Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphPar: Efficient Workload-Aware Subgraph Matching System on Multiple GPUs

Subgraph matching (SM) has witnessed tremendous progress in recent years, enabling a broad spectrum of big data applications. SM applications are extremely computeintensive since they require tremendous set operations, i.e., enumerating all the possible vertex pairs and counting the common neighbor of each pair. GPU is potentially promising hardware to accelerate SM applications due to its massive parallelism. However, SM applications achieve low efficiency and often fail to deliver high performance in multi-GPU systems owing to irregular edge distribution which exhausts the computing power and aggravates the load-imbalance problems. Although many existing frameworks have proffer numerous methods at high-level to improve the efficiency of GPU-based SM, e.g., assign matching order, early termination, and automorphismbreaking, the low-level issues on GPU architecture and system, e.g., thread mapping, graph partitions are not well addressed. In this work, we develop GraphPar, an efficient SM system targeting multi-GPUs. GraphPar proposes an effective workload- aware scheduling and an efficient set operation designing, which could successfully reduce the stragglers and significantly accelerate SM. Experiments on a V100 GPU cluster show that GraphPar is up to 4.21 × faster than the state-of-the-art GPU-based GPM system G2 Miner.
Children affected by earthquakes and their immediate emotional needs

English The article explores the way in which Iranian children were affected by grief after being uprooted and having experienced the loss of family members, their homes and communities. Lack of access to information about what had happened to their immediate family members and uncertainty concerning the future were the most important expressed worries. French Cette étude examine la façon dont les enfants iraniens ont immédiatement été affectés par la douleur après s’être vus déracinés et avoir vécu la perte des membres de leur famille, de leur maison, de leur communauté. Les deux principales détresses exprimées sont le manque d’information concernant le sort des membres de leur proche famille et l’incertitude quant à leur avenir. Spanish El artículo explora la manera en que los niños iraníes fueron afectados, en forma inmediata, por pena después de haber sido desarraigados y de haber experimentado la pérdida de miembros familiares, sus hogares y comunidades. La carencia de acceso a información sobre lo que ocurrió a los miembros de sus familias inmediatas y la incertidumbre sobre el futuro, fueron las preocupaciones más importantes expresadas.
Range-Aware Hand Gesture Recognition Using FMCW Radar and Deep Learning

This paper introduces a hand gesture classification implementation that combines FMCW radar and deep learning. Unlike previous work, we implement a Range-Aware methodology to automatically locate the hand range bin enabling more accurate classification. The inspiration behind our proposal is the observations made about the impact of the subject chest in the Range-Time (RT) information and the signature of the hand movement in the Range-Doppler (RD) space. Subsequently, we propose three distinct methods for hand range bin localization. Method I and II take advantage of the first observation by locating the chest and using it as a reference to select the hand bin. Method III, however, exploits the second observation about the Doppler signature of the hand movement to directly select the hand bin. The Doppler matrix resulting from each method is then fed to a CNN-based model for the hand gesture classification task. We perform subject-dependent and independent evaluations to classify six hand gestures and investigate the impact of several parameters including the type of input data, the use of an LSTM layer, and an increased range of up to 90 cm. The evaluation results show good performance even for subject-independent and from distance up to 90 cm achieving an average accuracy of 99.86% in subject-dependent evaluations and 94.23% in subject-independent scenarios.
Whose Trauma Is It Anyway? Furthering Our Understanding of Its Intergenerational Transmission

This article proposes that our understanding of the complex mechanisms of intergenerational transmission of trauma is enhanced by utilizing Wilfred Bion’s theory of group dynamics in looking at family dynamics, as they affect intrapsychic development. Family dynamics are viewed according to the Bionion concept of a “work group,” and its functioning in terms of task, authority, role, and boundary. Freudian and Ferenczi’s contributions to our understanding of identification with the aggressor are included as well. This multilayered and theoretically interwoven approach to the individual and the family group can increase our clinical effectiveness in working with children and families, especially when they are struggling with the effects of collective traumas. Clinical examples of the intergenerational effect of the experiences of the Holocaust on Jewish families and their offspring are explored in detail. The author maintains that this perspective offers potential for working with those whose family history includes collective traumas such as slavery and racism, war-related violence and homophobic reactions.
Can MDL Improve Unsupervised Chinese Word Segmentation?

It is often assumed that Minimum Descrip- tion Length (MDL) is a good criterion for unsupervised word segmentation. In this paper, we introduce a new approach to unsupervised word segmentation of Man- darin Chinese, that leads to segmentations whose Description Length is lower than what can be obtained using other algo- rithms previously proposed in the litera- ture. Suprisingly, we show that this lower Description Length does not necessarily corresponds to better segmentation results. Finally, we show that we can use very basic linguistic knowledge to coerce the MDL towards a linguistically plausible hypoth- esis and obtain better results than any pre- viously proposed method for unsupervised Chinese word segmentation with minimal human effort.
Exploring Cross-Channel Texture Correlation for Color Texture Classification

This paper proposes a novel approach to encode cross-channel texture correlation for color texture classification task. Firstly, we quantitatively study the correlation between different color channels using Local Binary Pattern (LBP) as the texture descriptor and using Shannon’s information theory to measure the correlation. We find that (R, G) channel pair exhibits stronger correlation than (R, B) and (G, B) channel pairs. Secondly, we propose a novel descriptor to encode the cross-channel texture correlation. The proposed descriptor can capture well the relative variance of texture patterns between different channels. Meanwhile, our descriptor is computationally efficient and robust to image rotation. We conduct extensive experiments on four challenging color texture databases to validate the effectiveness of the proposed approach. The experimental results show that the proposed approach significantly outperforms its mostly relevant counterpart (Multichannel color LBP), and achieves the state-of-the-art performance.
Chemosensitivity, plasticity, and functional heterogeneity of paraganglionic cells in the rat coeliac‐superior mesenteric complex

Chemosensitivity and plasticity of paraganglionic cells in the rat coeliac‐superior mesenteric complex (CSMC) were investigated at a basal state of normoxia (21% O2) and after long‐term moderate hypoxia (10% O2, 14 days). Chemical sympathectomy previous to hypoxia was performed to destroy principal ganglionic neurons and thus to allow measurement of the norepinephrine and dopamine content of paraganglionic cells.
AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%.
Joint 5D Pen Input for Light Field Displays

Light field displays allow viewers to see view-dependent 3D content as if looking through a window; however, existing work on light field display interaction is limited. Yet, they have the potential to parallel 2D pen and touch screen systems, which present a joint input and display surface for natural interaction. We propose a 4D display and interaction space using a dual-purpose lenslet array, which combines light field display and light field pen sensing, and allows us to estimate the 3D position and 2D orientation of the pen. This method is simple, fast (150Hz), with position accuracy of 2-3mm and precision of 0.2-0.6mm from 0-350mm away from the lenslet array, and orientation accuracy of 2 degrees and precision of 0.2-0.3 degrees within a 45 degree field of view. Further, we 3D print the lenslet array with embedded baffles to reduce out-of-bounds cross-talk, and use an optical relay to allow interaction behind the focal plane. We demonstrate our joint display/sensing system with interactive light field painting.
Clonal Expansion of Biofilm-Forming Salmonella Typhimurium ST34 with Multidrug-Resistance Phenotype in the Southern Coastal Region of China

To disclose the antibiotics susceptibility and wide adaptability of commonly occurring genotypes of Salmonella Typhimurium, the antibiotic resistance and biofilm formation of different multi-locus sequence typing (MLST) types of a collection of 240 S. Typhimurium isolates (33 food and 207 clinical ones) during 2010–2014 in Shenzhen were analyzed. Among these strains, 167 was ST34 (69.58%), and 57 was ST19 (23.75%), respectively. A total of 159 (95.21%) ST34 strains displayed the multidrug resistant phenotype (≥ three classes of antibiotic), whereas only 23 (40.35%) ST19 ones did (P < 0.01). Moreover, a relative high proportion (72.46%) of ST34 isolates was classified as moderate to strong biofilm-producers, while only 15.79% of ST19 (P < 0.01) was. Among the food isolates, more than half (51.52%) were from livestock products, among which 41.18% classified as moderate to strong biofilm-producers. In summary, this study highlights the expansion of S. Typhimurium ST34 of strong biofilm-forming ability and multidrug resistance in the southern coastal region of China. Therefore, monitoring the occurrence of ST34 S. Typhimurium in food sources, especially in livestock products, and taking appropriate measures to control Salmonella spp. infections via decreasing biofilm formation should be addressed.
Emerging research

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Vulnerability Detection and Monitoring Using LLM

Large Language Models (LLMs) have evolved as a cornerstone for intricate code evaluations in the modern realm of artificial intelligence and machine learning. The prioritizing of rigorous security requirements is a crucial requirement for the business in the dynamic and ever-changing world of software development. The current study has used the capabilities of the GPT-3.5- Turbo model to conduct a detailed assessment of various code snippets to find any vulnerabilities. The main objective of the experiment was to introduce continuous monitoring technologies to enhance software security and release control. To obtain reliable results, we used a classification report and a confusion matrix. Out of these validation methods we choose accuracy as an important metric for this validation because in this experiment we need our model to predict the vulnerabilities that are present in the 2740 test cases and we would need our model to focus more on true positives(TP). The ideal goal of this experiment was to predict any kind of vulnerability from the real-world data. Out of all test cases, we were able to have an accuracy of 0.77. This demonstrates the approach's potential efficacy in discovering vulnerabilities. Nonetheless, the study found certain parts that require improvement, emphasizing the importance of continual refinement in the model's methodology to ensure more thorough security assessments. This study lays the groundwork for future research into the use of powerful machine learning models in the assessment of software vulnerabilities. The findings not only highlight the effectiveness of the existing approach but also offer light on prospective future research directions, paving the way for the next generation of models and evaluation techniques.
The sensitization of high threshold mechanoreceptors with myelinated axons by repeated heating.

1. Seventy high threshold mechanoreceptor units (HTMs) with myelinated axons were isolated from the sural nerves of cats and rabbits. Thirteen cat and forty‐two rabbit HTMs were testec by controlled, repeated heating of the skin of the foot or lower leg to noxious levels. 2. Many of the units (77% in the cat and 40% in the rabbit) fired to heating. Only six (11%) of these fired to the first brief heating to 50‐55 degrees C. The rest required 2‐6 heat trials before responding. 3. Heat responding units always became more sensitive with repeated heat stimulation but their mechanical sensitivity showed no comparable changes when heat sensitization occurred. 4. If these results are applicable to man, they suggest that HTMs play little role in generating the first pain that follows skin heating but that they may be involved in the increased sensitivity to heat pain (hyperalgesia) shown by skin previously injured by heating.
Thread-level speculation with kernel support

Runtime systems for speculative parallelization can be substantially sped up by implementing them with kernel support. We describe a novel implementation of a thread-level speculation (TLS) system using virtual memory to isolate speculative state, implemented in a Linux kernel module. This design choice not only maximizes performance, but also allows to guarantee soundness in the presence of system calls, such as I/O. Its ability to maintain speedups even on programs with frequent mis-speculation, significantly extends its usability, for instance in speculative parallelization. We demonstrate the advantage of kernel-based TLS on a number of programs from the Cilk suite, where this approach is superior to the state of the art in each single case (7.28x on average). All systems described in this paper are made available as open source.
Rationality, optimism and guarantees in general reinforcement learning

In this article, we present a top-down theoretical study of general reinforcement learning agents. We begin with rational agents with unlimited resources and then move to a setting where an agent can only maintain a limited number of hypotheses and optimizes plans over a horizon much shorter than what the agent designer actually wants. We axiomatize what is rational in such a setting in a manner that enables optimism, which is important to achieve systematic explorative behavior. Then, within the class of agents deemed rational, we achieve convergence and finite-error bounds. Such results are desirable since they imply that the agent learns well from its experiences, but the bounds do not directly guarantee good performance and can be achieved by agents doing things one should obviously not. Good performance cannot in fact be guaranteed for any agent in fully general settings. Our approach is to design agents that learn well from experience and act rationally. We introduce a framework for general reinforcement learning agents based on rationality axioms for a decision function and an hypothesis-generating function designed so as to achieve guarantees on the number errors. We will consistently use an optimistic decision function but the hypothesis-generating function needs to change depending on what is known/assumed. We investigate a number of natural situations having either a frequentist or Bayesian flavor, deterministic or stochastic environments and either finite or countable hypothesis class. Further, to achieve sufficiently good bounds as to hold promise for practical success we introduce a notion of a class of environments being generated by a set of laws. None of the above has previously been done for fully general reinforcement learning environments.
PROGRAMMED LEARNING OF A SECOND LANGUAGE

Lcs processus Je l'apprentissage programme d'une langue etrangere sont examines selon les principes du conditionnement operant qui est a la base des techniques de la programmation. Les methodes utilisees dans divers programmes afin de conditionner des repertoires formels (Imitation, transcription, lecture et dictee) et des repertoires thematiques (traduction, appellation, interrogation, etc.) sont interpretees selon l'evidence experimentale pour les processus de comportement impliques : discrimination des Stimuli et differenciation des reponses. Das programmierte Erlernen einer Fremdsprache wurde in vorliegendem Beitrag im Rahmen der wirksamsten Konditionierung auf der Basis der Programmierungstechnik untersucht. V i", p rüf t dabei die in einer Vielzahl von Programmen verwendeten Methoden z u r Kondit ionierung formaler Sachgebiete (Nachahmung, Transkription, Lesen und Diktataufnahme), sowie thematischer Sachgebiete (Übersetzung, Begriffseinordnung, Fragestellung etc.). Diese Untersuchung erfolgt im Hinblick auf den klaren Einfluß des Sprachlabors auf die Verhaltensweise des Schülers: Unterscheidung von Stimulanten und Differenzierung der Antworten.
- Food Science of Animal Resources - Upload

Taste is one of the five senses that detect vital information about what we are eating. Comprehending taste is crucial for enhancing the flavor of foodstuffs and high-protein foods like meat. Umami has global recognition as the fifth elementary taste, alongside sweetness, sourness, saltiness, and bitterness. Umami compounds are known to enhance the sensation of recognized flavors such as salty, sweet, bitter, and others. This could end up in greater food consumption by consumption by consumers. With the rising global population, meat consumption is rising and is projected to double by 2025. It is crucial to comprehend the umami mechanism of meat and meat products, identify novel compounds, and employ laboratory methodologies to gather varied information. This knowledge will aid in the development of new consumer products. Although very limited information is available on umami taste and compounds in meat through research data. This article discusses recent advancements in umami compounds in other foodstuff as well as meat to aid in designing future research and meat product development. Moreover, another objective of this review is to learn present techniques in foodstuffs to enhance umami taste and utilize that knowledge in meat products.
Path following for an omnidirectional mobile robot based on model predictive control

In this paper, the path following problem of an omnidirectional mobile robot has been studied. Given the error dynamic model derived from the robot state vector and the path state vector, model predictive control (MPC) is employed to design the control law, which can deal explicitly with the rate of progression of a virtual vehicle to be followed along the path. The distinct advantage over other control strategies is that input and system constraints are able to be handled straightforwardly in the optimization problem so that the robot can travel safely with a high velocity. Unlike nonholonomic mobile robots, omnidirectional mobile robots, which we focus on in this paper, have simultaneously and independently controlled rotational and translational motion capabilities. Then, our purposed MPC controller was validated by experiments with a real omnidirectional mobile robot.
Towards a virtual agent using similarity-based laughter production

Facial expressions of emotions are often described statically at their apex. Lately several researchers (Keltner, 1995) have shown through analysis of video corpora that emotions are expressed through a sequence of micro-behaviours. These micro-behaviours correspond to signals spread over the whole body (face, head, gaze, gesture, etc). All these signals do not have to occur simultaneously. Some of them occur more often at the beginning of expressions, some others at the end, some occur in a sequence, while others are synchronized (Keltner, 1995).
Learning the patterns of balance in a multi-player shooter game

A particular challenge of the game design process is when the designer is requested to orchestrate dissimilar elements of games such as visuals, audio, narrative and rules to achieve a specific play experience. Within the domain of adversarial first person shooter games, for instance, a designer must be able to comprehend the differences between the weapons available in the game, and appropriately craft a game level to take advantage of strengths and weaknesses of those weapons. As an initial study towards computationally orchestrating dissimilar content generators in games, this paper presents a computational model which can classify a matchup of a team-based shooter game as balanced or as favoring one or the other team. The computational model uses convolutional neural networks to learn how game balance is affected by the level, represented as an image, and each team's weapon parameters. The model was trained on a corpus of over 50,000 simulated games with artificial agents on a diverse set of levels created by 39 different generators. The results show that the fusion of levels, when processed by a convolutional neural network, and weapon parameters yields an accuracy far above the baseline but also improves accuracy compared to artificial neural networks or models which use partial information, such as only the weapon or only the level as input.