Specific Feature Sets}
As previously shown in \cite{Scheiner2018}, multiclass binarization is a well-equipped technique for improving the classification performance on moving road users.
Best results were achieved by using a combined one-vs-one (OVO) and one-vs-all (OVA) approach.
Class membership is estimated by summing all pairwise class posteriors probabilities $p_{ij}$ from corresponding OVO classifiers.
Thereby, each OVO classifier is weighted by the sum of corresponding OVA classifier outputs $p_{i}$.
Subscripts $i$ and $j$ denote the corresponding class ids for which the classifier was trained.
During testing, this limits the influence of OVO classifiers which were not trained on the same class as the regarded sample, i.e., the OVA classifiers act as \emph{correction classifiers} \cite{Moreira1998}.
The final class decision for a feature vector $\bm{x}$ can then be calculated as:
\begin{align}
	\texttt{id}(\bm{x}) = \argmax_{i \in \{1,...,K\}} \text{\hspace{1mm}} \sum_{j=1, j\neq i}^K p_{ij}(\bm{x}) \cdot (p_{i}(\bm{x}) + p_{j}(\bm{x}))\texttt{.}
	\label{eq:opc}
\end{align}
$K$ is the amount of classes in the training set.
This combined approach of OVO and OVA yields a total of $K(K+1)/2$ classifiers.
Traditionally each classifier in the decomposed model uses the same feature set as input.
It is, however, most likely that not every feature is equally important for each classifier.
Moreover, early experiments with new features revealed that feature selection algorithms did not perform very well on larger feature sets.
The methods were either reluctant to reduce the complexity and drop features at all, or did not result in beneficial configurations in reasonable time.
Thus, this article proposes a method for estimating individual well performing feature sets for each binary classifier.

Before the selection algorithm is described, an overview over the features under consideration is necessary.
In \cite{Scheiner2018} a set of 50 features was described.
Each feature is calculated from all detections in a cluster during a certain time frame.
The features can be roughly divided into three groups:
statistical derivations of the four base units (range, angle, amplitude, and Doppler), geometric features describing the spatial distribution of detections in a cluster sample, and features concerning the micro-Doppler characteristics, i.e., the distribution of Doppler values on the observed object.
For this article, the set of possible feature candidates is further increased.
A variety of features was examined in \cite{Wagner2018}, however, due to redundancies only a couple of features can be transferred to this work.
For instance, the mean distance between all pairs of detections is adopted as a new measure of compactness.
Furthermore, the previous DBSCAN clustering stage can also deliver an amount of core samples besides grouping points.
The ratio of core points to the total number of detections serves as additional feature.
Inspired by the findings in \cite{volca2017}, the resemblance of the detection distribution to an ellipse shape is estimated.
Therefore, the ratio of points close to the estimated outlines of the object to the total amount of detections is calculated.
Moreover, the eigenvalues of the covariance matrix of all the x/y coordinates, Doppler velocities, and amplitudes are used, along with some additional nonlinear transformations of important base values.
A complete list of all 98 considered features can be found in Tab. \ref{tab:feats} in the appendix.

In order to determine optimized feature sets for all classifiers in the ensemble, feature selection techniques have to be applied.
The two most popular variants of feature selection techniques are \emph{filter} and \emph{wrapper} methods.
Filter methods rank features by calculating statistical relevance measures, e.g., the correlation between individual features and class labels.
This can be done independently from any classification algorithm and is computationally efficient.
On the downside, many filter techniques do not consider interdependency between features and are, therefore, suboptimal.
In comparison, wrapper methods use an actual classifier to test different subsets of features.
Most important variants of wrapper methods include \emph{backward elimination} and \emph{forward selection}.
Both methods greedily eliminate or add the one best fitting feature at a time without going back until some stop criterion is reached.
While those methods can usually find better subsets than filter methods, their computational costs are very high.
A more detailed overview over feature selection methods can be found, e.g., in \cite{BENNASAR20158520}.

The high amount of features and classifiers does not allow to repeatedly perform a full backward elimination sweep.
Hence, a combined approach of filter and wrapper methods is used in order to get a suitable subset in reasonable time.
Essentially, instead of testing all features in a backward elimination run for the least well performing one, only one feature is tested at a time.
If test results reveal a benefit of dropping the regarded feature, it is removed from the feature set.
Otherwise, the next feature is evaluated.
In order to determine the order in which to examine features, a feature ranking originating from the combination of two filter methods is used as a heuristic.

The \emph{Joint Mutual Information} (JMI) criterion is the first utilized filter.
JMI is a method that iteratively adds the feature maximizing the combined amount of information that reduces the uncertainty of the underlying class \cite{yang1999}.
It is a very popular variant due to its low complexity despite high stability.
As a second heuristic the \emph{MultiSURF} algorithm is used to create a ranking.
MultiSURF originates from the family of \emph{Relief-based algorithms}.
In contrast to the JMI criterion, Relief-based algorithms aim to build feature weights -- which can be interpreted as ranking -- based on proximity measures between different samples in the data set \cite{URBANOWICZ2018189}.
In doing so, Relief-based algorithms better account for interdependence between multiple features.

In order to profit from both feature rating approaches, the individual rankings of both methods are averaged.
The shared features in both rankings' top 50 are directly added to the final feature set to further decrease the computational costs.
The remainder of the combined ordering is used as heuristic for a directed backward elimination process as depicted in Fig. \ref{fig:feat_sel}.
This process is repeated for every classifier in the ensemble.

\begin{figure}[htb]
	\centering
	\includegraphics[width=0.9\columnwidth]{./figures/featSel2_color_crop.pdf}
	\caption{Simplified workflow of feature selection algorithm. A backward elimination scheme is enriched by a heuristic to successively eliminate bad-performing features. If no more features can be eliminated without decreasing the classification performance, the so-found subset is added to fixed set determined by previous feature ranking.}
	\label{fig:feat_sel}
\end{figure}

\subsection{Hidden Class Detection}
Another aim of this article is giving the classification ensemble the ability to detect hidden classes that have not been previously presented to the classifier.
To this end, three different approaches are being examined:
\paragraph{OVA thresholding} The OVA classifiers of the ensemble are already being used for weighting corresponding OVO classifiers based on their estimated relevancy.
This way, a \emph{relevancy threshold} can be defined which determines if any of the OVO classifiers is likely to be able to make a competent decision about the class membership of the sample.
Otherwise, the hidden class label is chosen if:
\begin{align}
	p_{i}(\bm{x}) < \text{thr} \text{\hspace{5mm}} \forall i \in \{1,...,K\}\texttt{.}
	\label{eq:ovathr}
\end{align}
\paragraph{Voting} In order to incorporate the whole ensemble in the decision process, a voting scheme was proposed in \cite{Chang2011}.
Hidden classes are assumed if no class label gets a minimum number of votes, i.e.:
\begin{align}
	\mathds{1}[p_{i}(\bm{x})>0.5] + \sum_{j\neq i} \mathds{1}[p_{ij}(\bm{x})>0.5] < \text{thr} \text{\hspace{5mm}} \forall i \texttt{.}
	\label{eq:ovathr}
\end{align}
$\mathds{1}[\mathord{\cdot}]$ denotes the indicator function.
\paragraph{OVO+OVA thresholding} Instead of finding the maximum value for the combined OVO and OVA approach in Eq. \ref{eq:opc}, the K class scores can be normalized to form pseudo probabilities.
These probabilities are then compared to a threshold value similar to the other two methods:
\begin{align}
	\nonumber &c^{-1} \cdot \sum_{j\neq i} p_{ij}(\bm{x}) \cdot (p_{i}(\bm{x}) + p_{j}(\bm{x})) < \text{thr} \text{\hspace{5mm}} \forall i \\
	&\text{\hspace{1mm} with \hspace{5mm}} c := \sum_{i} \sum_{j\neq i} p_{ij}(\bm{x}) \cdot (p_{i}(\bm{x}) + p_{j}(\bm{x}))\texttt{.}
	\label{eq:opcthr}
\end{align}

If parameterized correctly, all three methods are able to identify hidden classes with a reasonable performance.
The used data set only contains very few examples of other road users.
It includes motorcyclists, scooters, wheelchair users, cable cars, and dogs.
The main challenge is, however, to determine a parameter setting which has low impact on the correct classification of the existing classes.
In the next section, all three techniques will be evaluated based on their detection performance of the hidden class and their impact on the regular six classes.

\section{Results}\label{sec:results}
The data set used in this work contains strong imbalances among individual class occurrences.
In order to preserve the influence of each individual class, all classification scores are reported as \emph{macro-averaged F1 scores} where not mentioned otherwise.
F1 is defined as the harmonic mean of precision (true positive / predicted positive) and recall (true positive / condition positive).
Macro-averaging obtains F1 scores for each individual class, then uses the mean value of all $K$ F1 scores ($F1_\text{macro}=\frac{1}{K}\sum_{i=1}^K F1_i$) as a final measure.

Suitable feature sets are determined from the averaged scores based on 5-fold cross-validation.
During the guided backward elimination process each feature is tested with five different combinations of train and test data.
The averaged results determine, whether a feature is kept or removed from the set.
The so estimated classifier-specific feature sets are maintained for further experiments.

As it has to be expected that classifying a hidden class besides the original class set is more complicated, the results for the six class problem with and without the hidden class are discussed separately.
Moreover, an interpretation of the chosen feature sets is given at the end of the section.

\subsection*{Classification Performance On Six Class Problem}
For the evaluation of the proposed feature selection strategy on the original six class classification problem, it is necessary to define some baseline experiments for comparative reasons.
Three possible candidates utilize a shared feature set in addition to the combined decomposed OVO/OVA ensemble.
The common feature sets are, first, the full set of 98 features.
Second, a feature reduction utilizing the proposed guided backward elimination routine on the whole ensemble, and third, the optimized feature set determined in \cite{Scheiner2018}.
Moreover, a multiclass classification approach with a single classifier is evaluated on the full feature set.
Due to the largely increased feature set, running a full backward elimination without feature ranking is not possible in reasonable time.
Stable results are ensured by a 10-fold cross-validation routine.
The presented scores are the averaged results as depicted in Tab. \ref{tab:scores}.

With a \textbf{final score of \SI{91.46}{\%}}, the proposed method demonstrates its effectiveness over other methods.
Considering the already high base score from \cite{Scheiner2018}, the improvement of \SI{0.33}{\%} is a remarkable increase.
Interestingly, the larger feature set did not lead to a performance improvement.
Even in its reduced version after performing feature selection, the score could be only improved slightly, when the same feature set was shared between all classifiers.
This leads to the conclusion, that the complexity of optimizing the large feature set on the whole ensemble is simply too high for the utilized feature selection algorithm, hence, backing the classifier-specific selection approach.
As expected, the multiclass classification approach with a single classifier has the worst performance and will not be discussed any further.
In order to get a better understanding in the underlying classification subprocesses, the confusion matrix in Fig. \ref{fig:conf_6class} displays the distributions of predicted and true class memberships.
Most classification errors occur between the very similar \emph{pedestrian} and \emph{pedestrian group} classes.
Also, several cars are falsely classified as trucks.
Despite the problems with some similar classes, these issues are well compensated by the overall performance.

\begin{table}[htb]
\renewcommand{\arraystretch}{1.4}
	\caption{Classification Results On Six Class Problem.}
	\label{tab:scores}
	\centering
	\begin{tabular}{ll}
		\hline
 		Method & $\text{F1}_{\text{macro-averaged}}$ \\
 		\hline
 		\textbf{Proposed ensemble method} & \textbf{\SI{91.46}{\%}} \\
		Shared full feature set & \SI{91.08}{\%} \\
		Optimized shared features & \SI{91.17}{\%} \\
 		Shared feature set from \cite{Scheiner2018} & \SI{91.13}{\%}  \\
 		Full feature multiclass & \SI{90.64}{\%} \\
 		\hline
	\end{tabular}
\end{table}
\begin{figure}[htb]
	\centering
	\includegraphics[width=0.9\columnwidth]{./figures/cm6_cr}
	\caption{Normalized confusion matrix. All values as percentages.}
	\label{fig:conf_6class}
\end{figure}

\subsection*{Classification Results With Hidden Class}
Before the classification scores of the model on the six class problem including a hidden class can be calculated, the detection thresholds for the hidden class need to be set first.
The parameterization is done empirically, i.e., experiments are evaluated several times with different threshold settings.
Therefore, the data samples forming the hidden class are split in two parts of roughly equal size.
Each road user instance may only occur in either of those splits to avoid biasing the results.
Experiments are conducted for all three variants discussed in Section \ref{sec:methods}.
The results of these test runs are shown in Fig. \ref{fig:hidden_class_tuning}.
The goal is to maximize the hidden class detection performance without tempering with the scores on the remaining six classes.
Therefore, Fig. \ref{fig:hidden_class_tuning} indicates the true positive rate (TPR) of the hidden class and the micro-averaged F1 score based on all test samples from either class.
Micro-averaging, i.e., averaging over all samples and not classes, is the preferred metric in this case as it allows to estimate the direct impact on all classes when detecting an additional class.
\begin{figure}[tb]
\centering
\includegraphics{figures/ms-figure0}
\includegraphics{figures/ms-figure1}
\caption{Threshold estimation of the hidden class detector based on three different methods: classifier voting, OVA, and OVO+OVA thresholding. For each method, the true positive rate on hidden class detection and the micro-averaged F1 scores over all test samples is given at the bottom and top, respectively. The chosen threshold level is indicated as dashed orange line.}
\label{fig:hidden_class_tuning}
\end{figure}
The experiments indicate that all three methods are able to detect an additional hidden class without extreme losses in classification scores.
Although, the voting strategy is limited in its parameterization (only 5 or 6 vote thresholds show any effect), it provides an easy way to reach up to \SI{18}{\%} TPR for the hidden class.
OVA and combined OVO+OVA thresholds can be freely adjusted which allows to settle for more optimized choice of parameters.
However, the combined OVO+OVA approach seems inferior to the pure OVA implementation fore almost all threshold settings.
Even at high thresholds, when the combined approach reaches higher TPRs on the hidden class, the total score on all classes decreases compared to OVA thresholding.
Additional experiments were conducted with the aim to combine two or even all three proposed detection schemes.
The results did, however, not indicate any relevant improvements.
For final reported scores, the \emph{OVA thresholding} method with a corresponding threshold of 0.55 is chosen.
Hereby, the macro-averaged F1 score of the trained six classes decreases to \textbf{\SI{90.83}{\%}}, which is still a very respectable value, given the fact, that the model can now decide for an additional class label that was not in the training data.
The hidden class is detected at a rate of \textbf{\SI{29.01}{\%}} which is not a lot but far better than not being able to detect it at all.
As shown in Tab. \ref{tab:scores_hidden}, the proposed method also outperforms other methods in terms of hidden class detection rate.
A confusion matrix for the results on the new model can be found in Fig. \ref{fig:conf_7class}.
Evidently, the major confusions are present for \emph{other class} samples being classified as pedestrians or pedestrian groups.
As a large part of this class is made up by wheelchair user -- sometimes accompanied by other pedestrians -- and scooter drivers, this confusion is comprehensible.
Obviously, in the near future hidden classes in automotive radar classification tasks will no longer include scooters or wheelchair users as those classes will have representative numbers in the training data by then.
However, it is not possible to model the whole world, hence, a hidden class detector will always be of relevance.

\begin{figure}[htb]
	\centering
	\includegraphics[width=1.0\columnwidth]{./figures/cm7_cr}
	\caption{Normalized confusion matrix. All values as percentages.}
	\label{fig:conf_7class}
\end{figure}
\begin{table}[htb]
\renewcommand{\arraystretch}{1.4}
	\caption{Classification Results With Hidden Class Detector.}
	\label{tab:scores_hidden}
	\centering
	\begin{tabular}{lll}
		\hline
 		Method & $\text{F1}^{\text{6class}}_{\text{macro-avg}}$ & $\text{TPR}_{\text{hidden}}$ \\
 		\hline
 		\textbf{Proposed ensemble method} & \textbf{\SI{90.83}{\%}} & \textbf{\SI{29.01}{\%}} \\
		Shared full feature set & \SI{90.62}{\%} & \SI{24.71}{\%} \\
		Optimized shared features & \SI{90.70}{\%} & \SI{24.76}{\%}  \\
 		Shared feature set from \cite{Scheiner2018} & \SI{90.47}{\%} & \SI{27.54}{\%} \\
 		\hline
	\end{tabular}
\end{table}

\subsection*{Evaluation Of Feature Subsets}
Besides improving the overall classification score, the proposed ensemble structure gives the opportunity to more closely evaluate the performance of different features.
More specifically, the impact of each feature on a smaller and distinct classification problem can be examined.
In order to get a better overview on the distribution of features on the individual classifiers, instead of discussing features individually, all features have been divided into six different groups.
The group definitions aim to summarize features that were designed with a common purpose, e.g., to better inform the classifier about a cluster amplitude distribution or shape.
Therefore, the first four groups include features that are supposed to reveal valuable characteristics based on the basic radar values: \emph{Doppler velocity}, \emph{amplitude}, \emph{range}, and \emph{angle}.
Another group describes the \emph{shape} or compactness of a cluster.
Lastly, the \emph{microdoppler} category summarizes the interaction between velocity and spatial information, i.e., how radial velocities are distributed over the cluster area.
As it is sometimes not perfectly clear, in which category to put a feature, the category assignment information for each feature is also given in the feature overview in the appendix.

The distribution of categorized features over all classifiers in the ensemble is depicted in Fig. \ref{fig:feat_dist}.
A first observation of the displayed feature distribution is the expected tendency of classifiers to require a lower amount of features for simple classification problems.
It is clearly visible that, for instance, the \emph{pedestrian vs. truck} or \emph{pedestrian vs. car} classifier utilize a lot less features than the \emph{pedestrian vs. pedestrian group} or any one-vs-all classifier.
Despite this clear trend for facile subproblems, the variations between the number of utilized features in more difficult decision problems seem rather homogeneous.
The median number of utilized features per classifier is 85 which suggests, that many classifiers did remove a couple of features during backward elimination that had redundant information.
As there were no features that were always removed, no distinct conclusion can be made on this part.
However, this finding suggests that those classifiers might benefit from some additional features.

The influence of different feature categories on the individual classifiers can be summarized as follows:
The majority of classifiers uses all 12 range and all 14 Doppler features.
Even the \emph{pedestrian vs. truck} classifier with only 48 features in total still utilizes 11 Doppler features, despite massive removals all other categories.
Among the amplitude features, most classifiers settle at 10-11 from 13 features.
However, for \emph{angle}, \emph{shape}, and \emph{microdoppler} features a stronger correlation between total number of used features and dropped features from those categories can be observed.
This behavior is most distinct for \emph{angle} and \emph{shape} features.
A possible explanation for this, is that the angular information in the radar data is often not good enough.
As soon as the classifier has sufficiently enough good features, it starts to drop angle-based ones for better generalization performance.
This hypothesis is supported by the low angular resolution of radar sensors.
It is, therefore, expected that angular information will become more important when sensors with higher angular resolution are used.

\begin{figure}[b!]
\centering
\includegraphics{figures/ms-figure2}
\caption{Final feature distribution over classifiers in ensemble. OVA classifiers are identified by their corresponding ``one'' class, i.e., pedestrian (P), pedestrian group (G), bike (B), car (C), truck (T), or garbage (R). OVO classifiers are indicated likewise by two letters. The full feature categorization is displayed on the right for comparison.}
\label{fig:feat_dist}
\end{figure}

In order to also give an overview of the most important feature for each classifier, the merged set of the top 50 ranked features of both filter methods are depicted in Fig. \ref{fig:fixed_feat}.
Among these features, which are used as fixed set in the feature selection process, fewer clear trends are visible.
Each category has an average of $\approx5$ features except for \emph{amplitude} ($2.8$) and \emph{range} ($7.2$) features.
The lack of amplitude-based features in the fixed set suggests that a better representation of those values might be beneficial in the future.
Also, the constant high ratio of range-based and Doppler-based features in the final ensemble indicates that more features of those kinds could help the classification process even further.

\begin{figure}[tb]
\centering
\includegraphics{figures/ms-figure3}
\caption{Distributions of common features in the top 50 rankings of all classifiers in ensemble. OVA classifiers are identified by their corresponding ``one'' class, i.e., pedestrian (P), pedestrian group (G), bike (B), car (C), truck (T), or garbage (R). OVO classifiers are indicated likewise by two letters.}
\label{fig:fixed_feat}
\end{figure}

\section{Conclusion}\label{sec:conclusion}
This articles examined the benefits of providing each classifier in a binarized multiclass classification network with its own unique feature set.
Motivated by the observation, that the high complexity of the combined one-vs-one and one-vs-all ensemble could not benefit from the design of a largely increased feature set (from 50 to 98), feature selection was performed individually for each single classifier in the ensemble.
Besides improving the classification performance, this technique also allows for getting a better understanding about the influence of individual features on specific classification subproblems.
In order to reduce the computational costs of these multiple feature selection procedures, a combined approach of a backward elimination routine guided by a heuristic to only evaluate one feature at a time was chosen.
The heuristic was made up by the combination of two feature ranking procedures, namely, \emph{Joint Mutual Information} and the Relief-based \emph{MultiSURF} algorithm.
The specialized classifier ensemble reaches an averaged F1 score of \SI{91.46}{\%} which is a strong performance boost when compared to previous methods.
Moreover, it was shown how the result aggregation stage of the ensemble can be manipulated to also detect hidden classes which have not been previously seen by the model during training.
Naturally, the overall classification performance of the model decreases when allowing to detect an additional class.
However, the ability to recognize objects from classes other than the ones seen in the training data, is a vital part towards autonomous driving.
For future work it is planned to enhance current results by applying high resolution signal processing techniques that allow to increase the radar's resolution in range, angle, and Doppler.
It is expected that classifier specific feature selection helps to find adequate compromises between an excellent classification performance and high computational demands of the signal processing techniques.

\section{Acknowledgment}
The research for this article has received funding from the European Union under the H2020 ECSEL Programme as part of the DENSE project, contract number 692449.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


This stylish thermoelectric compact fridge is designed to be a portable storage container for cold drinks and cold food.
It can run continuously provided that it has the proper ventilation requirement which is a minimum of 5 inches clearance all around the unit. It also has to be maintained periodically and the unit would have to be unplugged and the back panel removed and the fan motor and heatsink cleaned. It can get dusty and that would impact performance. It may also have to be manually defrosted and unplugged for a few days to completely dry out the interior of the cooler.
Weight kg / lbs 15 lbs.


text:
2017 TV Week Logie Awards: Behind the winners
By James Manning
2017 Logies winners wrap
TV Week Gold Logie – Best Personality On Australian TV: Samuel Johnson (Molly, Seven)
Best Actor: Samuel Johnson (Molly, Seven)
Best Drama Program: Molly (Seven)
Best Actress: Jessica Marais (Love Child, Nine Network) (The Wrong Girl, Network Ten)
Nine took the opportunity to preview the new season of Love Child at its post-Logies brunch, which has become an annual fixture of Logies weekend. Amongst the guests at the event were Logie winner Marais, her new co-star Dan Hamill, Nine EPs Andy Ryan and Jo Rooney plus Playmaker Media co-founder and series EP David Taylor.
Love Child's Jessica Marais and newcomer Dan Hamill
Best Presenter: Waleed Aly (The Project, Network Ten)
There was surprisingly plenty of controversy surrounding the nominations of both Aly and Peter Helliar for the Gold Logie. The are-they-aren't-they-worthy debate seemed generally a waste of time in what is a popularity vote by TV viewers. In the end they weren't worthy of the Gold, ending the show's winning streak after successive wins for Carrie Bickmore (2015) and Waleed Aly (2016). The series however was still a multiple winner with two Logies this year.
Best News Panel Or Current Affairs Program: The Project (Ten)
Best New Talent: Rob Collins (Cleverman, ABC) (The Wrong Girl, Network Ten)
Plenty of people can put up their hand to help celebrate this win along with actor Rob Collins. His series Cleverman is a joint production by Goalpost Pictures and New Zealand's Pukeko Pictures for ABC TV in collaboration with Sundance TV and Red Arrow International. Meanwhile his work on The Wrong Girl is another victory for Playmaker Media which produced the series about a breakfast TV show for Network Ten.
Best Entertainment Program: Have You Been Paying Attention? (Ten)
The hit Monday night series returns to Network Ten on Monday May 8, which is not a day too soon for the network. It will join MasterChef Australia to help boost the channel's Monday numbers. Produced by Working Dog, the show started life late in 2013 and then ran for close to 30 weeks in the following three years. TEN has again ordered a similar amount for what will be series five. With the utmost respect to regulars Sam Pang and Ed Kavalee, the program boasts more talent off air than on with a production team that includes Rob Sitch, Santo Cilauro and Michael Hirsh. Working Dog member Tom Gleisner gets to pull double duty, however, hosting and working as an EP.
Most Outstanding Entertainment Program: Have You Been Paying Attention? (Ten)
Best Reality Program: The Block (Nine)
This was the fifth win for The Block and the TV veterans who are Cavalier Television – executive producers Julian Cress and David Barbour. They are joined by Nine's EP on the format, Justin Sturzaker. Although they had a bit of a break earlier this year with no additional series of The Block or its spinoffs, things are about to get very busy. Production started this week on series 13 of The Block with relocated homes filling the empty Elsternwick block Nine purchased for this season. Meanwhile Cress and Barbour also secured the purchase of St Kilda's Gatwick Private Hotel, meaning they could start work on the next season later this year if need be.
Best Sports Program: The NRL Footy Show (Nine)
The program's continued success at the Logies puzzles many observers given its relatively low ratings. That audience is very loyal though. The NRL wins comes after a big week of speculation about the future of the AFL Footy Show, which has just started a new year with co-hosts Rebecca Maddern and Craig Hutchison. The show sorted its own future, however, with a big ratings surge for a regular Thursday night episode a week ago.
[L] Nine CEO Hugh marks at the broadcasters post-Logies Recovery Brunch with Karl Stefanovic and Andy Lee, [R] Holding the fifth Logie won by The Block – hosts Shelley Craft and Scott Cam
Best Lifestyle Program: The Living Room (Ten)
Melbourne-based production company WTFN has moved into the Ten building in Sydney where it works on this show, which airs for 40 weeks every year. The Living Room has been nominated every year since it launched in 2012 and this was its third win. Special mention of host Amanda Keller too and her introduction to the Hall Of Fame winner on Logies' night.
Best Factual Program: Gogglebox Australia (Foxtel – Lifestyle/Ten)
The series is a hit on two networks and it has now won two Logies in successive years from a total of four nominations. The business model that sees the Endemol Shine production broadcast on first Lifestyle and then a day later on Ten should perhaps be used more often by both broadcasters to help spread their investment dollars. The win can further add momentum to Mediaweek's campaign to try to get Gogglebox on air for 40 weeks of the year instead of 20!
Most Outstanding Drama Series: A Place To Call Home (Foxtel – showcase)
This series too has been on two channels, but not at the same time. Created by Bevan Lee and produced by Julie McGauran and John Holmes for Seven Productions, the series first aired in 2013. A second season followed on Seven in 2014 before the show was dropped. An outcry from fans and some negotiating from Foxtel's Brian Walsh saw the show return in 2015 and 2016 with new seasons on Foxtel's SoHo and then showcase channels.
Most Outstanding Miniseries or Telemovie: The Kettering Incident (Foxtel – showcase)
While Seven's Molly cleaned up in the Best Drama category – plus individual wins for its star Samuel Johnson in two categories – it was Foxtel and its drama series that dominated when it came to Most Outstanding series with three of the four acting awards. Porchlight and Sweet Potato Films and executive producers Victoria Maddern and Vincent Sheehan won this award plus their castmember Henry Nixon was also a winner.
Most Outstanding Actor: Henry Nixon (Fergus McFadden, The Kettering Incident, Foxtel – showcase)
Most Outstanding Actress: Anna Torv (Harriet Dunkley, Secret City, Foxtel – showcase)
Also a dual Foxtel winner was Matchbox Pictures' adaption of the novels from Canberra journalists Chris Uhlmann and Steve Lewis from producer Penny Chapman.
Most Outstanding Supporting Actor: Damon Herriman (Kim Gordon, Secret City, Foxtel – showcase)
Most Outstanding Supporting Actress: Debra Lawrance (Mum, Please Like Me, ABC)
After nominations in 2014, 2015 and 2016, the Josh Thomas-created sitcom was a two-time winner in 2017 in the Most Outstanding section of the awards. Executive producer Todd Abbott delivered one of the best acceptance speeches of the night, clearly moved by what this series has been able to achieve in Australia and around the world over four seasons and 32 episodes. Josh Thomas' manager Kevin Whyte was sitting at the Please Like Me table on Logies night and his Guesswork TV business has been a backer of the series with support from the ABC plus Hulu and Pivot for the US.
Graham Kennedy Award For Most Outstanding Newcomer: Elias Anton (Danny Kelly, Barracuda, ABC)
Another winner for Matchbox Pictures and producers Tony Ayres and Amanda Higgs for one of the better dramas on television in 2016. Anton is a worthy winner while it is disappointing Matt Nable didn't get an award for his work in the series. Ayres and Higgs could well be winners for Seven Types Of Ambiguity this time next year.
[In addition to winning a TV Week Logie Award, the Graham Kennedy Award for Most Outstanding Newcomer winner will receive a one-year management contract in LA with Australian Artists Management, as well as a TV Week photo shoot.]
Most Outstanding Comedy Program: Please Like Me (ABC)
Most Outstanding Children's Program: Little Lunch: The Nightmare Before Graduation (ABC ME)
A first-up win for producers Robyn Butler and Wayne Hope in their debut as kids TV producers. If you've overlooked this previously, there is a fair bit of entertainment on offer for grown ups too in this mockumentary comedy series about 15 highly significant minutes of a child's life: snack time in the primary school playground.
Most Outstanding Sports Coverage: Rio 2016 Olympic Games (Channel Seven)
Most Outstanding News Coverage: Sky News Election Coverage 2016 (Sky News, Sky News/Foxtel)
A first-time Logie win for Sky News Australia after a number of nominations over the years. Election coverage doesn't get any better than this and the channel should be a regular winner at least once every four years.
Most Outstanding Public Affairs Report: Australia's Shame (Four Corners, ABC)
Most Outstanding Factual or Documentary Program: Conviction (ABC)
Conviction gets a repeat screening on May 2 and is an examination of how detectives brought Jill Meagher's killer to justice. Age journalist John Silvester is the narrator and associate producer. The doco is a DD&B Production in association with ABC TV, Film Victoria and Screen Australia. Producer/director/cinematographer/editor is Terry Carlyon and co-producer is Robyn Miller.
Top photo: Gold Logie winner Samuel Johnson with some of the TV Week team [L-R]: Editor Emma Nolan, associate editor Amber Giles, creative director Jacqueline Page and GM Jayne Ferguson.
Related Items:Featured, Logies, TV Week Logie Awards
Mercado on The TV Week Logies: Sunday night's highs and lows
TV Week Logie History: ABC, 10 & Foxtel win 21 of 24 awards
Voting now open for TV Week Logies Gold and Most Popular categories
Mediaweek Asia: Weekly Roundup
Andrew Mercado's TV Guide: Bold and The Beautiful down under + Eurovision!


I got introduced to Saadat Hasan Manto when I read Toba Tek Singh in school while studying the partition of India. Khushwant Singh's Train To Pakistan and Govind Nilhalani's Tamas completed my partition trilogy. (Tamas' author is Bhisham Shahni and I watched the Doordarshan masterpiece).
Manto kept on resurfacing at regular intervals and his impact grew on me with the passage of time. Thanda Ghosht and Khol Do threw light on monstrous brutalities committed by fellow human beings in the name of partition.
Nandita Das brings Manto back into the spotlight – a much awaited film on the genius by a sensitive and intellectual director.
Nandita's Manto depicts his journey from pre-Independent British India to post-partition India and a newly formed Pakistan. The movie's initial focus is on Manto's Bollywood days, friendship with Ismat Chughtai, family time with wife Safiya etc. One gets a glimpse of Bollywood personalities, Manto's fearless voice, his empathy for the less elite members of the society and the power of his words. Post India's independence, the evil partition divides a newly reborn country. After a brief time in Bombay, Manto moves to Pakistan. The loss of Bombay and the scars of partition slowly affect him and break him down. Liquor and cigarettes become his fast friends and he stops writing back to his friends in Bombay. His pen is still brutally naked and focusses on the monstrous human behavior. He is charged with a case of obscenity in Pakistan and he loses it. The progressive writers also attack him, and he finds himself in a minority. Eventually he goes to an asylum for de-addiction.
Returning after a decade to the director's chair, Nandita Das provides a very touching portrayal of the man behind the author and his eventual breakdown. She depicts Manto's struggles, pains and heartbreak with total empathy. Her hard-work in research is very evident.
Nawazuddin Siddiqui is at the top of his game. He looks and plays the attitude of Saadat Hasan Manto with great effectiveness. Manto is a new breaking ground for Nawazuddin, who has mastered the thriller and gangster space. Rasika Duggal is a perfect companion to Manto and she provides the right anchor. She is silent, spirited, strong and dignified. The supporting cast includes stalwarts like Paresh Rawal, Divya Dutta, Rishi Kapoor, Ranvir Shoerey, Javed Akhtar, Ila Arun, Gurdaas Maan, Niraj Kabi etc. Tahir Bhasin shines as Manto's friend/superstar Shyam. It is worth noting that most of the stars have done the film at zero or minimal cost. Nawaz has charged only Re 1 for the movie. The movie's production values, art, costume music and background score are world-class and in perfect sync with the movie's tone. 'Bol Ke Lab Azaad Hai Tere' is an anthem!
"Agar aap mere afsaano ko bardasht nahi kar sakte, toh iska matlab yeh hai ke zamaana hi naakabil-e-bardaasht hai".
The scenes are very well-designed and thoughtful. Take a very simple scene at the start of the movie – where Manto and Safiya are sitting in a park and discussing about a woman's facial hair. A simple park banter speaks of their companionship, Manto's imagination and simplicity of their lives.
Or another scene in which prompts Manto to move to Pakistan. His friend is trying to stop him from going to Pakistan. He argues and says that Manto is not a Muslim enough (in reference to his alcoholic habits). Manto retorts that he is a Muslim enough to be killed. The scene delivers in the message in the most thundering silent way!
Manto's sensitivity is seen in another scene. He is hurt when Faiz Ahmed Faiz defends Manto's writing as not obscene, but he also remarks that Manto's work is not literature. He keeps on thinking about this comment in garden. His daughter's sickness and his apology to Safiya really affect you – why does he have to suffer!
Nandita weaves Manto's famous stories into the main narrative with great dexterity but I found the stories to be too brief and directly striking the key message. I understand Nandita never wanted to talk about the stories because everyone knows them – she was highlighting the context and inspiration of those stories. But I would have loved to see Nandita's Midas touch to these timeless stories.
Many of us do not know Gandhi, so to expect people to be aware of Manto is an important assumption. The movie's promos and Mantoiyat theme had created an impression of a strong, courageous and larger-than-life Manto. People were expecting more fiery stuff whereas the movie showcased only the few years of Manto's life. This created an expectation vs reality gap. But it is director's prerogative to choose what to showcase. The chosen aspect of life is more difficult to showcase. More importantly, it is more significant to be showcased – how a creative genius withers away!
The post millennial generation cannot relate to DevDas and even Gandhi has to be wrapped in Gandhigiri. So, the film came across as a little gloomy and melancholic.
I loved Manto. It is a class act. It is a rare gem. It is one of the fine works of recent times. Its grows on you! And it really makes you think!
The impact and importance of the movie is beyond the box-office numbers!
Watch it! Do not miss it!


On January 20, 2017, over 1,000 people joined Haymarket Books, Jacobin, and Verso Books in Washington, D.C. for "The Anti-Inauguration."
With powerful speeches from Keeanga-Yamahtta Taylor, Naomi Klein, Jeremy Scahill, Anand Gopal, and Owen Jones, the event was a counterpoint to Donald Trump's (poorly attended) inauguration. It also served as a counter-inauguration for the resistance we need in the era of Trump — and a reminder of the kind of future worth fighting for.
Now, Haymarket Books is pleased to offer The Anti-Inauguration as a FREE e-book. Read it. Share it. Resist.
Each speaker at the Anti-Inauguration stressed the need for broad movements to emerge, in solidarity across lines of gender, race, and national origin. Since then, women's marches drew historic numbers in cities across the country and the world. Airports have been mobbed with emergency protests in defense of immigrants detained and denied entry to the country.
Any movement, especially one in its infancy, needs ideas. And politics. This was the very reason we came together to make The Anti-Inauguration.


text:
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Masoretic Text
The Masoretic Text is the authoritative Hebrew text of the Jewish Bible for Rabbinic Judaism. However 'contemporary scholars seeking to understand the history of the Hebrew Bible's text use a range of other sources,'. These include Greek and Syriac translations, quotations from rabbinic manuscripts, the Samaritan Pentateuch and others. Many of these are older than the Masoretic text and often contradict it. While the Masoretic Text defines the books of the Jewish canon, it also defines the precise letter-text of these biblical books, with their vocalization and accentuation known as the Masorah. The MT is widely used as the basis for translations of the Old Testament in Protestant Bibles, and in recent years also for some Catholic Bibles, although the Eastern Orthodox churches continue to use the Septuagint, as they hold it to be divinely inspired. In modern times the Dead Sea Scrolls have shown the MT to be nearly identical to some texts of the Tanakh dating from 200 BCE but different from others. The MT was primarily copied, edited and distributed by a group of Jews known as the Masoretes between the 7th and 10th centuries CE. Though the consonants differ little from the text generally accepted in the early 2nd century, it has numerous differences of both greater and lesser significance when compared to manuscripts of the Septuagint, a Greek translation of the Hebrew Scriptures that was in popular use in Egypt and Israel.
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Well - it seems that my marrow samples are sent off to a lab in London and tested against a sample taken in 2015 before I started any treatment. I was pretty sick back then and my very first biopsy only yielded a small amount of marrow sample - it was very dry and powdery and marrow should be gungy. The upshot is that there is no original marrow sample left to test against and they were unable to tell me whether the residual disease has gone or not! There are other tests that can be done but they are not as accurate and give less warning of a potential relapse. I'm not sure how I feel about that but I try not to worry about things that I have no control over.
On top of all that, I found a lump in my neck on Boxing Day - my GP thought it could be a swollen lymph node but advised to me to check with the hospital. I saw my specialist the next day and she thought the same but couldn't confirm it so sent me off for some blood tests. I've tested positive for glandular fever so they are keeping a very close eye on me at the moment - I've had 4 hospital visits since Christmas and it's only the middle of January! I don't feel too bad apart from a sore throat and some fatigue so have been taking it easy wherever possible.


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Ukraine gives Russia 48 hours to return abducted orphans
Pavlo Petrenko, Ukraine's Minister of Justice
Ukraine's Ministry of Justice, together with human rights representatives, is appealing to the European Court of Human Rights regarding the illegal abduction and transfer to the Russian Federation of orphaned children. Additionally, Ukraine has given Russia 48 hours to return the abducted children, said the Minister of Justice Pavlo Petrenko during a Cabinet briefing, as reported on the Ministry's website, June 13.
"Yesterday there was an incident involving the kidnapping of orphaned children by terrorists in the city of Snizhne. According to current information, they are in the (Russian) city of Donetsk in the Rostov Oblast of the Russian Federation. The Ministry of Justice, together with human rights representatives, has prepared and submitted a request to the European Court of Human Rights in the manner prescribed by Rule 39 of the Convention "On Protection of Human Rights" — namely, to take immediate action in cases where there is a threat to human life, rights, and freedom, Petrenko said.
According to Petrenko, actions by the Russian side have violated a number of interstate and international agreements.
"We have made a declaration to the Russian Federation stating that the Russian side, in addition to violating the Convention on Human Rights, has violated a number of international agreements regarding a state's obligation not to implement measures for the forcible removal of persons and not to cooperate with terrorists. This regards particularly the UN Convention on the protection of children and the declaration by the UN General Assembly on combating the forced removal of people," he said.
Petrenko said that in this declaration Ukraine is issuing a clear signal to competent authorities in the Russian Federation to return the abducted children through diplomatic channels.
"If Russian officials do not want to go down in history together with Asian and African dictators, who, in addition to war crimes, have been involved in the abduction of civilians and children, they have 48 hours to deliver the orphaned children to Ukraine's Consul General, who is presently in the Rostov Oblast. We expect a reaction from appropriate authorities of the Russian Federation by the end of day, Petrenko concluded.
Source: http://www.minjust.gov.ua/news/45552
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Sharing Life Across Generations
Senior Living | May 8, 2017
A glimpse into each other's lives – that's what 22 University of Denver students and 22 residents of Clermont Park have been exchanging for the past few weeks. Those insights are being shared via photography and story as part of a partnership between DU and Clermont Park.
The students are currently enrolled in a Life Walks photography and writing course led by Roddy McInnes. Roddy approached Clermont Park Life Plan Community to see if residents there would be open to participating. Students and residents were paired with the goal of interviewing and portraying each other through photography and story.
Clermont Park resident Shirley Brown, an avid photographer for more than 50 years, met with McInnes and helped map out the project. She jumped on the idea but wasn't sure whether her neighbors would share her enthusiasm. She was very surprised that they were able to fill the 22 spots fairly quickly and that the reaction has been so positive. Once the residents had met with the students and were sharing their experiences, other residents came forward and said they'd be willing to participate if such an opportunity presented itself again.
"It was great to find out people really enjoyed it," she said.
For her personally, it has been gratifying. "It was so refreshing to have a chance to meet with a young person," she said. "A lot of kids don't have a close relationship with a grandparent and they have a lot of preconceptions, as do we. It's really good to get to know somebody, and find the generalities you thought about them are not necessarily true."
The "Life Walks" photography exhibit was at Clermont Park during May 2017. This project documented the partnerships of Clermont Park residents and DU students as they shared personal photographs with each other and then wrote essays about the experience.
9News was there to cover the exhibit's opening. See the story here.
Quick and Easy Beneficiary Designation
Transforming Residents at Dayspring Villa
Words of the Wise


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Police Officers Accuse New Departmental Management of Pressure
According to information we have obtained, as of 11 December, 2012, 493 employees had been dismissed from the Ministry of Internal Affairs across the country, while 157 employees had been transferred to the Human Resources Department. The agency had recruited 186 new employees. The letter that includes this data is signed by the Deputy Head of the Public Information Bureau of the Ministry's Administrative Department, Teona Kozmanashvili.
In the letter the Ministry fails to provide reasons for either dismissing the staff, or for the transfer of employees to the Chief Human Resources Department.
Following the 2012 parliamentary elections, 27 employees of the Samegrelo-Zemo Svaneti Department of the Patrol Police of the Ministry of Internal Affairs of Georgia were transferred to the authority of the Chief Human Resources Department, where they will remain for 4 months. If their employment issues are not resolved by the end of this period, which in practice seems unlikely, the Ministry of Internal Affairs will dismiss these employees permanently.
Transparency International Georgia was directly informed about the transfers in the Samegrelo-Zemo Svaneti Department from those who had written applications to the Chief Human Resources Department. Irakli Tsaava, Zurab Soselia, Levan Jishkariani and Dazmir Janashia stated that they had been pressured into writing these applications for transfer, or else face direct dismissal. The management of the Department was replaced soon after the parliamentary elections. According to the officers, the new management asked them several times to write applications to transfer to the Human Resources Department, which they refused to do. Finally, they said that they were told "you don't meet our requirements and should leave".
Police officers told the representatives of TI Georgia's Zugdidi office that the new management of the Department has even told them that officers serving during the reign of the UNM must leave. However, it is unclear to the patrol officers why the new management focused on these 27 employees, when during the previous administration the Department had several hundred employees.
Of the 27 employees mentioned, several have contacted the Ministry of Internal Affairs in writing regarding reinstatement to their positions. Two of them have already received a response letter from the agency, in which the Deputy Head of the Department for Human Resources and Financial Logistics of the Police Department of the Ministry of Internal Affairs (the Head of the Human Resources Department, Zaza Chilaia) writes that, due to forthcoming staff reorganization in the Patrol Police Department, the issue of the employment of officers transferred to the Human Resources Department cannot be considered at this stage.
We enclose the copies of these letters: one addressee is Zurab Soselia, and the other is Davit Kolbaia (those two PDF files should be attached here).
Below is the list of employees transferred from the Samegrelo-Zemo Svaneti Department to the Chief Human Resources Department during November-December 2012:
Malkhaz Kharchilava
Levan Tordia
Lasha Lataria
Zurab Soselia
Levan Jishkariani
Davit Kolbaia
Giorgi Gegelia
Paata Siria
Dazmer Janashia
Lasha Kilasonia
Zviad Beraia
Irakli Tsaava
Davit Gulua
Elvard Akhalaia
Irakli Churghulia
Vaso Bechvaia
Giorgi Aronia
Khvicha Kvaratskhelia
Levan Tkebuchava
Irakli Partsvania
Temur Lashkhia
Zurab Kukava
Vano Lipareishvili
Giorgi Bulia
Pridon Abaishvili
Lasha Inalishvili
Davit Ghvinjilia
Additionally, another 130 employees in the rest of the country were transferred to the Chief Human Resources Department of the Ministry of Internal Affairs after the parliamentary elections.
Transparency International Georgia acknowledges that during the pre-election period certain representatives of law-enforcement units failed to react adequately to offences committed against the activists of the then opposition party, and also committed offences themselves by persecuting citizens for political beliefs. Nevertheless, TI Georgia calls on the Ministry of Internal Affairs to publicize and make transparent the reasons for dismissing employees or for transferring them to the Chief Human Resources Department. In addition, an inquiry should be conducted into the cases of pressure used against the policeman and the protection of rights of the victims should be ensured.
Author: Malkhaz Chkadua
Scan 1.pdf Scan 2.pdf


Texas Based, Internationally Focused. The Texas International Education Consortium (TIEC) has been sharing the expertise and knowledge of more than 25,000 faculty and administrators from Texas universities with the global community since 1985.
Together with TIEC's Texas Intensive English Program, we offer pathways for students to study in Texas and become globally competent and career-ready for today's workforce. We also offer a one-of-a-kind consultancy that delivers unique programs to help universities, governments, and businesses succeed on the global stage.
We have decades of experience helping institutions around the world grow their global reputation and partnerships. Our member universities include programs that are world-renowned for their academics and scholarships, have numerous specialized research centers and institutes, extension and technical training programs, and public policy and service units. Each of these resources delivers support and expertise through active participation in TIEC projects worldwide.
Have a project? We are here to help.
TIEC is committed to building deep and lasting relationships with its partners. Once we decide to work together, we create a tailor-made team of experts from the TIEC network that delivers high quality proprietary services to meet our partners' needs. We go further for the international colleagues we serve to build operational linkages with Texas universities that establish faculty-faculty collaboration and research, new university design, student mobility programs, virtual learning programs, access to each other's markets, and much more.
Take the next step toward your global future and become our partner: fill out our brief potential partner survey today, and contact Cyan Green for more information.


Yesterday we had an example of how quickly and ruthlessly a major corporation can move with BigW closing 30 stores in Australia with probably another 30 to go soon.
Today Google shut down Google+. If you have a Google+ page for your business well it is gone now. I am stunned as according to its Wikipedia page it had 100s of millions of monthly users surely there would have been some loud protest from some of these and it would be a fortune that Google has written off, I saw figures of $585 million the project had cost Google in 2011, 8 years ago, I wonder what it would be now? The investors I am sure are concerned.
What these examples do show is that business owners have to think objectively about their business just like these companies are doing to survive. If you do not do it then your competitors will do it.
Then like the Video stores (remember them), you will go.
Gone the last video library left near me.


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Who are the composers behind the most famous classical masterpieces? Take the opportunity to get to know some of them and listen to the music - maybe it will be the start of a lifelong friendship.
Who are the composers behind the most famous classical masterpieces? Take the opportunity to get to know some of them and listen to the music – maybe it will be the start of a lifelong friendship.
Ludwig van Beethoven (1770-1827)
Beethoven has become synonymous with classical music: the introduction to the Fifth Symphony has been number one on the classical lists for hundreds of years. In social life, Beethoven could be a lout with his straightforward and uncensored comments – he did not have time for flattery or obsequiousness.
Get to know Beethoven
In the past, people talked about the music's three big "B's": Bach, Beethoven and Brahms. They became white plaster busts and placed on the piano in every middle-class home, or even on a pedestal. Far away from a Hamburger guy who played the piano at cheap harbor restaurants where sailors and prostitutes flocked.
Get to know Brahms
There is a weightlessness in Grieg's music that makes it go straight in. He had a divine sense of melody and as a skilled craftsman could create an enchanted mood or a sensual intoxication in just a few moments, with seemingly simple means. The Norwegian with the hat, the drooping mustache and the sad eyes.
Get to know Grieg
Mozart is from the Rococo – the art style that is balanced, neat, clear and distinct. Just like his music. He was positive and cheerful and this in combination with an exceptional musical talent makes his music glide forward like on clouds. He was a master of the piano and a rascal on the violin – and often broke.
Get to know Mozart
This tough Finn is often associated with the forests and granite cliffs of Finnish nature, but he was just as much a world-famous socialite. Jean Sibelius' real name was Johan, called Janne, and the surname has its origins in Sibbe farm, owned by the family for generations. That it became Jean is because Sibelius inherited a box of business cards from an uncle with this name.
Get to know Sibelius
Dmitry Shostakovich (1906-1975)
The life and career of the Russian composer Dmitry Shostakovich was truly a roller coaster in the shadow of dictator Stalin. After the sensational breakthrough of the first symphony and subsequent successes, everything crashed with the opera Lady MacBeth, which Stalin thought sounded bad. But was Shostakovich an accomplice or a subversive system critic?
Get to know Shostakovich
Wilhelm Stenhammar (1871-1927)
In Sweden, Wilhelm Stenhammar has always been regarded as a composer on a large scale. Alongside Hugo Alfvén, he is the strongest candidate as a national Swedish equivalent of the Norwegians' Grieg, the Finns' Sibelius and the Danes' Nielsen. In addition, he was chief conductor of the Gothenburg Symphony Orchestra and one of the leading Swedish pianists of the time.
Get to know Stenhammar
Richard Strauss was a natural talent and with a father in the Main Opera Orchestra in Munich, there were also the external conditions. The composer often sat in the opera darkness where he got the musical experiences of his life when Wagner's huge works were staged, magnificent both in terms of scenography, performance and musical richness of sound. This is where his taste for "extra everything" was born!
Get to know R Strauss


Love this dress I'm typically a medium but ordered a small and it fit just right, I got the dress in black and for me it wasn't see through and hugs my curves in the right way and hides my stomach. Wore it to graduation yesterday and got a few compliments. Going to buy this dress again in red and gray and hopefully they're just as good as the black one.
Fits GREAT! Fast shipping. I get so many compliments when i wear it.
I ordered a couple of sizes up because I wanted it to be appropriate for church and it is. Love the dress and the color. I paired it with a gold belt and gold accessories.
Love the dress but you need to learn about sizing!!
I usual wear a large or X large, seeing that asian sizes run small I ordered XL. It was a perfect fit! I love it! Also shipped quicker than expected too! Definitely buy from them again!


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It's time to plant those veggies and tomatoes!
So we're having a class next week!
This event was a very big hit last year, please join us in welcoming Steve List of the California Association of Nurseries and Garden Centers (CANGC) and high school teacher back again this year to lead this informative workshop on how to get your veggie gardens going for the season.
You'll want to arrive early to get a good seat 🙂 There is no cost for the instruction time, no reservations required. We have plenty of free parking at the rear of the store off Jordan Avenue.


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> Celebrity News
Selena Gomez being treated for lupus
May 29, 2014 - 15:53 BST hellomagazine.com Selena Gomez's grandfather has confirmed that she's 'in treatment' for lupus after being diagnosed with the disease a couple of years ago
Selena Gomez's grandfather has revealed that the singer is being treated for lupus.
Ricardo Gomez confirmed to Radar Online that 21-year-old Selena was diagnosed with the auto-immune disease "a couple of years ago" but is now coping well with her symptoms.
"She's in treatment," said Ricardo. "She's getting along pretty good."
Ricardo added that despite his granddaughter's illness what worried him more were media reports suggesting Selena was taking drugs and the fact that she rarely takes time off work.
Selena Gomez admits 'I lost sight of who I was' in inspirational post-rehab speech
"We just saw in some newspaper that she was doing drugs. I called her and said, [you] need to watch out," he said. "She was working a lot. She was exhausted. She hardly takes some time out.
"There was some pressure, first because it's a lot of work," he added. "The doctor was telling her she needs to take some time out. It's just too much pressure what she's going through. It's just too much! She doesn't have a single day's break... It's getting to the point with all this pressure... that it's too much.
"Everyone was telling her, even her agent and the doctor, 'Selena take some time to yourself!'"
Selena is currenty in Nepal working for UNICEF
Selena, who is currently in Nepal working for UNICEF, was raised by her grandparents Ricardo and Mary for about three or four years while her parents were still in high school.
"It was kind of a hard start for them," said Ricardo. "They had dated for I would say two years prior to that. They went wild and [the pregnancy] happened.
"We kind of raised her," said Ricardo. "Selena's father, he was always working. Her mom, Mandy, too was real hard-working when she was a young lady."
Despite growing up close to his granddaughter, Ricardo said that Selena's hectic schedule means he rarely sees the Springbreakers actress.
Selena Gomez unfollows everyone on Instagram - including BFF Taylor Swift and Justin Bieber
"The sad part is we hardly see her nowadays," he explained. "Just by phone …When we talk on the phone we say, 'We don't see you in person but we see you in the news, in the newspaper, the TV… Are you ok?' She says, 'Yeah.'"
More about selena gomez
Celebrity Daily Edit - Stars step out for The Cannes Film Festival, Prince William and Kate Middleton...
The most glamorous gowns from the Cannes Film Festival 2019 opening ceremony
Justin Bieber fumes at troll who criticised his marriage to wife Hailey Baldwin
Selena Gomez's family concerned about her reunion with Justin Bieber
Selena Gomez goes grungy for new Adidas shoot
Man re-arrested for trespassing at Selena Gomez's LA home
Orlando Bloom and Selena Gomez: new Hollywood couple?
Michelle Obama, Selena Gomez and Justin Bieber lead celebrity tributes on Mother's day


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Натуральне число 1895
 1895 рік до нашої ери
 1895 рік нашої ери

redpajama_set_name: RedPajamaWikipedia


Joyce Wycoff Connecting through Art: We're Not in Kansas Anymore, Toto!
Yesterday in Redding it was blazing hot. Today on the north coast there are gale force winds ... cold, gale force winds ... and evacuation route signs for when the big one comes.
In between there and here were three hours of wildflowers, miles of wild river, tiny towns, new gold claim markers, the oldest continuously used Taoist temple in California and a stop for rocks at agate beach ... the car is now about five pounds heavier.
The beach was also covered with beach morning glories.
You sound like you're having a wonderful time and I too am happy to be tagging along!


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Balls to the Wall REISSUE
THE METAL MASTERS SERIES
Balls to the Wall is a classic album from the thundering title track to the quiet closing of Winterdeams. The band didn't find much success here in the states though, largely because of their lead singer Udo. Germany's version of Bon Scott, the small camouflage clad frontman screeched his way into the hearts of some of the heavy metal audience, but didn't have the crossover appeal to break into the mainstream American audience. The two bonus live tracks don't add much to the album, but the digital mastering should make this an essential purchase particularly to those of you that hadn't already bought this album on cd. --Scott
Balls to the Wall
London Leatherboys
Fight it Back
Losing More Than You've Ever Had
Losers and Winners
Guardian of the Night
Winterdreams
* Head Over Heels (Live)
* Love Child (Live)
VOCALS: Udo Dirkschneider
GUITAR: Wolf Hoffmann
Herman Frank
BASS: Peter Baltes
DRUMS: Stefan Kaufmann
LABEL: Portrait - Columbia - Legacy
PRODUCED BY: Jeff Magid
"I am the footprint in the sand. I am the wind that sweeps the land. I am the eyeglass of a blinded man. In the heat of desire, I'm a starfire falling down from the sky."


After a mad taxi ride through Beijing traffic, smog and really strong winds I arrived at the airport with plenty of time to spare. The flight ended up being delayed about 4 hours due to the wind. the flight to Urumqi was 4 hours and we finally arrived.
The final 20 minutes of the flight was over a vast industrial landscape with smoke stacks and huge power lines, silos and domes..
Urumqi is just as polluted as Beijing with a sulphurous yellow haze in the distance.
I met the tour group of 7 at the Tumaris hotel, a uighur run hotel with a very Turkic feel. Security is ever present everywhere you go in Urumqi. There's at least one Police building every 100 meters. When we entered the hotel we had to pass through a metal detector then get patted down. This happens every time we enter. Then we headed off to a restaurant… guess what? Metal detector and pat down! Super market?? Yep metal detector, pat down, scan with a hand held metal detector just for good measure.
Driving into the hotel carpark, our boot was checked. We had to show passports to get into the national park.. actually you need to show your passport for everything here.


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How to Engage College Students With Learning Differences
Statistics have shown that more students with learning differences (LD) are entering college campuses, according to the National Center for Learning Disabilities. As a higher education institution servicing a diverse population, Fayetteville State University (FSU) strives to make success a reality for all of its students.
Experts say UNC, NC State campuses are safer this fall. But COVID fears remain for some
Last fall, it only took a week and 135 COVID-19 cases for UNC-Chapel Hill to abandon its plan for in-person classes and dorms, sending classes online and students home. But this year, after two weeks, 394 cases and growing anxiety among students and faculty, UNC-CH is staying the course. Why is this year different?
(-) North Carolina (16)