nils-herrmann/clean_scirepeval huggingface dataset:
Real-time dynamic wrinkling of coarse animated cloth

Dynamic folds and wrinkles are an important visual cue for creating believably dressed characters in virtual environments. Adding these fine details to real-time cloth visualization is challenging, as the low-quality cloth used for real-time applications often has no reference shape, an extremely low triangle count, and poor temporal and spatial coherence. We introduce a novel real-time method for adding dynamic, believable wrinkles to such coarse cloth animation. We trace spatially and temporally coherent wrinkle paths, overcoming the inaccuracies and noise in low-end cloth animation, by employing a two stage stretch tensor estimation process. We first employ a graph-cut segmentation technique to extract spatially and temporally reliable surface motion patterns, detecting consistent compressing, stable, and stretching patches. We then use the detected motion patterns to compute a per-triangle temporally adaptive reference shape and a stretch tensor based on it. We use this tensor to dynamically generate new wrinkle geometry on the coarse cloth mesh by taking advantage of the GPU tessellation unit. Our algorithm produces plausible fine wrinkles on real-world data sets at real-time frame rates, and is suitable for the current generation of consoles and PC graphics cards.
"PASS" principles for predictable bone regeneration.

Guided bone regeneration is a well-established technique used for augmentation of deficient alveolar ridges. Predictable regeneration requires both a high level of technical skill and a thorough understanding of underlying principles of wound healing. This article describes the 4 major biologic principles (i.e., PASS) necessary for predictable bone regeneration: primary wound closure to ensure undisturbed and uninterrupted wound healing, angiogenesis to provide necessary blood supply and undifferentiated mesenchymal cells, space maintenance/creation to facilitate adequate space for bone ingrowth, and stability of wound and implant to induce blood clot formation and uneventful healing events. In addition, a novel flap design and clinical cases using this principle are presented.
A recent development of antenna-in-package for 5G millimeter-wave applications (Invited paper)

Millimeter-wave technology brings a new paradigm of wireless communication in various areas including mobile devices, automotive, IoT (Internet of Things), medical, military and many others. In this paper, we present our recently developed antenna in a package and system for 5G (Fifth-Generation) millimeter-wave applications. The antenna is configured to be an array to compensate path loss and maintain data link budget. The antenna is designed with a multi-layer laminate substrate and is integrated with a 5G mmW RFIC. 5G NR (New Radio) wireless connection is demonstrated with a RFIC transceiver at 28 GHz band. Interoperability test is conducted to ensure compatibility with the 3GPP standard. Finally, indoor and outdoor field test including beamforming and beamtracking are presented to show realistic system-level throughput performance.
A First Step Towards Automated Detection of Buffer Overrun Vulnerabilities

We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotely-exploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed
A contextual-bandit approach to personalized news article recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.
 In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
 The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
A Partial Derandomization of PhaseLift using Spherical Designs

The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced algorithm for phase recovery that is computationally efficient, numerically stable, and comes with rigorous performance guarantees. PhaseLift is optimal in the sense that the number of amplitude measurements required for phase reconstruction scales linearly with the dimension of the signal. However, it specifically demands Gaussian random measurement vectors — a limitation that restricts practical utility and obscures the specific properties of measurement ensembles that enable phase retrieval. Here we present a partial derandomization of PhaseLift that only requires sampling from certain polynomial size vector configurations, called t-designs. Such configurations have been studied in algebraic combinatorics, coding theory, and quantum information. We prove reconstruction guarantees for a number of measurements that depends on the degree t of the design. If the degree is allowed to to grow logarithmically with the dimension, the bounds become tight up to polylog-factors. Beyond the specific case of PhaseLift, this work highlights the utility of spherical designs for the derandomization of data recovery schemes.
Return of Frustratingly Easy Domain Adaptation

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being “frustratingly easy” to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple–it can be implemented in four lines of Matlab code–CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. “Everything should be made as simple as possible, but not simpler.”
Region-of-interest based rate control scheme for high efficiency video coding

In this paper, we propose a new rate control scheme designed for the newest high efficiency video coding (HEVC) standard, and aimed at enhancing the quality of regions of interest (ROI). Our approach allocates a higher bit rate to the region of interest while keeping the global bit rate close to the assigned target value. This algorithm is developed for a videoconferencing system, where the ROIs (typically, faces) are automatically detected and each coding unit is classified in a region of the interest map. This map is given as input to the rate control algorithm and the bit allocation is made accordingly. Experimental results show that the proposed scheme achieves accurate target bit rates and provides an improvement in the region of interest quality, both in objective metrics and based on subjective quality evaluation.
IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi [Invited Paper]

With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave band. However, multi-gigabit-per-second throughput and small interference footprint come at the price of adverse signal propagation characteristics, and require a fundamental rethinking of Wi-Fi communication principles. This article describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular, we study the transition from omnidirectional to highly directional communication and its impact on the design of IEEE 802.11ad.
Compact E-Cash

This paper presents efficient off-line anonymous e-cash schemes where a user can withdraw a wallet containing 2 coins each of which she can spend unlinkably. Our first result is a scheme, secure under the strong RSA and the y-DDHI assumptions, where the complexity of the withdrawal and spend operations is O( + k) and the user’s wallet can be stored using O( +k) bits, where k is a security parameter. The best previously known schemes require at least one of these complexities to be O(2 · k). In fact, compared to previous e-cash schemes, our whole wallet of 2 coins has about the same size as one coin in these schemes. Our scheme also offers exculpability of users, that is, the bank can prove to third parties that a user has double-spent. We then extend our scheme to our second result, the first e-cash scheme that provides traceable coins without a trusted third party. That is, once a user has double spent one of the 2 coins in her wallet, all her spendings of these coins can be traced. However, the price for this is that the complexity of the spending and of the withdrawal protocols becomes O( · k) and O( · k + k) bits, respectively, and wallets take O( · k) bits of storage. All our schemes are secure in the random oracle model.
Event Detection in Social Media Detecting

The proliferation of social media and user-generated content in the Web has opened new opportunities for detecting and disseminating information quickly. The Twitter stream is one large source of information, but the magnitude of tweets posted and the noisy nature of its content makes the harvesting of knowledge from Twitter very hard. Aiming at overcoming some of the challenges and extract some of the hidden information, this thesis proposes a system for real-time detection of news events from the Twitter stream. The first step of our approach is to let a classifier, based on an Artificial Neural Network and deep learning, detect news relevant tweets from the stream. Next, a novel streaming data clustering algorithm is applied to the detected news tweets to form news events. Finally, the events of highest interest is retrieved based on events’ sizes and rapid growth in tweet frequencies, before the news events are presented and visualized in a web user interface. We evaluate the proposed system on a large, publicly available corpus of annotated news events from Twitter. As part of the evaluation, we compare our approach with a related state-of-the-art solution. Overall, our experiments and user-based evaluation show that our approach on detecting current (real) news events delivers state-of-the-art performance.
Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning

Emerging paradigms like High Performance Data Analytics (HPDA) and Deep Learning (DL) pose at least two new design challenges for existing MPI runtimes. First, these paradigms require an efficient support for communicating unusually large messages across processes. And second, the communication buffers used by HPDA applications and DL frameworks generally reside on a GPU's memory. In this context, we observe that conventional MPI runtimes have been optimized over decades to achieve lowest possible communication latency for relatively smaller message sizes (up-to 1 Megabyte) and that too for CPU memory buffers. With the advent of CUDA-Aware MPI runtimes, a lot of research has been conducted to improve performance of GPU buffer based communication. However, little exists in current state of the art that deals with very large message communication of GPU buffers. In this paper, we investigate these new challenges by analyzing the performance bottlenecks in existing CUDA-Aware MPI runtimes like MVAPICH2-GDR, and propose hierarchical collective designs to improve communication latency of the MPI_Bcast primitive by exploiting a new communication library called NCCL. To the best of our knowledge, this is the first work that addresses these new requirements where GPU buffers are used for communication with message sizes surpassing hundreds of megabytes. We highlight the design challenges for our work along with the details of design and implementation. In addition, we provide a comprehensive performance evaluation using a Micro-benchmark and a CUDA-Aware adaptation of Microsoft CNTK DL framework. We report up to 47% improvement in training time for CNTK using the proposed hierarchical MPI_Bcast design.
LonchaNet: A sliced-based CNN architecture for real-time 3D object recognition

In the last few years, Convolutional Neural Networks (CNNs) had become the default paradigm to address classification problems, specially, but not only, in image recognition. This is mainly due to the high success rate that they provide. Despite there currently exist approaches that apply deep learning to the 3D recognition problem, they are either too slow for online uses or too error prone. To fill this gap, we propose LonchaNet, a deep learning architecture for point clouds classification. Our system successfully achieves a high accuracy yet providing a low computation cost. A dense set of experiments were carried out in order to validate our system in the frame of the ModelNet — a large-scale 3D CAD models dataset — challenge. Our proposal achieves a success rate of 94.37% in the ModelNet-10 classification task, the second place in the leaderboard as of today (November, 2016).
Profiling users in a 3g network using hourglass co-clustering

With widespread popularity of smart phones, more and more users are accessing the Internet on the go. Understanding mobile user browsing behavior is of great significance for several reasons. For example, it can help cellular (data) service providers (CSPs) to improve service performance, thus increasing user satisfaction. It can also provide valuable insights about how to enhance mobile user experience by providing dynamic content personalization and recommendation, or location-aware services.
 In this paper, we try to understand mobile user browsing behavior by investigating whether there exists distinct "behavior patterns" among mobile users. Our study is based on real mobile network data collected from a large 3G CSP in North America. We formulate this user behavior profiling problem as a "co-clustering" problem, i.e., we group both users (who share similar browsing behavior), and browsing profiles (of like-minded users) simultaneously. We propose and develop a scalable co-clustering methodology, Phantom, using a novel hourglass model. The proposed hourglass model first reduces the dimensions of the input data and performs divisive hierarchical co-clustering on the lower dimensional data; it then carries out an expansion step that restores the original dimensions. Applying Phantom to the mobile network data, we find that there exists a number of prevalent and distinct behavior patterns that persist over time, suggesting that user browsing behavior in 3G cellular networks can be captured using a small number of co-clusters. For instance, behavior of most users can be classified as either homogeneous (users with very limited set of browsing interests) or heterogeneous (users with very diverse browsing interests), and such behavior profiles do not change significantly at either short (30-min) or long (6 hour) time scales.
Architecture-Based Runtime Software Evolution Information and Computer Science

Continuous availability is a critical requirement for an important class of software systems. For these systems, runtime system evolution can mitigate the costs and risks associated with shutting down and restarting the system for an update. We present an architecture-based approach to runtime software evolution and highlight the role of software connectors in supporting runtime change. An initial implementation of a tool suite for supporting the runtime modification of software architectures, called ArchStudio, is presented.
Transferring a semantic representation for person re-identification and search

Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge

This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge. Our method builds upon the idea of convolutional part heatmap regression , extending it for 3D face alignment. Our method decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z (depth) estimation. At the first stage, our method estimates the X,Y coordinates of the facial landmarks by producing a set of 2D heatmaps, one for each landmark, using convolutional part heatmap regression. Then, these heatmaps, alongside the input RGB image, are used as input to a very deep subnetwork trained via residual learning for regressing the Z coordinate. Our method ranked 1st in the 3DFAW Challenge, surpassing the second best result by more than 22%. Code can be found at http://www.cs.nott.ac.uk/~psxab5/
Common-centroid capacitor placement considering systematic and random mismatches in analog integrated circuits

One of the most important issues during the analog layout phase is to achieve accurate capacitance ratios. However, systematic and random mismatches will affect the accuracy of the capacitance ratios. A common-centroid placement is helpful to reduce the systematic mismatch, but it still needs the property of high dispersion to reduce the random mismatch . To deal with this problem, we propose a simulated annealing [15] based approach to construct a common-centroid placement which exhibits the highest possible degree of dispersion. To facilitate this framework, we first propose the pair-sequence representation to represent a common-centroid placement. Then, we present three operations to perturb the representation, which can increase the degree of dispersion without breaking the common-centroid constraint in the resulting placement. Finally, to enhance the efficiency of our simulated annealing based approach, we propose three techniques to speed up our program. The experimental results show that our placements can simultaneously achieve smaller oxide-gradient-induced mismatch and larger overall correlation coefficients (i.e., higher degree of dispersion) than [10] in all test cases. Besides, our program can run much faster than  in larger benchmarks.
TCP Ordo: The cost of ordered processing in TCP servers

To achieve scalable, high-throughput, low-latency packet processing, TCP implementations are parallelized across cores in multicore platforms. This, however, significantly affects the order in which packets from different flows are delivered to application processing. Our measurements record up to 75% of the packets are delivered to applications in a way that does not match the order in which they are received on the network interface. For many important classes of applications, such as financial services, bidding and trading engines, game engines, this cross-flow packet reordering affects their ability to provide fairness guarantees. To address this gap, we propose TCP-Ordo - a TCP stack which provides strict ordering of packets across multiple flows, as well as flexibility to control the degree to which ordering is enforced. TCP-Ordo outperforms existing TCP implementations in both latency and throughput. The current prototype is implemented as a user-level TCP stack for Mellanox Connectx3 NICs with 40Gbps Ethernet interfaces. Without ordering guarantees TCP-Ordo delivers one way latency of 4.75usec (a 5x improvement over the Linux kernel) for 150B packets and throughput of 22Gbps (a 2x improvement over mTCP) for 1500B packets. Furthermore, with TCP-Ordo applications are provided with strict packet order delivery, with performance that continues to be superior to the state-of-the-art, even when enforcing packet order across 800,000 connections on a 12-core platform. Finally, TCP-Ordo supports the notion of `ordered domains' that offer flexibility in the degree of ordering that an application will experience, and pay for.
Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications

The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any “thing” we may be interested in. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring applications.