Miracle-dz/UniFAR-FTUs huggingface dataset:
MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition

In this paper, we address self-supervised representation learning from human skeletons for action recognition. Previous methods, which usually learn feature presentations from a single reconstruction task, may come across the overfitting problem, and the features are not generalizable for action recognition. Instead, we propose to integrate multiple tasks to learn more general representations in a self-supervised manner. To realize this goal, we integrate motion prediction, jigsaw puzzle recognition, and contrastive learning to learn skeleton features from different aspects. Skeleton dynamics can be modeled through motion prediction by predicting the future sequence. And temporal patterns, which are critical for action recognition, are learned through solving jigsaw puzzles. We further regularize the feature space by contrastive learning. Besides, we explore different training strategies to utilize the knowledge from self-supervised tasks for action recognition. We evaluate our multi-task self-supervised learning approach with action classifiers trained under different configurations, including unsupervised, semi-supervised and fully-supervised settings. Our experiments on the NW-UCLA, NTU RGB+D, and PKUMMD datasets show remarkable performance for action recognition, demonstrating the superiority of our method in learning more discriminative and general features. Our project website is available at https://langlandslin.github. io/projects/MSL/.
AI at the Disco: Low Sample Frequency Human Activity Recognition for Night Club Experiences

Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading---embedding accelerometer data in the Bluetooth Low Energy broadcast packet---which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club.
Low-level Optimizations for Faster Mobile Deep Learning Inference Frameworks

Over the last ten years, we have seen a strong progression of technology around smartphones. Each new generation acquires capabilities that significantly increase performance. On the other hand, several deep learning tools are offered today by the giants of the net for mobile, embedded devices and IoT. The proposed libraries allow a machine learning inference on the device with low latency. They provide pre-trained models, but one can also use one's own models and run them on mobile, embedded or microcontroller devices. Lack of privacy, poor Internet connectivity and high cost of cloud platform let on-device inference became popular through app developers but there are more significant challenges especially for real-time tasks like augmented reality or autonomous driving. This PhD research aims at providing a path for developers to help them choose the best methods and tools to do real-time inference on mobile devices. In this paper, we present the performance benchmark of four popular open-source deep learning inference frameworks used on mobile devices on three different convolutional neural network models. We focus our work on image classification process and particularly on validation image bank of ImageNet 2012 dataset. We try to answer three questions : How does a framework influence model prediction and latency - Why some frameworks are better in terms of latency/accuracy than others with the same model - And what are the difficulties to implement these frameworks inside a mobile application - Our first findings demonstrate that low-level software implementations chosen in frameworks, model conversion steps and parameters set in the framework have a big impact on performance and accuracy.
Multimodal Multi-Task Financial Risk Forecasting

Stock price movement and volatility prediction aim to predict stocks' future trends to help investors make sound investment decisions and model financial risk. Companies' earnings calls are a rich, underexplored source of multimodal information for financial forecasting. However, existing fintech solutions are not optimized towards harnessing the interplay between the multimodal verbal and vocal cues in earnings calls. In this work, we present a multi-task solution that utilizes domain specialized textual features and audio attentive alignment for predictive financial risk and price modeling. Our method advances existing solutions in two aspects: 1) tailoring a deep multimodal text-audio attention model, 2) optimizing volatility, and price movement prediction in a multi-task ensemble formulation. Through quantitative and qualitative analyses, we show the effectiveness of our deep multimodal approach.
Multi-Group Multi-Attention: Towards Discriminative Spatiotemporal Representation

Learning spatiotemporal features is very effective but challenging for video understanding especially action recognition. In this paper, we propose Multi-Group Multi-Attention, dubbed MGMA, paying more attention to "where and when" the action happens, for learning discriminative spatiotemporal representation in videos. The contribution of MGMA is three-fold: First, by devising a new spatiotemporal separable attention mechanism, it can learn temporal attention and spatial attention separately for fine-grained spatiotemporal representation. Second, through designing a novel multi-group structure, it can capture multi-attention rendered spatiotemporal features better. Finally, our MGMA module is lightweight and flexible yet effective, so that can be easily embedded into any 3D Convolutional Neural Network (3D-CNN) architecture. We embed multiple MGMA modules into 3D-CNN to train an end-to-end, RGB-only model and evaluate on four popular benchmarks: UCF101 and HMDB51, Something-Something V1 and V2. Ablation study and experimental comparison demonstrate the strength of our MGMA, which achieves superior performance compared to state-of-the-arts. Our code is available at https://github.com/zhenglab/mgma.
A Smart-Site-Survey System using Image-based 3D Metric Reconstruction and Interactive Panorama Visualization

This work presents a so-called Smart Site Survey (SSS) system that provides an efficient, web-based platform for virtual inspection of remote sites with absolute 3D metrics. Traditional manual surveying requires sending surveyors and specialised measuring tools to the targeted scene, which takes time and requires significant human resource, and often includes human error. The proposed system provides an automated site survey tool. Sample indoor scenes including offices, storage rooms, and laboratory are used for testing purposes, and highly precise virtual scenes are restored, with the measurement accuracy of 1%, i.e. an error ±1.5cm to a 150cm length. This is comparable or superior to existing works or commercial products.
InteractGAN: Learning to Generate Human-Object Interaction

Compared with the widely studied Human-Object Interaction DE-Tection (HOI-DET), no effort has been devoted to its inverse problem, i.e. to generate an HOI scene image according to the given relationship triplet , to our best knowledge. We term this new task "Human-Object Interaction Image Generation" (HOI-IG). HOI-IG is a research-worthy task with great application prospects, such as online shopping, film production and interactive entertainment. In this work, we introduce an Interact-GAN to solve this challenging task. Our method is composed of two stages: (1) manipulating the posture of a given human image conditioned on a predicate. (2) merging the transformed human image and object image to one realistic scene image while satisfying the ir expected relative position and ratio. Besides, to address the large spatial misalignment issue caused by fusing two images content with reasonable spatial layout, we propose a Relation-based Spatial Transformer Network (RSTN) to adaptively process the images conditioned on their interaction. Extensive experiments on two challenging datasets demonstrate the effectiveness and superiority of our approach. We advocate for the image generation community to draw more attention to the new Human-Object Interaction Image Generation problem. To facilitate future research, our project will be released at: http://colalab.org/projects/InteractGAN.
ConfFlow: A Tool to Encourage New Diverse Collaborations

ConfFlow is an interactive web application that allows conference participants to inspect other attendees through a visualized similarity space. The construction of the similarity space is done in a similar manner to the well-known Toronto Paper Matching System (TPMS) and based on the publicly available former publications of the attendees, obtained by crawling through the Web. ConfFlow aims to help attendees initiate new connections and collaborations with participants that have similar and/or complementary research interests. It has multiple functionalities that allow users to customize their experience and identify the perfect connection for their next collaboration.
Improving STING Agonist Delivery for Cancer Immunotherapy Using Biodegradable Mesoporous Silica Nanoparticles

Stimulator of interferon genes (STING) activation by intratumoral STING agonist treatment has been recently shown to eradicate tumors in preclinical models of cancer immunotherapy, generating intense research interest and leading to multiple clinical trials. However, there are many challenges associated with STING agonist‐based cancer immunotherapy, including low cellular uptake of STING agonists. Here, biodegradable mesoporous silica nanoparticles (bMSN) with an average size of 80 nm are developed for efficient cellular delivery of STING agonists. STING agonists delivered via bMSN potently activate innate and adaptive immune cells, leading to strong antitumor efficacy and prolonged animal survival in murine models of melanoma. Delivery of immunotherapeutic agents via biodegradable bMSN is a promising approach for improving cancer immunotherapy.
Comparative studies on the molecular genetic diversities between the Zhoushan wild Miichthys miiuy and their early filial generation by AFLP markers.

Genetic diversity of a wild population and an artificial breeding population of Miichthys miiuy was investigated with amplified fragment length polymorphism(AFLP). The wild population was caught with cast net in the East China Sea near Zhoushan,and the cultured population was artificial breeding offspring of the wild population which was the first filial generation(F1). Eight EcoR I and Mse I primers combination produced 505 clear bands,of which 369 bands were polymorphic loci.The overall proportion of polymorphic loci was 73.07%. The two groups of parental and offspring generation's polymorphic loci,percentage of polymorphic loci,Nei genetic diversity index and Shannon information diversity index were 367,349,72.51%,68.86%,0.422 2,0.413 9 respectively.The coefficients of genetic similarity of the parental and the offspring generation were 0.987 6,and 0.971 5.Miichthys miiuy parent's population,Nei index of genetic diversity,genetic similarity and the Shannon information diversity index were higher than those of the offspring population.The genetic similarity coefficient and genetic distances between two populations were 0.979 6,and 0.020 6.Gst coefficient of gene differentiation was 0.022 3.There was no significant genetic differentiation between two populations.The analysis of molecular variance(AMOVA) results showed 97.78% of the genetic variation among individuals within groups.There was no significant genetic variation between two populations.
Eye fixations and electrodermal activity during low‐budget virtual reality embodiment

As motion‐sensing devices become more accessible to consumers, it is important to understand how users experience embodiment when using these devices. In our 3 × 2 between‐groups study, we explored eye fixations and electrodermal activity (EDA) in order to more objectively understand potential interaction effects between the self‐avatar body, and the presence of a mirror within the context of low‐budget embodiment. We developed six experimental conditions concerning Body (human, mannequin, and zombie self‐avatars) and Mirror (mirror and no mirror) factors, and presented participants with a virtual environment in which they could control their self‐avatars by using HTC Vive controllers and trackers. In addition to eye fixations and EDA, we assessed self‐reported data concerning body ownership, agency, self‐location, as well as enjoyment of the experience. Our results suggest that the Body may have been more influential in eliciting body ownership than the Mirror, and that an interaction effect between Gender and Body may influence eye gaze behavior. Additionally, female participants reported significantly higher agency than males. We consider logical next steps for similar research which might elaborate upon our findings.
Embedding 3D models in offline physical environments

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space‐efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user‐friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low‐cost autonomous robots, and 3D printing.
Fault-tolerant with load balancing scheduling in a fog-based IoT application

Fog computing (FC) with a distributed architecture plays an essential role in Internet-of-Things (IoT). This paradigm utilises the processing abilities of Fog devices (FDs) and decreases latency. The large volume of data and its process in IoT can cause network failures. Researchers tend to consider communication reliability to reduce fault effects and achieve high performance. Fault tolerance becomes a necessary matter to enhance the reliability of the Fog. Notably, fault tolerance studies have been performed mostly on the Cloud system. To counter this issue, the authors propose a novel fault-tolerant scheduling algorithm of modules in FC and optimise it. The main idea of this approach is a classification method for different modules alongside of computing the energy consumption of all FDs and finding minimal FDs' energy consumption. To distribute modules between FDs, they present an energy-efficient checkpointing and load balancing technique based on the Bayesian classification and call it by ECLB. The performance of the proposed method is evaluated by comparing it with the state-of-the-art algorithms in terms of delay, energy consumption, execution cost, network usage, and total executed modules. Analysis and simulation results indicate that the authors' methods are efficient and superior to others.
Isolation of reovirus SC-A strain from fecal of diarrhea pigs and sequence analysis of σ2 gene

Using Vero cell line and by the method of adding the trypsin into the viral culture MEM,reovirus SC-A strain was isolated form the pigs' diarrhea fecal.The stable CPE characters of the reovirus,including cell granulations increasing,cellular swelling and shedding were observed.In the cytoplasm of infected cell,the virion of diameter approximately 70 nm,shows typical lattice arrangement,the cytoplasmic inclusion bodies and the distinctive microbule-like structure.The S2 gene(DQ396805)of reovirus SC-A,obtained by RT-PCR,is 1 331 bp in length and encodes the σ2 protein,whose relative molecular weight is 47 140,with an open reading frame of 1 257 nucleotides(including the termination codon). The sequence analysis indicates the reoviurs SC-A strain' sequence similarity of nucleotide sequence and deduced amino acid sequence are 86.9%,76.7%,85.4% and 94.6%,93.3%,98.3% with standard strains T1L,T2J,T3D,respectively.Phylogenetic trees based on σ2 shows standard strains and field isolate strains were divided into A(serotype 3),B(serotype 2),C(serotype 1) tree groups.Excepted T3C31 field isolate strain,serotype 3 of the other field isolate strains,SA-C strain,all of serotype 1 strains are located in C group(serotype 1).
Inhibitory effects of salidroside on hypoxia induced proliferation of rabbit pulmonary arterial smooth muscle cells

AIM To investigate the effects of salidroside on the proliferation, the DNA synthesis and cell cycle of rabbit pulmonary arterial smooth muscle cell(PASMC)under hypoxia and to study its mechanism. METHODS Techniques of cell culture, MTT test, 3H TdR incorporation and flow cytometric DNA analysis were used. RESULTS PASMC 3H TdR incorporation and A value of MTT test increased significantly in response to 24 hour hypoxia culture, and verapamil (80 μmol·L -1 ), a Ca 2+ channel bloker, could inhibit the effect induced by hypoxia. When salidroside (0.2, 0.8, 3.2×10 μmol·L -1 ) were added into the medium, 3H TdR incorporation and OD value of MTT decreased significantly as compared with hypoxia group. Flow cytometric DNA analysis revealed that hypoxia significantly enhanced G2/M phase of PASMC and decreased G 0/G 1 phase of PASMC ( P 0.01). Salidroside (6.4 μmol·L -1 ) could reverse this effect ( P 0 05). But no further inhibitory effects were observed by co incubation of verapamil (80 μmol/l) and sal (3.2 μmol·L -1 ). CONCLUSION The results reveal that hypoxia can directly stimulate the proliferation and DNA synthesis, and its mechanism may be associated with the increase of intracellular [Ca 2+ ]. Salidroside can inhibit the enhancing effects of hypoxia on the proliferation and DNA synthesis of PASMC.
P2 KASE A2 - privacy-preserving key aggregate searchable encryption supporting authentication and access control on multi-delegation

Delegation is a technique that allows a subject receiving a delegation (the delegatee) to act on behalf of the delegating subject (the delegator). Although the existing Key Aggregate Searchable Encryption (KASE) schemes support delegation of search rights over any set of ciphertexts using a key of constant-size, two critical issues still should be considered. Firstly, an adversary can intercept the aggregate key or query trapdoor from the insecure communication channels involving the cloud server and impersonate as an authorized user to the server for accessing the data. Secondly, the existing KASE schemes only discuss the delegation of rights from the data owner to other users. However, if a subject receiving a delegation cannot perform the time-critical task on the shared data because of the unavailability, it becomes necessary for the delegatee to further delegate his received rights to another user. In this paper, we propose a novel KASE scheme that allows a fine-grained multi-delegation, i.e., if the attributes of the delegatee satisfy the hidden access policy (defined by the data owner), the delegatee can delegate his received rights to another user, without compromising data privacy. The proposed scheme provides security against the impersonation attack by verifying the user's authentication.
Performance analysis of two-way AF relaying system with the presence of hardware impairments over Nakagami-m fading channels

The authors evaluate the outage probability, ergodic capacity, and symbol error rate (SER) performances of an amplify-and-forward relaying system equipped with a non-ideal variable gain single relay with hardware (HW) distortions at both the transmitter and receiver sides with the derivations of the instantaneous end-to-end signal-to-noise-and-distortion ratios. More specifically, they analytically derive new algebraic expressions for the outage probability and ergodic capacity in the two-way relaying system over the Nakagami-m fading environments. They further derive new closed-form expressions for the asymptotic outage probability to analyse the performance in the high signal-to-noise regime. The derived statistics, along with the transmitter–relay–receiver channels are then used to obtain a closed-form expression of the approximate and upper bound ergodic capacity. Finally, they investigate the generic mathematical expressions utilising the average and asymptotic SER, valid for several types of modulation schemes operating over the Nakagami-m fading. Monte Carlo simulations and numerical examples are provided to quantify the validity of the derived analytical expressions. The authors' results highlight the effect of HW impairments in the two-way relaying system over the Nakagami-m fading channels and reveal the impact of the level of HW impairments, data rates, modulation types, and shaping parameters on the overall system performance.
Multiplex PCR-based detection of Salmonella typhimurium and Salmonellaenteritidis in Specific Pathogen Free (SPF) and Commercial Eggs

Salmonella serovars are one of the major bacterial causes of food borne diseases. Eggs are commonly identified as food sources responsible for salmonellosis outbreaks. This study aimed to isolate Salmonella typhimurium and Salmonella enteritidis from 1750 hens’ eggs, and use of multiplex polymerase chain reaction (Multiplex PCR) in the identification of different Salmonella serovars from eggs. The incidence of salmonellae among the Balady eggs yolk was 1.3%, while the incidence was 1.2% among white and brown eggs samples (each). S. typhimurium and S. enteritidis were identified (0.6 and 0.5% respectively). The isolates were confirmed using fliC, sefA genes and gene specific for genus Salmonella. All albumen samples negative for isolation of isolation of salmonellae by culture method were retested by PCR. From the retested albumen samples 3%, 8.4% and 6% collected from balady, white and brown eggs respectively were positive for Salmonella serovars using Multiplex PCR. No salmonellae could be detected from specific pathogen free (SPF) eggs using both PCR and conventional methods.
Effects of dietary lysine and methionine supplementation on Ross 708 male broilers from 21 to 42 days of age (II): breast meat quality

SUMMARY In a previous companion study, we found that broiler growth performance and meat yield were improved by the feeding of increased levels of dietary lysine (Lys) and methionine (Met). In the current study, dietary Lys and Met supplementation were evaluated for their effect on the breast (pectoralis major) meat quality of male Ross × Ross 708 broilers. A completely randomized block design with a 2 × 4 factorial arrangement of treatments was applied (10 blocks and 8 treatments/block). The diets, including 2 Lys levels (1.013 and 1.216%) and 4 Met levels (0.304, 0.380, 0.456, and 0.532%), were fed from 21 to 42 d of age in the form of pellets, and broilers were processed at 42 d of age. Data were analyzed by 2-way ANOVA using the MIXED procedures of SAS 9.2. Lysine supplementation yielded breast meat with increased sarcoplasmic protein (soluble protein) solubility (P = 0.007) and pH (less protein denaturation; P