Miracle-dz/UniFAR-FTUs huggingface dataset:
A Topology-Independent TDMA MAC Policy for Safety Applications in Vehicular Networks

The problem of accessing the medium to support safety applications in vehicular networks is challenging, when vehicles’ mobility increases and time constraints become tight. In such environments, and particularly for vehicular-to-vehicular communication where safety is the main concern, time delay bounded data packet delivery is a prerequisite. Consequently, topology-independent MAC policies that guarantee a number of successful transmissions independently of the underlying topology, can be employed as a suitable choice for the particular vehicular environment. One such policy is revisited in this paper and its performance is demonstrated against VeMAC – a well-established MAC protocol in the area of vehicular networks – using simulation results. As it is shown in this paper, throughput under the considered topology-independent policy remains comparable to that of VeMAC whereas the number of retransmissions are significantly reduced leading to a reduced time delay.
Alchemist: An Apache Spark ⇔ MPI interface

The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce‐style programming model can incur significant overheads when performing computations that do not map directly onto this model. One way to mitigate these costs is to off‐load computations onto MPI codes. In recent work, we introduced Alchemist, a system for the analysis of large‐scale data sets. Alchemist calls MPI‐based libraries from within Spark applications, and it has minimal coding, communication, and memory overheads. In particular, Alchemist allows users to retain the productivity benefits of working within the Spark software ecosystem without sacrificing performance efficiency in linear algebra, machine learning, and other related computations. In this paper, we discuss the motivation behind the development of Alchemist, and we provide a detailed overview of its design and usage. We also demonstrate the efficiency of our approach on medium‐to‐large data sets, using some standard linear algebra operations, namely, matrix multiplication and the truncated singular value decomposition of a dense matrix, and we compare the performance of Spark with that of Spark+Alchemist. These computations are run on the NERSC supercomputer Cori Phase 1, a Cray XC40.
New And Surprising Ways to Be Mean

Creating Non-Player Characters (NPCs) that can react robustly to unforeseen player behaviour or novel game content is difficult and time-consuming. This hinders the design of believable characters, and the inclusion of NPCs in games that rely heavily on procedural content generation. We have previously addressed this challenge by means of empowerment, a model of intrinsic motivation, and demonstrated how a coupled empowerment maximisation (CEM) policy can yield generic, companion-like behaviour. In this paper, we extend the CEM framework with a minimisation policy to give rise to adversarial behaviour. We conduct a qualitative, exploratory study in a dungeon-crawler game, demonstrating that CEM can exploit the affordances of different content facets in adaptive adversarial behaviour without modifications to the policy. Changes to the level design, underlying mechanics and our character’s actions do not threaten our NPC’s robustness, but yield new and surprising ways to be mean.
Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.
Emotional facial expression transfer from a single image via generative adversarial nets

Facial expression transfer from a single image is a challenging task and has drawn sustained attention in the fields of computer vision and computer graphics. Recently, generative adversarial nets (GANs) have provided a new approach to facial expression transfer from a single image toward target facial expressions. However, it is still difficult to obtain a sequence of smoothly changed facial expressions. We present a novel GAN‐based method for generating emotional facial expression animations given a single image and several facial landmarks for the in‐between stages. In particular, landmarks of other subjects are incorporated into a GAN model to control the generated facial expression from a latent space. With the trained model, high‐quality face images and a smoothly changed facial expression sequence can be effectively obtained, which are showed qualitatively and quantitatively in our experiments on the Multi‐PIE and CK+ data sets.
(Simulated) listener gaze in real‐time spoken interaction

Gaze is an important aspect of social communication. Previous research has concentrated mainly on the role of speaker gaze and listener gaze in isolation, neglecting the effect of the listener's gaze behavior on the speaker's behavior. This paper presents an exploratory eye‐tracking study involving an interactive human‐like agent following participants' gaze. This study demonstrates that a rather simple gaze‐following mechanism convincingly simulates active listening behavior engaging the speaker. The study also highlights how speakers rely on their interlocutors' gaze when establishing common references.
LSTM Benchmarks for Deep Learning Frameworks

This study provides benchmarks for different implementations of LSTM units between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras. The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized, but more flexible LSTM implementations. The benchmarks reflect two typical scenarios for automatic speech recognition, notably continuous speech recognition and isolated digit recognition. These scenarios cover input sequences of fixed and variable length as well as the loss functions CTC and cross entropy. Additionally, a comparison between four different PyTorch versions is included. The code is available online this https URL.
Accelerating the Data Deduplication Performance with GPU in Hybrid Storage Systems

The explosive increase in data production has made the problem of data storage space worse. Data compression has been actively adopted in backup storage as a way to improve storage space. However, the compression not only failed to completely remove the replica across the cluster, but also had difficulties applying it immediately to in-line mode due to high compression overhead. As a result, the community began applying deduplication techniques that divide the data into smaller chunks, calculate the fingerprint of the chunks, identify the fingerprint in the lookup table, and then insert new data or maintain references to existing data. This paper explores the inline deduplication of the SSD layer in a hybrid scaleout tiered storage system for cost and performance efficiency that employs a high-speed SSD as the cache layer for lowspeed HDDs. We configure Ceph [1] as tiered storage and build inline deduplication on the SSD layer. Deduplication has a significant impact on storage performance and cost in a tiered architecture. First, we investigate the performance degradation of Ceph [1], after enabling data deduplication across the cluster. We observed an overhead of 27% due to fingerprint computation during an investigation that seriously degraded the overall cluster performance. To reduce fingerprinting overhead in inline deduplication, we propose a technique that uses a GPU accelerator to optimize fingerprint computation to improve cluster performance. By using our Approach we obtain a 50% dedup performance improvement.
Experimental and Numerical Evaluations of the Viscoelastic Mechanical Behavior of Arterial Tissue

The viscoelastic properties of arterial tissue dictate vessel behavior in certain disease states, injury modalities, and during some endovascular procedures. In this study, we characterized the viscoelastic mechanical response of porcine abdominal aortic tissue via uniaxial mechanical experiments on vascular tissue samples coupled with full-field surface strain measurements using the 2-D digital image correlation (DIC) technique. The measured stress relaxation response and intimal surface strain field were used to identify material parameters for a proposed viscoelastic anisotropic (VA) constitutive model of the passive arterial wall. The obtained results show that the VA constitutive model is able to capture the viscoelastic mechanical behavior of the arterial tissue over clinically-relevant time scales. The identified material model and numerical simulations provide a comprehensive description of the passive viscoelastic tissue properties of the arterial wall, and a quantitative understanding of the spatial and directional variability underlying arterial tissue mechanical behavior.
EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing

Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs). This paper proposes an easy yet efficient technique for both Convolutional Neural Network training and testing. The conventional convolution and pooling operations are replaced by Easy Convolution and Random Pooling (ECP). In ECP, we randomly select one pixel out of four and only conduct convolution operations of the selected pixel. As a result, only a quarter of the conventional convolution computations are needed. Experiments demonstrate that the proposed EasyConvPooling can achieve 1.45x speedup on training time and 1.64x on testing time. What's more, a speedup of 5.09x on pure Easy Convolution operations is obtained compared to conventional convolution operations.
An Accurate Estimate of the Free Energy and Phase Diagram of All-DNA Bulk Fluids

We present a numerical study in which large-scale bulk simulations of self-assembled DNA constructs have been carried out with a realistic coarse-grained model. The investigation aims at obtaining a precise, albeit numerically demanding, estimate of the free energy for such systems. We then, in turn, use these accurate results to validate a recently proposed theoretical approach that builds on a liquid-state theory, the Wertheim theory, to compute the phase diagram of all-DNA fluids. This hybrid theoretical/numerical approach, based on the lowest-order virial expansion and on a nearest-neighbor DNA model, can provide, in an undemanding way, a parameter-free thermodynamic description of DNA associating fluids that is in semi-quantitative agreement with experiments. We show that the predictions of the scheme are as accurate as those obtained with more sophisticated methods. We also demonstrate the flexibility of the approach by incorporating non-trivial additional contributions that go beyond the nearest-neighbor model to compute the DNA hybridization free energy.
Cross-lingual complex word identification with multitask learning

We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach. Our method is highly language agnostic, as evidenced by the ability of our system to generalize across languages, including languages for which we have no training data. In the shared task, this is the case for French, for which our system achieves the best performance. We further provide a qualitative and quantitative analysis of which words pose problems for our system.
Investigation of Rotor–Airframe Interaction Noise Associated with Small-Scale Rotary-Wing Unmanned Aircraft Systems

In this study, acoustic measurements of a hover condition are taken on isolated rotor–airframe configurations representative of small-scale, rotary-wing unmanned aircraft systems (UAS). Each rotor–airframe configuration consists of two fixed-pitch blades powered by a brushless motor, with a simplified airframe geometry intended to represent a generic multicopter arm. In addition to acoustic measurements, computational fluid dynamics–based aeroacoustic predictions are implemented on a subset of the experimentally tested rotor–airframe configurations in an effort to better understand the noise content of the rotor–airframe systems. Favorable agreements are obtained between acoustic measurements and predictions, based on both time- and frequency-domain postprocessing techniques. Results indicate that close proximity of airframe surfaces results in the generation of considerable tonal acoustic content in the form of harmonics of the rotor blade passage frequency (BPF). Analysis of the acoustic prediction data shows that the presence of the airframe surfaces can generate noise levels either comparable to or greater than the rotor blade surfaces under certain rotor tip clearance conditions. Analysis of the on-surface Ffowcs Williams and Hawkings source terms provides insight as to the predicted physical noise-generating mechanisms on the rotor and airframe surfaces.
Semantic modeling of indoor scenes with support inference from a single photograph

We present an automatic approach for the semantic modeling of indoor scenes based on a single photograph, instead of relying on depth sensors. Without using handcrafted features, we guide indoor scene modeling with feature maps extracted by fully convolutional networks. Three parallel fully convolutional networks are adopted to generate object instance masks, a depth map, and an edge map of the room layout. Based on these high‐level features, support relationships between indoor objects can be efficiently inferred in a data‐driven manner. Constrained by the support context, a global‐to‐local model matching strategy is followed to retrieve the whole indoor scene. We demonstrate that the proposed method can efficiently retrieve indoor objects including situations where the objects are badly occluded. This approach enables efficient semantic‐based scene editing.
Cross-Fostering Implications for Pig Mortality, Welfare and Performance

This study aimed to (1) identify cross-fostering (CF) practices employed on a commercial farm; (2) characterize CF pigs according to litter of origin traits, and (3) investigate the implications of CF practices on pig mortality, performance and welfare. This was an observational study whereby pigs were managed according to normal farming practice. Pigs (n = 1,016) born within 1 week were classified as non-CF (NCF); CF during the first (CFW1) and second or third (CFW2+) weeks of lactation. Pigs were individually weighed and inspected for the presence of tail (TL), ear (EL) and body lesions (BL) at weaning (7.03 ± 1.61 kg) and at the end of the first (12.9 ± 3.03 kg) and second (31.9 ± 5.50 kg) weaner and grower (66.3 ± 9.12 kg) stages. Mortality was recorded through to slaughter (c. 115 kg). At slaughter, TL were scored and carcass characteristics, presence of pleurisy, enzootic pneumonia, pericarditis and heart condemnations were recorded. 40.8% of CF pigs were CFW1; ANOVA tests revealed these were born to sows with a higher number of piglets born alive than NCF pigs (14.6 ± 2.61 and 12.8 ± 2.68, respectively). The remaining 59.2% of CF pigs were CFW2+; these were, on average, 0.14 kg lighter at birth than NCF pigs. Therefore, a nested case control design was retrospectively applied whereby pigs with complete records to slaughter, were matched for these variables to investigate associations between CF weeks, welfare and performance traits. Growth performance did not differ between CF week (P > 0.05); however, CFW2+ carcasses were 4.9 kg lighter (P < 0.05) compared with NCF and CFW1 pigs. EL were more likely in CFW1 compared to NCF and CFW2+ (P < 0.05) pigs. To investigate the effect of CF week on the risk of mortality, all 1,016 pigs were used. CF pigs were at higher risk of death (P < 0.05) with similar odds in CFW1 and CFW2+ pigs compared with NCF pigs, although other underlying factors could contribute to this result. Performance and health traits were similar between CF weeks. Early cross-fostering appeared to influence the presence of ear lesions but the mechanism is likely indirect and difficult to explain.
Competition in the Venture Capital Market and the Success of Startup Companies: Theory and Evidence

We examine the effect of a competitive supply of venture capital (VC) on the exits (IPO or M\&A) of startups. We develop a matching model with double-sided moral hazard, and identify a novel differential effect of VC competition on the success of startups. Using VC data, we find evidence for this differential effect. For example, when the VC market becomes more competitive (HHI decreases by 50 percent from its mean of 0.08), the absolute likelihood of success increases by 2.8 percent for startups backed by less experienced VC firms, but it decreases by 3.6 percent for startups backed by the most experienced VC firms
Deep Image Compression via End-to-End Learning

We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate. Currently, most of the CNNs based approaches train the network using a L2 loss between the reconstructions and the ground-truths in the pixel domain, which leads to over-smoothing results and visual quality degradation especially at a very low bit rate. Therefore, we improve the subjective quality with the combination of a perception loss and an adversarial loss additionally. To achieve better rate-distortion optimization (RDO), we also introduce an easy-to-hard transfer learning when adding quantization error and rate constraint. Finally, we evaluate our method on public Kodak and the Test Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.
THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY POST-QUANTUM CRYPTOGRAPHY

Modern cryptography relies on the intractability of certain problems in mathematics, such as those related to factoring, discrete logarithms, and elliptic curves. Cryptographers have exploited the hardness of such problems to construct the world’s most widely used cryptosystems, which have become critical in matters of electronic privacy, banking, national security, and others. These systems, resting on inefficiencies in classical computing, will likely become obsolete in the presence of a machine with superior computing ability. We assess that the introduction of quantum computing will force cryptographers to reexamine the schemes they once thought to be robust against computationally bounded adversaries. In 2017, 69 research groups from around the world submitted proposals to the United States’ National Institute of Standards and Technology (NIST), suggesting new quantum resistant public-key infrastructures and signature schemes for standardization. We have examined the quantum resistant public-key systems presented to NIST, grouped them based on security assumption, and recommend the most viable option, considering resistance to known quantum attacks, efficiency, and fast implementation under existing classical
Fabrication and In Vitro Characterization of Electrochemically Compacted Collagen/Sulfated Xylorhamnoglycuronan Matrix for Wound Healing Applications

Skin autografts are in great demand due to injuries and disease, but there are challenges using live tissue sources, and synthetic tissue is still in its infancy. In this study, an electrocompaction method was applied to fabricate the densely packed and highly ordered collagen/sulfated xylorhamnoglycuronan (SXRGlu) scaffold which closely mimicked the major structure and components in natural skin tissue. The fabricated electrocompacted collagen/SXRGlu matrices (ECLCU) were characterized in terms of micromorphology, mechanical property, water uptake ability and degradability. The viability, proliferation and morphology of human dermal fibroblasts (HDFs) cells on the fabricated matrices were also evaluated. The results indicated that the electrocompaction process could promote HDFs proliferation and SXRGlu could improve the water uptake ability and matrices’ stability against collagenase degradation, and support fibroblast spreading on the ECLCU matrices. Therefore, all these results suggest that the electrocompacted collagen/SXRGlu scaffold is a potential candidate as a dermal substitute with enhanced biostability and biocompatibility.
Selenide-Containing Polyimides with an Ultrahigh Intrinsic Refractive Index

This work developed novel selenium-containing polyimides with a high intrinsic refractive index. Four polyimides with different selenium contents and repeat unit structures were designed and synthesized via amine-dianhydride polycondensation of one of two diamines, i.e., 4,4′-oxydianiline or bis(4-aminophenyl)selanide, with one of two dianhydrides, i.e., bis(4-(3,4-dicarboxylbenzoyloxy)phenyl) ester dianhydride or 1,1′-bis(4-(3,4-dicarboxylbenzoyloxy)phenyl) selenide dianhydride. Various techniques, e.g., nuclear magnetic resonance, Fourier transformed infrared spectroscopy, and wide-angle X-ray diffraction, were used to characterize the polymers’ structures. Differential scanning calorimetry, thermogravimetric analysis, ultraviolet-visible spectroscopy, and spectroscopic ellipsometry were used to characterize the properties of the polymers. The selenium contents showed a positive effect on the refractive index of the final polymer. In addition, the refractive index can reach up to 1.968 at 633 nm, which was the highest intrinsic refractive index of a polyimide ever reported. Because of the high intrinsic refractive index, the reflective ratio of visible light on the surface of a silicon wafer was significantly reduced, indicating the potentially utility of the polymer in an anti-reflection coating.
Automatic Input Enrichment for Selecting Reading Material: An Online Study with English Teachers

Input material at the appropriate level is crucial for language acquisition. Automating the search for such material can systematically and efficiently support teachers in their pedagogical practice. This is the goal of the computational linguistic task of automatic input enrichment (Chinkina & Meurers, 2016): It analyzes and re-ranks a collection of texts in order to prioritize those containing target linguistic forms. In the online study described in the paper, we collected 240 responses from English teachers in order to investigate whether they preferred automatic input enrichment over web search when selecting reading material for class. Participants demonstrated a general preference for the material provided by an automatic input enrichment system. It was also rated significantly higher than the texts retrieved by a standard web search engine with regard to the representation of linguistic forms and equivalent with regard to the relevance of the content to the topic. We discuss the implications of the results for language teaching and consider the potential strands of future research.