Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Learning for Fine-Grained Image Classification

Fine-grained image classification involves identifying different subcategories of a class which possess very subtle discriminatory features. Fine-grained datasets usually provide bounding box annotations along with class labels to aid the process of classification. However, building large scale datasets with such annotations is a mammoth task. Moreover, this extensive annotation is time-consuming and often requires expertise, which is a huge bottleneck in building large datasets. On the other hand, self-supervised learning (SSL) exploits the freely available data to generate supervisory signals which act as labels. The features learnt by performing some pretext tasks on huge unlabelled data proves to be very helpful for multiple downstream tasks. Our idea is to leverage self-supervision such that the model learns useful representations of fine-grained image classes. We experimented with 3 kinds of models: Jigsaw solving as pretext task, adversarial learning (SRGAN) and contrastive learning based (SimCLR) model. The learned features are used for downstream tasks such as fine-grained image classification. Our code is available at http://github.com/rush2406/Self-Supervised-Learning-for-Fine-grained-Image-Classification
Subwavelength Metawaveguide Filters and Metaports

This paper proposes a novel technique for the design of miniaturized waveguide filters based on locally resonant metamaterials (LRMs). We implement ultra-small metamaterial filters (Meta-filters) by exploiting a subwavelength (sub-l) guiding mechanism in evanescent hollow waveguides, which are loaded by small resonators. In particular, we use composite pin-pipe waveguides (CPPWs) built from a hollow metallic pipe loaded by a set of resonant pins, which are spaced by deep subwavelength distances. We demonstrate that in such structures, multiple resonant scattering nucleates a sub-l mode with a customizable bandwidth below the induced hybridization bandgap (HBG) of the LRM. The sub-l guided mode and the HBG, respectively, induce passand rejectionbands in a finite-length CPPW, creating a filter whose main properties are largely decoupled from the specific arrangement of the resonant inclusions. To guarantee compatibility with existing technologies, we propose a unique subwavelength method to match the small CPPW filters to standard waveguide interfaces, which we call a meta-port. Finally, we build and test a family of lowand high-order ultra-compact aluminum CPPW filters in the Ku-band (10-18GHz). Our measurements demonstrate the customizability of the bandwidth and the robustness of the passband against geometrical scaling. The 3D-printed prototypes, which are one order of magnitude smaller and lighter than traditional filters and are also compatible with standard waveguide interfaces, may find applications in future satellite systems and 5G infrastructures.
A P ] 2 8 Ju l 2 02 1 A NONSTANDARD GROWTH STEKLOV OPTIMIZATION PROBLEM WITH VOLUME CONSTRAINT

In this article we study an optimal design problem for a nonstandard growth Steklov eigenvalues ruled by the g−Laplacian operator. More precisely, given Ω ⊂ R and α, c > 0 we analyze existence and symmetry properties of solution of the optimization problem inf{λ(α,E) : E ⊂ Ω, |E| = c}, where, for a suitable function u(α,E), λ(α,E) solves { −div(g(|∇u|) ∇u |∇u| ) + (1 + αχE)g(|∇u|) ∇u |∇u| = 0 in Ω, g(|∇u|) ∇u |∇u| · η = λg(|u|) u |u| on ∂Ω being g the derivative of a Young function, and η the unit outward normal derivative. We analyze the behavior of the optimization problem as α approaches infinity and its connection of the trace embedding for Orlicz-Sobolev functions.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction

Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at https://github.com/lixiaojieff/HGTAN.
Methodology and Analysis of Smart Contracts in Blockchain-Based International Trade Application

Blockchain is used in a variety of applications where trustworthy com-puting is required. Trade finance is one of these areas that would benefit im-mensely from a decentralized way of doing transactions. This paper presents the preliminary assessment of Accepire-BT, a software platform developed for the practice of collaborative Trade Finance. The proposed solution is enforced by smart contracts using Solidity, the underlying programming language for the Ethereum blockchain. We evaluated the performance in the Rinkeby test network by using Remix and MetaMask. The results of the preliminary trial show that smart contracts take less than one minute per cycle. Also, we present a discussion about costs for using the public Ethereum Rinkeby network.
Lyapunov-based uncertainty-aware safe reinforcement learning

Reinforcement learning (RL) has shown a promising performance in learning optimal policies for a variety of sequential decision-making tasks. However, in many real-world RL problems, besides optimizing the main objectives, the agent is expected to satisfy a certain level of safety (e.g., avoiding collisions in autonomous driving). While RL problems are commonly formalized as Markov decision processes (MDPs), safety constraints are incorporated via constrained Markov decision processes (CMDPs). Although recent advances in safe RL have enabled learning safe policies in CMDPs, these safety requirements should be satisfied during both training and in the deployment process. Furthermore, it is shown that in memorybased and partially observable environments, these methods fail to maintain safety over unseen out-of-distribution observations. To address these limitations, we propose a Lyapunov-based uncertainty-aware safe RL model. The introduced model adopts a Lyapunov function that converts trajectory-based constraints to a set of local linear constraints. Furthermore, to ensure the safety of the agent in highly uncertain environments, an uncertainty quantification method is developed that enables identifying riskaverse actions through estimating the probability of constraint violations. Moreover, a Transformers model is integrated to provide the agent with memory to process long time horizons of information via the self-attention mechanism. The proposed model is evaluated in grid-world navigation tasks where safety is defined as avoiding static and dynamic obstacles in fully and partially observable environments. The results of these experiments show a significant improvement in the performance of the agent both in achieving optimality and satisfying safety constraints.
Learning User-Interpretable Descriptions of Black-Box AI System Capabilities

Several approaches have been developed to answer speciﬁc questions that a user may have about an AI system that can plan and act. However, the problems of identifying which questions to ask and that of computing a user-interpretable symbolic description of the overall capabilities of the sys-tem have remained largely unaddressed. This paper presents an approach for addressing these problems by learning user-interpretable symbolic descriptions of the limits and capabilities of a black-box AI system using low-level simulators. It uses a hierarchical active querying paradigm to generate questions and to learn a user-interpretable model of the AI system based on its responses. In contrast to prior work, we consider settings where imprecision of the user’s conceptual vocabulary precludes a direct expression of the agent’s capabilities. Furthermore, our approach does not require assumptions about the internal design of the target AI system or about the methods that it may use to compute or learn task solutions. Empirical evaluation on several game-based simulator domains shows that this approach can efﬁciently learn symbolic models of AI systems that use a deterministic black-box policy in fully observable scenarios.
Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes

Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. The majority of Re-ID methods focus on small-scale surveillance systems in which each pedestrian is captured in different camera views of adjacent scenes. However, in large-scale surveillance systems that cover larger areas, it is required to track a pedestrian of interest across distant scenes (e.g., a criminal suspect escapes from one city to another). Since most pedestrians appear in limited local areas, it is difficult to collect training data with cross-camera pairs of the same person. In this work, we study intra-camera supervised person re-identification across distant scenes (ICS-DS Re-ID), which uses cross-camera unpaired data with intra-camera identity labels for training. It is challenging as cross-camera paired data plays a crucial role for learning camera-invariant features in most existing Re-ID methods. To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs. Furthermore, we automatically localize and extract local-level feature by a transformer. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme for mining fine-grained cross-camera self supervision information. Finally, cross-camera self supervision and intra-camera supervision are aggregated in a framework. The experiments are conducted in the ICS-DS setting on Market-SCT, Duke-SCT and MSMT17-SCT datasets. The evaluation results demonstrate the superiority of our method, which gains significant improvements of 15.4 Rank-1 and 22.3 mAP on Market-SCT as compared to the second best method. Our code is available at https://github.com/g3956/CCFP.
Significance of Speaker Embeddings and Temporal Context for Depression Detection

Depression detection from speech has attracted a lot of attention in recent years. However, the significance of speaker-specific information in depression detection has not yet been explored. In this work, we analyze the significance of speaker embeddings for the task of depression detection from speech. Experimental results show that the speaker embeddings provide important cues to achieve state-of-the-art performance in depression detection. We also show that combining conventional OpenSMILE and COVAREP features, which carry complementary information, with speaker embeddings further improves the depression detection performance. The significance of temporal context in the training of deep learning models for depression detection is also analyzed in this paper.
Towards a Survey on Static and Dynamic Hypergraph Visualizations

Leveraging hypergraph structures to model advanced processes has gained much attention over the last few years in many areas, ranging from protein-interaction in computational biology to image retrieval using machine learning. Hypergraph models can provide a more accurate representation of the underlying processes while reducing the overall number of links compared to regular representations. However, interactive visualization methods for hypergraphs and hypergraph-based models have rarely been explored or systematically analyzed. This paper reviews the existing research landscape for hypergraph and hypergraph model visualizations and assesses the currently employed techniques. We provide an overview and a categorization of proposed approaches, focusing on performance, scalability, interaction support, successful evaluation, and the ability to represent different underlying data structures, including a recent demand for a temporal representation of interaction networks and their improvements beyond graph-based methods. Lastly, we discuss the strengths and weaknesses of the approaches and give an insight into the future challenges arising in this emerging research field.
Guided Disentanglement in Generative Networks

Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), thus lowering the translation quality and variability. In this paper, we present a comprehensive method for disentangling physics-based traits in the translation, guiding the learning process with neural or physical models. For the latter, we integrate adversarial estimation and genetic algorithms to correctly achieve disentanglement. The results show our approach dramatically increase performances in many challenging scenarios for image translation.
Exploiting GISAXS for the Study of a 3D Ordered Superlattice of Self-Assembled Colloidal Iron Oxide Nanocrystals

A three-dimensional (3D) ordered superlattice of colloidal iron oxide nanocrystals obtained by magnetic-field-assisted self-assembly has been studied by grazing incidence small-angle X-ray scattering (GISAXS). A new model to simulate and interpret GISAXS patterns is presented, which returns the structural and morphological details of 3D nanocrystal-built supercrystals. The model is applied to a sample with a suitable surface morphology, allowing the observation of “volume diffraction” even at extremely low grazing incidence angle. In this particular case, the average fcc-like stacking of the nanocrystals (building blocks), their spherical shape, and statistical information on their size distribution and positions within the superlattice have been safely deduced. The proposed model is expected to be amendable for the analysis of more complex structures and applicable to a large variety of nanocrystal-based assemblies.
Synthesis of 1,4-dipalmitoyl hydroxyproline

1,4-dipalmitoyl hydroxyroline,a skin anti-aging agent used in cosmetics was synthesized from L-hydroxyproline and palmitoyl chloride. The influence factors,such as molar ratio of raw materials,the reaction time,temperature and media of reaction were studied. The appropriate reaction conditions were as follows:volume ratio of acetone 50%,molar ratio of L-hydroxyproline to palmitoyl chloride 1∶2.5,pH 9-10,reaction temperature 15 ℃,reaction time 4 h. After separation,a white solid powder with melting point of 81-85 ℃ was obtained.
THE INFLUENCE OF THICKNESS ON THE OPTICAL PARAMETERS OF THERMALLY EVAPORATED PURE AND SILVER-DOPED CADMIUM SELENIDE THIN FILMS

Silver doped CdSe thin films has been prepared by thermal evaporation in different proportions (0, 3, 5, and 10) wt. % . Pure cadmium selenide (CdSe) films sediment with different thicknesses (200,218,225,254) nm on glass substrates at room temperature. The results of the measurements through the studying of the optical properties showed that the absorbance of the (CdSe) films increase within the thickness of the film and the distortion ratios for the wavelengths located within the visible region of the electromagnetic spectrum. It also showed that the absorption coefficient of all the prepared films was a value greater than (α >104 cm-1) and that this value increases in general with increasing thickness and distortion ratios, especially at low photonic energies. the results showed as for the value of the optical energy gap, that the films have (CdSe) all direct optical energy gap and its values decrease from (1.76 eV) to (1.73 eV) with increasing thickness. As for the defective films, their value decreases from (1.73 eV) to (1.66 eV) with an increase in the doping percentage.
ADVANCES IN TECHNOLOGY-BASED EDUCATION: TOWARDS A KNOWLEDGE BASED SOCIETY

The Next Generation Intelligent Tutoring Systems (NGITS) project is a research project aimed at developing a new generation of human-like intelligent multimedia systems that are capable of individualizing instruction. We hope to achieve a level of individualization that is comparable to one-on-one human tutoring. To be able to do this, these systems should be capable of modeling not only the knowledge state of the student but also their emotional state. Current e-learning course material is not very adaptive and is far less effective than one-on-one tutoring. The emotional state of the learner is estimated through the analysis of various bio-signals such as heartbeat, skin resistance, voice intonations and facial expressions. This information can be used to improve the performance of the tutoring system. This paper reports a real-time implementation of a facial expression analysis system using FPGA technology. Initial results indicate performance improvement in comparison with the software implementation.
Long non-coding RNA SNHG16 contributes to progression of carotid atherosclerosis by regulating miR-30c-5p/ADAM10 axis

Introduction Carotid atherosclerosis (CAS) is one of the main causes of cerebral infarction in the ageing population. Long non-coding RNA small nucleolar RNA host gene 16 (lnc-SNHG16) could promote the development of atherosclerosis. However, the mechanism of lnc-SNHG16 in CAS remains vague. Material and methods The expression levels of lnc-SNHG16, microRNA-30c-5p (miR-30c-5p) and disintegrin and metalloproteinase 10 (ADAM10) were detected by real-time quantitative polymerase chain reaction (RT-qPCR). Cell viability and migration were detected by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT) and transwell assays, severally. The levels of interleukin-6 (IL-6), IL-β and tumor necrosis factor-α (TNF-α) were assessed by enzyme-linked immunosorbent assay (ELISA). Protein levels of spinal muscular atrophy (SMA), calponin and ADAM10 were examined by western blot assay. The binding relationship between miR-30c-5p and lnc-SNHG16 or ADAM10 was predicted by Starbase, then verified by the dual-luciferase reporter assay. Results Lnc-SNHG16 and ADAM10 were increased, and miR-30c-5p was decreased in CAS patient and oxidized low-density lipoprotein (ox-LDL)-treated human aortic smooth muscle cells (hASMCs). LncSNHG16 silencing repressed cell viability, migration, inflammation, facilitated differentiation in ox-LDLtreated hASMCs. Moreover, mechanical analysis proved that lnc-SNHG16 improved ADAM10 expression by sponging miR-30c-5p. Conclusions Our data indicated that lnc-SNHG16 could regulate the progression of ox-LDL induced CAS model by the miR-30c-5p/ADAM10 axis, implying a potential therapeutic strategy for CAS Powered by TCPDF (www.tcpdf.org) Pr ep ri t Long non-coding RNA SNHG16 contributes to progression of carotid 1 atherosclerosis by regulating miR-30c-5p/ADAM10 axis 2
Controllable Real-Time Style Transfer

We explored in this project how to train a real time controllable style transfer network. In particular, we implemented a style transfer neural network with two controllable input parameter: degree of style transfer and multiple style targets. The main idea is concatenating the parameters to the input image and adjusting the loss function accordingly so that the network is correctly optimized for different input parameters. The system we have implemented is able to transfer image to multiple styles in different degrees in a user controllable way.
Thai Pregnant Mobile Application: Review and Development Report

Since health-related information for pregnant women is extremely complex and sensitive, end-users of any application are required to have the same level of knowledge as the information providers to help them to navigate between various sources of information to ascertain the reliability of contents that are often confusing. However, although there are several such applications for Thai pregnant women in the market, few were developed based on research evidence. This study is focused on the design of a health application specifically for Thai pregnant women by extracting the explicit problems of existing applications and providing solutions to them. A design thinking methodology comprised of empathise, define, ideate, prototype and test was used as a guideline to develop a prototype app. IT experts, physical therapists and pregnant women were recruited as participants to address the problem taxonomy and obtain a solution.The new application produced in this study with the aim of encouraging pregnant women especially for Thai women to engage in physical activity was based on research, and the input of physical therapists, IT experts and pregnant women users was able to feasibly inform the development of an acceptable and usable mobile health application for pregnant women.
for nitrate removal from aqueous solution

Groundwater contaminated by nitrate has become a worldwide problem. Existing technologies for nitrate removal, including zero-valent iron reduction, can only transform nitrate ions to predominantly NH þ 4 . A s a result, many studies have concentrated on seeking a solution to convert nitrate to N2. In this study, the efficiencies of nitrate removal from aqueous solution by single (TiO2 and nano zero valent iron, nZVI), and composite (Nano-TiO2–Fe 0
Stability Analysis of Underground Pillar Supporting System under Different Disturbed Stresses

Small external disturbances may destabilize the bearing pillar, which in turn will change the stress distribution of the pillar supporting system, leading to its overall instability. Based on the engineering background of a mine and the mechanical analysis of pillar under disturbed stress, this paper investigated the stress, strain, and plastic zone of the pillar supporting system under different disturbed stresses. Then, the chain instability of the pillar supporting system was achieved. The law of stress transfer and plastic development of the pillar supporting system was explored. The results showed that the greater the disturbed stress, the faster the increase rate of the maximum stress of the pillar supporting system. As the width of the pillar increased, the maximum stress of the pillar decreased, so its risk of damage decreased. As the disturbed stress increased, the maximum principal strain and equivalent plastic strain of the 6m wide pillar increased approximately linearly, and their growth rates of the 4m wide pillar gradually increased. In the process of chain instability of the pillar supporting system, the sides of the middle pillar were destroyed first, and then the plastic zone gradually penetrated, causing the stress of the adjacent pillar to increase, which in turn led to its destruction. By analyzing the monitoring data of stress, displacement, or plastic strain, the instability of the pillar can be predicted.