Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation Recognition by Clustering-Based Method in Virtual Soccer

In multi-agent systems, the task of adapting to the world is extremely difficult due to interactions of agents and a constantly changing world. There is an important problem to recognize formations in multi-agent systems with team opposition, because formation can describe the interactions of team agents. Knowing an opposing team formation, an agent’s behavior can be adapted to performing effective and timely actions. The paper provides a brief overview of existing approaches (home area method, classification using neural networks and machine learning and others), their main advantages and disadvantages. Also, an alternative clustering-based approach was proposed to solve the problem. The problem is solved in the virtual soccer environment RoboCup Simulation 2D. It is the most famous platform for testing models of team opposing nowadays. Features of the environment are limited perception, dynamic world changing and noisiness. Recognition algorithm was designed, implemented and tested.
Notes on Avron's Self-extensional Four-valued Paradefinite Logic

Avron‘s (propositional) self-extensional four-valued paradefinite logic, referred to here as A4 in a Gentzen-type sequent calculus style, is re-investigated. The logic A4 is known to be the unique self-extensional extension of Dunn-Belnap logic (also referred to as Belnap-Dunn logic) by adding classical implication. It is observed that A4 is the classical-negation-free fragment of De and Omori's axiomatic extension BD+ of Dunn-Belnap logic by adding classical negation and classical implication. Theorems for embedding A4 into a Gentzen-type sequent calculus LK for propositional classical logic (with classical negation) are proved, and the completeness (with respect to a valuation semantics) and cut-elimination theorems for A4 are obtained using these embedding theorems. Similar theorems are also obtained for an extension A4c of A4 by adding conflation.
Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming

In this paper, the deep learning instance segmentation architectures DetectoRS, SOLOv2, DETR and Mask R-CNN were applied to data from the field of Pig Precision Livestock Farming to investigate whether these models can address the specific challenges of this domain. For this purpose, we created a custom dataset consisting of 731 images with high heterogeneity and high-quality segmentation masks. For evaluation, the standard metric for benchmarking instance segmentation models in computer vision, the mean average precision, was used. The results show that all tested models can be applied to the considered domain in terms of prediction accuracy. With a mAP of 0.848, DetectoRS achieves the best results on the test set, but is also the largest model with the greatest hardware requirements. It turns out that increasing model complexity and size does not have a large impact on prediction accuracy for instance segmentation of pigs. DETR, SOLOv2, and Mask R-CNN achieve similar results to DetectoRS with a parameter count almost three times smaller. Visual evaluation of predictions shows quality differences in terms of accuracy of segmentation masks. DetectoRS generates the best masks overall, while DETR has advantages in correctly segmenting the tail region. However, it can be observed that each of the tested models has problems in assigning segmentation masks correctly once a pig is overlapped. The results demonstrate the potential of deep learning instance segmentation models in Pig Precision Livestock Farming and lay the foundation for future research in this area.
Directional and Qualitative Feature Classification for Speaker Diarization with Dual Microphone Arrays

Automatic meeting transcription has long been one of the common applications for natural language processing methods. The quality of automatic meeting transcription for the cases of distant speech acquired by a common audio recording device suffers from the negative effects of distant speech signal attenuation, distortion imposed by reverberation and background noise pollution. Automatic meeting transcription mainly involves the tasks of Automatic Speech Recognition (ASR) and speaker diarization. While state-of-the-art approaches to ASR are able to reach decent recognition quality on distant speech, there still exists a lack of prominent speaker diarization methods for the distant speech case. This paper studies a set of directional and qualitative features extracted from a dual microphone array signal and evaluates their applicability to speaker diarization for the noisy distant speech case. These features represent respectively the speaker spatial distribution and the intrinsic signal quality properties. Evaluation of the feature sets is performed on real life data acquired in babble noise conditions by conducting several classification experiments aimed at distinguishing between utterances produced by different conversation participants and between those produced by the background speakers. The study shows that specific sets of features result in satisfying classification accuracy and can be further investigated in experiments combining them with biometric and other types of properties.
Practical Comparison of High-Level Synthesis and Hardware Generation Frameworks: CPU Floating Point Unit Case

The research is devoted to analyzing and matching advantages and drawbacks of various high-level design environments for the components of modern CPU cores. In the paper, high-level synthesis (HLS) and hardware generation frameworks (HGF) are compared for the case of floating-point execution unit (FPU). We use HGF-based FPU available in open-source SonicBOOM RISC-V CPU design from Berkeley as reference. Original HLS-based design of FPU module is proposed. This design is functionally equivalent to HGF-based one, but is described in behavioral (untimed) style, and its microarchitecture is optimized automatically by the HLS tool. The designed FPU has been synthesized in Vivado HLS and successfully tested in FPGA device. The research has shown that raising abstraction level up to behavioral one has provided the design with comparable frequency and resource characteristics, however, with significantly more concise design specification and automatic generation of microarchitecture. Based on these estimations, we envision HLS to be promising not only for accelerators that are external from CPUs, but also for selective, execution-centric components of modern CPUs themselves.
GC-MS analysis of the chemical component of volatile oil from leaves of Pyracantha fortuneana

The volatile oil from the leaves of Pyracantha fortuneana was extracted by water-steam distillation and the chemical component was determined by GC-MS.The relative contents of the chemical component was determined with peak area normalization method.Sixty-nine compounds accounting for 80.52% of total volatile oil were identified.The major constituents were identified as(-)-b-cadinene(22.62%),plant alcohol(19.90%),bicyclosesquiphellandrene(5.95%),β-eudesmol(5.78%),1,2,3,4,4a,7hexahydro-1,6-dimethyl-4-(1-methylethyl)-naphthalene(2.78%),and epizonarene(2.34%).
Arrangement of Block Copolymer Microdomains Confined inside Hemispherical Cavities

We investigated, via scanning and transmission electron microscopy, the arrangement of the microdomains of symmetric polystyrene-block-poly(methyl methacrylate) copolymer (PS-b-PMMA) confined in hemispherical cavities. The hemispherical cavities were prepared by using anodic aluminum oxide (AAO) template, where the inner surface of the cavities was modified by thin brush layers of PS, PMMA, and PS-ran-PMMA copolymer. When the inner surface of the cavity is strongly selective to PS chains, concentric rings consisting of PS and PMMA microdomains are formed, replicating the confined geometry of hemisphere. However, as the selectivity of a brush to PS chains becomes weaker, various interesting morphologies are formed that have not been reported in the literature. The experimentally observed microdomain arrangement confined inside the hemispherical cavities was compared with the simulation results based on the dynamic Metropolis Monte Carlo method.
Cerebellar ataxia, neuropathy, vestibular areflexia syndrome: genetic and clinical insights

Purpose of review This review aims to summarise the present cerebellar ataxia, neuropathy, vestibular ataxia syndrome (CANVAS) literature, providing both clinical and genetic insights that might facilitate the timely clinical and genetic diagnosis of this disease. Recent findings Recent advancements in the range of the clinical features of CANVAS have aided the development of a broader, more well-defined clinical diagnostic criteria. Additionally, the identification of a biallelic repeat expansion in RFC1 as the cause of CANVAS and a common cause of late-onset ataxia has opened the door to the potential discovery of a pathogenic mechanism, which in turn, may lead to therapeutic advancements and improved patient care. Summary The developments in the clinical and genetic understanding of CANVAS will aid the correct and timely diagnosis of CANVAS, which continues to prove challenging within the clinic. The insights detailed within this review will raise the awareness of the phenotypic spectrum and currently known genetics. We also speculate on the future directions of research into CANVAS.
Survey on Blockchain Networking

Blockchains, in general, and cryptocurrencies such as Bitcoin, in particular, are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however, can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This article is motivated by these differences and, in particular, by the observation that many fundamental design aspects of these networks are not well-understood today. To support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery, over block and transaction propagation, to sharding and off-chain networks, also reviewing existing empirical results from different measurement studies. In particular, for each of these domains, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our article can hence also be seen as a call-to-arms to improve the foundation on top of which blockchains are built.
Synthesis of two AMPS-based polymerizable room temperature ionic liquids and swelling difference between their co-polymeric gels with HEMA

Abstract Two polymerizable room temperature ionic liquids (RTIL), AMPS-BA and AMPS-DMAEMA, were synthesized by neutralization of 2-acrylamido-2-methyl-1-propane sulfonic acid (AMPS) with butylamine (BA) and 2-(N,N-dimethylamino)ethyl methacrylate (DMAEMA), respectively, in acetone, followed by evaporation of the solvent under a reduced pressure at room temperature. The RTILs were characterized by differential scanning calorimetry to determine their glass transition temperatures (Tg). Co-polymeric gels of the RTILs with 2-hydroxyethylmethacrylate (HEMA) were prepared by aqueous solution polymerization using N,N′-methylenebisacrylamide (MBAm) as a cross-linker and ammonium persulfate as an initiator. The superabsorbency of the gels in water and various organic solvents was gravimetrically investigated. The results showed that the Tg of AMPS-BA and AMPS-DMAEMA was -47.7°C and -45.8°C, respectively. Poly(AMPS-BA-co-HEMA) gels exhibited superabsorbency in both water and various organic solvents, while poly(AMPS-DMAEMA-co-HEMA) gels did not swell in any liquids. The mechanism for the swelling difference between poly(AMPS-BA-co-HEMA) gels and poly(AMPS-DMAEMA-co-HEMA) gels was critically discussed.
Stochastic Optimal Energy Storage Management for Energy Routers Via Compressive Sensing

The functionality of energy routing among microgrids is becoming increasingly important with the progress of deploying smart power systems all over the world. For higher energy routing performance and better renewable energy integration, a new type of electrical device, called energy router (ER), is being developed as a part of the infrastructure of the future energy Internet (EI). Generally, the long-term operation of ERs requires an effective energy management scheme for the energy storage inside these devices. In this article, considering the randomness of power generation by renewable energy sources and the stochastic power usage of loads in EI scenario, the compressive sensing is adopted for the solution to the nonlinear energy storage management problem which is essential for the design of ERs. The compressive sensing method used in this article is proven to be more efficient than the conventional Monte–Carlo methods and polynomial chaos expansion method, and the performance of the proposed method is evaluated with numerical examples.
Performance Evaluation of Select Mutual Fund Schemes in India

Mutual funds are portfolios of diversified financial securities chosen by professional fund managers. These funds assure that the risk factor of their portfolios meets the risk preferences of the investors. The study aims to determine the performance of the direct growth plan scheme of different companies by using the Sharpe, Treynor, and Jensen methods. Tata Mutual Fund has the highest average return followed by Axis Mutual Fund, LIC Mutual Fund, and HSBC Mutual Fund. SBI Mutual Fund (12.54), L&T Mutual Fund (11.87), Aditya Birla Sun Life Mutual Fund (10.07), and India bulls Mutual Fund (9.63) all had higher positive Sharpe values among sample companies.
Evaluating the Use of Persuasive Design Cards for Novice Designers

This paper examines the use of physical persuasive cards for novice designers in ideation sessions. Through experimental study, we found that the tools a designer uses affects the kind of outcome they will get. The observations from four workshop sessions indicate that persuasive cards can be a two-edged sword, as they can affect the design process both positively and negatively. Additionally, in this paper, certain insights are highlighted when it came to how novice designers interacted with the cards. One of the most interesting behaviors witnessed was how participants were depending on the cards while debating their own ideas, and in some situations, neglecting their own or their colleagues' ideas as they believed the cards knew better. Moreover, this study was able to report “The Commonality Effect” as a new finding, as session outcomes from with-card teams showed a higher rate of repetitiveness and commonality in the persuasive ideas. This paper provides 10 design card heuristics that can be used as a guideline when it comes to producing and evaluating card-based tools.
Applying Deep Learning for Food Image Analysis

Food is an important component in people’s daily life, examples of the previous assertion are the range of possible diets according to the animal or vegetal origin, the intolerance to some aliments and lately the increasing number of food pictures in social networks. Several computer vision approaches have been proposed for tackling food analysis problems, but few eﬀort has been done in taking beneﬁt of the hierarchical relation between elements in a food image; dish and ingredients. In this project the highly performing state of the art CNN method is adapted concatenating an ontology layer, a multidimensional layer which contains the relation between the elements, in order to help during the classiﬁcation process. Diﬀerent structures for the ontology have been tested to prove which relations have the most beneﬁcial impact, and which are less relevant. Additionally to structure, the value of the elements that compound this hierarchical relation layer play an important role, therefore the experiments performed contained diﬀerent weighted relations between the components. The ontology layer is built with the labels of the multiple task in the dataset used to train the model. At the end, the results obtained will be compared to a baseline model without the ontology layer and it will be appreciated how hierarchical relations between tasks beneﬁts classiﬁcation. Finally, the result will be a model which will be able to simultaneously predict two food-related tasks; dish and ingredients.
Trajectory prediction of vehicles on highway

The topic of this thesis is trajectory prediction of the vehicles on a highway. The task is to predict a 5s future position of a target vehicle, given its current state and history. First, this problem is formally defined, and data on which the proposed models shall be evaluated are described. Subsequently, an overview of currently used techniques is presented, followed by three proposed approaches. The first approach is based on a direct prediction of the future position using Gradient Boosted Trees. The second is focused on improving an existing trajectory generating module in the Frenet frame of reference. The third one is taking inspiration from face recognition, describing the trajectory by Point Distribution Model, and inferring the coefficients of Eigenvectors obtained by a Principal Component Analysis. In the final chapter evaluation and comparison of the proposed methods concludes this thesis.
Different multipliers and a new architecture of multiplier and accumulator based on radix-2 MBA

This Paper consist of detailed review of five multiplier compared with respect to their parameters speed, area, power consumption and circuit complexity namely they are array multiplier, modified booth multiplier, Wallace multiplier and modified booth Wallace multiplier also in this project experimental analysis of Multiplier and Accumulator (MAC) with the help of modified booth Wallace multiplier is proposed to perform the arithmetic operation with high speed, the performance was elevated when two operation multiplication and accumulation was combined and devised with the carry save adder(CSA). Because of the accumulator propagation delay which is largest after merging CSA and MAC it results in improved performance. Radix-2 modified booth algorithm based on 1’s complement has been used in proposed CSA design the bit density of multiplier and multiplicand are going to increase by using the modified booth algorithm. To decrease the number of input bits, carries propagates through the LSB of partial products so has to generate the LSB in advance. Pipeline scheme has been introduced in the architecture proposed for MAC in order to accumulate the intermediate result in the form of sum and carry so that the performance of architecture get improved. While keeping the clock frequency same the proposed architecture is twice efficient as of previous design.
The Impact of Disease Activity on Quality of Life, Fatigue, Functional Status and Physical Activity in Patients with Ankylosing Spondylitis.

BACKGROUND Inflammatory back pain and stiffness are the leading symptoms of ankylosing spondylitis (AS). AS progression leads to substantial functional impairment and can reduce quality of life (QoL). The aim of this study was to determine the impact of disease activity on QoL, fatigue, functional status and physical activity. SUBJECTS AND METHODS One hundred and fifty AS patients were included in the study, their body mass index (BMI) was calculated and they completed questionnaires regarding disease activity (The Bath Ankylosing Spondylitis Disease Activity Index, BASDAI) functional status (The Bath Ankylosing Spondylitis Functional Index, BASFI) spinal mobility (The Bath Ankylosing Spondylitis Metrology Index, BASMI), physical activity (the International Physical Activity Questionnaire, IPAQ), functional disability (The Health Assessment Questionnaire Disability Index, HAQ-DI), fatigue (The Functional Assessment of Chronic Illness Therapy - fatigue, FACIT-F) and QoL (The Short Form Survey-36, SF-36). RESULTS Patients with inactive disease (BASDAI<4) had significantly better HAQ scores (p=0.001), SF-36 mental component scores - MCS (65.68±19.54 inactive vs. 46.89±21.78 active disease, p=0.001), SF-36 physical component scores - PCS (median score 56.25 inactive vs. 30.00 active disease, p=0.001) and FACIT-F scores (38.49±10.62 inactive vs. 26.21±10.81 active disease, p=0.001). There was no significant difference in patient's physical activity or BMI regarding disease activity (p=0.564 and p=0.162 respectively). Also, there was no significant difference in BASDAI, BASMI or BASFI scores regarding different BMI categories (p=0.818, p=0.474, p=0.436, respectively). CONCLUSION AS activity increased fatigue, impaired functional ability and QoL, especially the physical component. Although more than half (61.4%) of our patients were categorized as pre-obese or obese according to BMI, this was not related to disease activity, spinal mobility or daily functioning scores. Reported physical activity level had no effect on disease activity. Disease activity influences the course of AS and QoL assessment should be implemented into regular AS evaluation in order to improve treatment outcome.
Design an X-Band Frequency Synthesizer for Microsatellite Systems

This paper describe the design of an X-band frequency synthesizer that can be applied to microsatellite transmitter system. We discusse the effects of the phase noise in each component in the circuit (VCO, phase detctor, TCXO, dividers and loop filter) and the reference spurs on the noise performance system. The effect of the carrier recovery PLL in demodulation on the level of the single-sideband (SSB) phase noise is also included in the analysis. In the simulation, we consider the characteristics of commercially available components.
An end-to-end neural network for mobile phone detection in driving scenarios

Real-time detection of small objects is always a difficult problem in image processing. Based on the target detection algorithm of deep learning, this paper proposed an end-to-end neural network for mobile phone small target detection in complex driving scenarios. Firstly, an end-to-end small target detection network (OMPDNet) was designed to extract image features by improving the YOLOv4 algorithm. Secondly, based on the K-means algorithm, a K-means-Precise clustering algorithm of more appropriate data samples distribution in the clustering center was designed, which was used to generate prior frames suitable for small target data, so as to improve the efficiency of the network model. Finally, we constructed our own data set with supervision and weak supervision, and added negative samples to the data set for training. In the complex driving scene experiments, the OMPDNet algorithm —————————————————— 收稿日期:2020-09-02; 收到修改稿日期:2020-12-21 基金项目:安徽省 2018 年度重点研究与开发计划项目(1804a09020049) 作者简介:戴腾(1996-),男,硕士研究生,主要从事计算机视觉的研究。 E-mail:daiteng@mail.ustc.edu.cn 通信作者:尹东(1965-),男,副教授,主要从事图像处理的研究。 E-mail:yindong@ustc.edu.cn 版权所有○C 2021 中国科学院光电技术研究所 光电工程, 2021, 48(4): 200325 https://doi.org/10.12086/oee.2021. 200325 200325-2 proposed in this paper can not only effectively complete the detection task of using mobile phone while driving, but also has certain advantages over the current popular algorithms in accuracy and real-time for small target detection.