nils-herrmann/clean_scirepeval huggingface dataset:
More Effective Synchronization Scheme in ML Using Stale Parameters

In Machine learning (ML) the model we use is increasingly important, and the model's parameters, the key point of the ML, are adjusted through iteratively processing a training dataset until convergence. Although data-parallel ML systems often engage a perfect error tolerance when synchronizing the model parameters for maximizing parallelism, the synchronization of model parameters may delay in completion, a problem that generally gets worse at a large scale. This paper presents a Bounded Asynchronous Parallel (BAP) model of computation that allows computations using stale model parameters in order to reduce synchronization overheads. In the meanwhile, our BAP model ensures theoretical convergence guarantees for large scale data-parallel ML applications. This model permits distributed workers to use the stale parameters storing in the local cache, instead of waiting until the Parameter Server (PS) produces a new version. This expressively reduces the time workers spend on waiting. Furthermore, the BAP model guarantees the convergence of ML algorithm by bounding the maximum distance of the stale parameters. Experiments conducted on 4 cluster nodes with up to 32 GPUs showed that our model significantly improved the proportion of computing time relative to the waiting time and led to 1.2–2×speedup. Besides, we elaborated how to choose the staleness threshold when considering the tradeoff between Efficiency and Speed.
A generic camera calibration method for fish-eye lenses

Fish-eye lenses are convenient in such computer vision applications where a very wide angle of view is needed. However, their use for measurement purposes is limited by the lack of an accurate, generic, and easy-to-use calibration procedure. We hence propose a generic camera model for cameras equipped with fish-eye lenses and a method for calibration of such cameras. The calibration is possible by using only one view of a planar calibration object but more views should be used for better results. The proposed calibration method was evaluated with real images and the obtained results are promising. The calibration software becomes commonly available at the author's Web page.
Warpage Simulation and Optimization of Fan-Out Wafer level Package(FO-WLP) with TMV under Different Processes

Fan-Out wafer-level package (FO-WLP) has become a mainstream advanced packaging technology and from the first generation fan-out package, the second generation of new fan-out packaging technologies, such as FO MCP, FO POP, and POP SiP is gradually derived. In FO-WLP, wafer warpage caused by mismatch of coefficients of thermal expansion (CTE) between materials has always been an important matter. This paper aims at a FO POP package, which uses through mold via (TMV) to achieve the upper and lower interconnections between packages. The packaging method is chip-first and face-up. The wafer warpage in each thermal process of the package is simulated and analyzed. At the same time, the chip spacing, molding compound thickness, TMV diameter, carrier thickness and material effect on warpage in the plastic molding process and copper layer thickness, copper area ration, dielectric layer thickness and material effect on warpage in the re-distribution layer (RDL) process is studied. And an effective method for reducing wafer warpage in each thermal process is proposed.
Low-energy wireless communication network design

Energy-efficient wireless communication network design is an important and challenging problem. Its difficulty lies in the fact that the overall performance depends, in a coupled way, on the following subsystems: antenna, power amplifier, modulation, error control coding, and network protocols. In addition, given an energy constraint, improved operation of one of the aforementioned subsystems may not yield better overall performance. Thus, to optimize performance one must account for the coupling among the above subsystems and simultaneously optimize their operation under an energy constraint. In this article we present a generic integrated design methodology that is suitable for many kinds of mobile systems and achieves global optimization under an energy constraint. By pointing out some important connections among different layers in the design procedure, we explain why our integrated design methodology is better than traditional design methodologies. We present numerical results of the application of our design methodology to a situational awareness scenario in a mobile wireless network with different mobility models. These results illustrate the improvement in performance that our integrated design methodology achieves over traditional design methodologies, and the trade-off between energy consumption and performance .
WAVES: A Wearable Asymmetric Vibration Excitation System for Presenting Three-Dimensional Translation and Rotation Cues

WAVES, a Wearable Asymmetric Vibration Excitation System, is a novel wearable haptic device for presenting three dimensions of translation and rotation guidance cues. In contrast to traditional vibration feedback, which usually requires that users learn to interpret a binary cue, asymmetric vibrations have been shown to induce a pulling sensation in a desired direction. When attached to the fingers, a single voicecoil actuator presents a translation guidance cue and a pair of voicecoil actuators presents a rotation guidance cue. The directionality of mechanoreceptors in the skin led to our choice of the location and orientation of the actuators in order to elicit very strong sensations in certain directions. For example, users distinguished a "left" cue versus a "right" cue 94.5% of the time. When presented with one of six possible direction cues, users on average correctly identified the direction of translation cues 86.1% of the time and rotation cues 69.0% of the time.
Tactile roughness: neural codes that account for psychophysical magnitude estimates.

Hypothetical neural codes underlying the sensation of tactile roughness were investigated in a combined psychophysical and neurophysiological study. The stimulus set consisted of plastic surfaces embossed with dot arrays of varying dot diameter and center-to-center spacing. Human subjects explored each surface with the pad of the index finger and reported their subjective sense of roughness magnitude. The same surfaces were scanned across the receptive fields of cutaneous mechanoreceptive afferents in monkeys while recording the evoked action potentials. Hypothetical neural codes for roughness magnitude were computed from the neural response patterns and tested for their ability to account for the psychophysical data. The psychophysical results showed that subjective roughness magnitude is an inverted U-shaped function of dot spacing that peaks near 3.0 mm spacing, and that increased dot diameter produces decreased roughness sensations at all dot spacings. Hypothetical neural codes that do not bear a consistent relationship to roughness magnitude across all of these stimulus conditions can be rejected as the code for roughness. Four types of neural codes were considered. They were based on (1) mean firing rate, (2) general variation in firing rate, (3) short-term temporal variation in firing rate, and (4) local spatial variation in firing rate. Mean firing rate failed to explain the psychophysical results: surfaces that evoked the same firing rate often evoked very different roughness judgments. In contrast, neural codes based on firing-rate variation, especially in slowly adapting afferents, account for the psychophysical results.
18-year experience in the management of men with a complaint of a small penis.

In many cultures, the erect penis has been a symbol of masculine qualities. Because of this symbolism, a penis that is less than average size can cause insecurity or embarrassment. This series reports the authors' 18-year experience in the management of 60 men with a complaint of a small penis. For 44 of these 60 men, counseling was sufficient; the other 16 had surgery, and of these, 9 were satisfied with the result. Despite limitations, the authors conclude that those men who already achieve a penis length of no less than 7.5 cm (2.95 in) in erection, have only limited benefit from penis-enhancing surgery. This particular patient category should therefore be dissuaded from surgery.
Realized Volatility and Variance : Options via Swaps

Let St denote the value of a stock or stock index at time t. Given a variance/volatility option to be priced and hedged, let us designate as time 0 the start of its averaging period, and time T the end. For t ∈ [0, T ], and τ ≤ t, let R2 τ,t denote the realized variance of returns over the time interval [τ, t]. The mathematical results about the synthesis of volatility and variance swaps will hold exactly if R2 refers to the continuously-monitored variance. This means the quadratic variation of logS, ∗Bloomberg L.P. and NYU Courant Institute pcarr@nyc.rr.com. †University of Chicago. RL@math.uchicago.edu. We thank the International Securities Exchange, Kris Monaco,
Enhancing the RGR Routing Protocol for Unmanned Aeronautical Ad-hoc Networks

The design of a routing protocol for Unmanned Aeronautical Ad-hoc Networks (UAANETs) is quite challenging, especially due to the high mobility of Unmanned Aerial Vehicles (UAVs) and the low network density. Among all routing protocols that have been designed for UAANETs, the Reactive-Greedy-Reactive (RGR) protocol has been proposed as a promising routing protocol in high mobility and density-variable scenarios. While prior results have shown that RGR is competitive to AODV or other purely reactive routing protocols, it is not fully exploiting all the unique features of UAANETs and the knowledge maintained by intermediate nodes. This report presents a number of different enhancements, namely scoped flooding, delayed route request, and mobility prediction, in order to improve the performance of RGR in terms of overhead, packet delivery ratio and delay respectively. These modifications were implemented in Opnet, and simulation results show that we can reduce the protocol overhead by about 30%, while at the same time increasing PDR by about 3-5% and reducing packet latency. The results further show that there is still significant scope for further performance improvements.
Skill Rating for Generative Models

We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes: tournament win rate and skill rating. Evaluations are useful in different contexts, including monitoring the progress of a single model as it learns during the training process, and comparing the capabilities of two different fully trained models. We show that a tournament consisting of a single model playing against past and future versions of itself produces a useful measure of training progress. A tournament containing multiple separate models (using different seeds, hyperparameters, and architectures) provides a useful relative comparison between different trained GANs. Tournament-based rating methods are conceptually distinct from numerous previous categories of approaches to evaluation of generative models, and have complementary advantages and disadvantages.
Emotional BCI control of a smart wheelchair

In this paper, an emotional BCI control system to drive a smart wheelchair is proposed. The proposed BCI control system permits to the user to select one of four commands to drive the wheelchair. Once a command is selected, the control system executes the selected command and, at the same time, monitors the emotional state of the user. While the user is satisfied, the selected command is still executed and when the user becomes unsatisfied, the control system will stop the wheelchair and ask the user to select another command.
Cross Selling of Financial Products – A Study Based on Customers in Kerala

Cross-selling is defined as "the action or practice of selling among or between established clients, markets, traders, etc." or "that of selling an additional product or service to an existing customer “It is a sales technique in which the salesperson recognizes what a customer is purchasing and will make suggestions or recommendations of other related merchandise the shopper may also be interested in purchasing. It is also known as suggestive selling. It also stands for being able to offer to the existing bank customers, some additional banking products, with a view to expand banking business, reduce the per customer cost of operations and provide more satisfaction and value to the customer. An existing customer provides an advantage to the seller over the competitor. The satisfied customer will always consider the same seller for other requirements before searching for other sellers. The seller can make use of this situation and the customer will be pleased as well.
A double-tail sense amplifier for low-voltage SRAM in 28nm technology

A double-tail sense amplifier (DTSA) is designed as a drop-in replacement for a conventional SRAM sense amplifier (SA), to enable a robust read operation at low voltages. A pre-amplification stage helps reduce the offset voltage of the sense amplifier by magnifying the input of the regeneration stage. The self-timed regenerative latch simplifies the timing logic so the DTSA can replace the SA with no area overhead. A test chip in 28nm technology achieves 56% error rate reduction at 0.44V. The proposed scheme achieves 50mV of VDDmin reduction compared to commercial SRAM with a faster timing option that demonstrates a smaller bitline swing.
Viscero-visceral hyperalgesia: Characterization in different clinical models

Co-existing algogenic conditions in two internal organs in the same patient may mutually enhance pain symptoms (viscero-visceral hyperalgesia). The present study assessed this phenomenon in different models of visceral interaction. In a prospective evaluation, patients with: (a) coronary artery disease (CAD)+gallstone (Gs) (common sensory projection: T5); (b) irritable bowel syndrome (IBS)+dysmenorrhea (Dys) (T10-L1); (c) dysmenorrhea/endometriosis+urinary calculosis (Cal)(T10-L1); and (d) gallstone+left urinary calculosis (Gs+LCal) (unknown common projection) were compared with patients with CAD, Gs, IBS, Dys or Cal only, for spontaneous symptoms (number/intensity of pain episodes) over comparable time periods and for referred symptoms (muscle hyperalgesia; pressure/electrical pain thresholds) from each visceral location. In patients' subgroups, symptoms were also re-assessed after treatment of each condition or after no treatment. (a) CAD+Gs presented more numerous/intense angina/biliary episodes and more referred muscle chest/abdominal hyperalgesia than CAD or Gs; cardiac revascularization or cholecystectomy also reduced biliary or cardiac symptoms, respectively (0.001<p<0.05). (b) IBS+Dys had more intestinal/menstrual pain and abdomino/pelvic muscle hyperalgesia than IBS or Dys; hormonal dysmenorrhea treatment also reduced IBS symptoms; IBS dietary treatment also improved dysmenorrhea (0.001<p<0.05) while no treatment of either conditions resulted in no improvement in time of symptoms from both.
Health-Related Disaster Communication and Social Media: Mixed-Method Systematic Review.

This mixed-method evidence synthesis drew on Cochrane methods and principles to systematically review literature published between 2003 and 2016 on the best social media practices to promote health protection and dispel misinformation during disasters. Seventy-nine studies employing quantitative, qualitative, and mixed methods on risk communication during disasters in all UN-languages were reviewed, finding that agencies need to contextualize the use of social media for particular populations and crises. Social media are tools that still have not become routine practices in many governmental agencies regarding public health in the countries studied. Social media, especially Twitter and Facebook (and equivalents in countries such as China), need to be incorporated into daily operations of governmental agencies and implementing partners to build familiarity with them before health-related crises happen. This was especially observed in U.S. agencies, local government, and first responders but also for city governments and school administrations in Europe. For those that do use social media during health-related risk communication, studies find that public relations officers, governmental agencies, and the general public have used social media successfully to spread truthful information and to verify information to dispel rumors during disasters. Few studies focused on the recovery and preparation phases and on countries in the Southern hemisphere, except for Australia. The vast majority of studies did not analyze the demographics of social media users beyond their geographic location, their status of being inside/outside the disaster zone; and their frequency and content of posting. Socioeconomic demographics were not collected and/or analyzed to drill deeper into the implications of using social media to reach vulnerable populations. Who exactly is reached via social media campaigns and who needs to be reached with other means has remained an understudied area.
Detecting Clickbait in Online Social Media: You Won't Believe How We Did It

In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user’s attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given contentis useful for clickbait detection.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data

Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model, both on the small human action recognition dataset of SBU and the currently largest NTU dataset.
Circular Quadruple-Ridged Flared Horn Achieving Near-Constant Beamwidth Over Multioctave Bandwidth: Design and Measurements

A circular quadruple-ridged flared horn achieving almost-constant beamwidth over 6:1 bandwidth is presented. This horn is the first demonstration of a wideband feed for radio telescopes which is capable of accommodating different reflector antenna optics, maintains almost constant gain and has excellent match. Measurements of stand-alone horn performance reveal excellent return loss performance as well as stable radiation patterns over 6:1 frequency range. Physical optics calculations predict an average of 69% aperture efficiency and 13 K antenna noise temperature with the horn installed on a radio telescope.
Research on key application issues of smart synchronous rectifier driver IC in LLC resonant converter

In this paper, several key application issues of smart synchronous rectifier (SR) driver IC which is applied in LLC resonant converter are studied and researched. With the smart SR diver IC applied to drive the secondary-side SRs, the LLC resonant converter can further improve the efficiency and be suitable for the application which requires high power density such as brick power modules. Many smart SR driver ICs have been produced and are widely used in this application. However, with the smart SR driver IC applied in the LLC resonant converter, the reverse current problem in secondary side may occur when the converter operates at the status of light load, start up and load transient. The reverse current will low down the efficiency of circuit or even break down the converter. This paper mainly studies and analyzes the reasons of the reverse current problem caused by the utilization of smart SR driver IC in LLC resonant converter in detail and proposes some simple solutions in theory as well. The experimental results are obtained from a 400W (12V/33A) power module prototype which verifies the theoretical analysis.
FEBEI-Face Expression Based Emoticon Identification CSB 657 Computer Vision

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.
A new weight initialization method for sigmoidal feedforward artificial neural networks

Initial weight choice has been recognized to be an important aspect of the training methodology for sigmoidal feedforward neural networks. In this paper, a new mechanism for weight initialization is proposed. The mechanism distributes the initial input to output weights in a manner that all weights (including thresholds) leading into a hidden layer are uniformly distributed in a region and the center of the region from which the weights are sampled are such that no region overlaps for two distinct hidden nodes. The proposed method is compared against random weight initialization routines on five function approximation tasks using the Resilient Backpropagation (RPROP) algorithm for training. The proposed method is shown to lead to about twice as fast convergence to a pre-specifled goal for training as compared to any of the random weight initialization methods. Moreover, it is shown that at least for these problems the networks reach a deeper minima of the error functional during training and generalizes better than the networks trained whose weights were initialized by random weight initialization methods.