Miracle-dz/UniFAR-FTUs huggingface dataset:
Classifier Two Sample Test for Video Anomaly Detections

In this paper, we study challenging anomaly detections in streaming videos under fully unsupervised settings. Unsupervised unmasking methods [12] have recently been applied to anomaly detection; however, the theoretical understanding of it is still limited. Aiming to understand and improve this method, we propose a novel perspective to establish the connection between the heuristic unmasking procedure and multiple classiﬁer two sample tests (MC2ST) in statistical machine leaning. Based on our analysis of the testing power of MC2ST, we present a history sampling method to increase the testing power as well as to improve the performance on video anomaly detection. We also offer a new frame-level motion feature that has better representation and generalization ability, and obtain improvement on several video benchmark datasets. The code could be found at https://github.com/MYusha/Video-Anomaly-Detection.
Ranking Using Transition Probabilities Learned from Multi-Attribute Data

In this paper, as a novel approach, we learn Markov chain transition probabilities for ranking of multi -attribute data from the inherent structures in the data itself. The procedure is inspired by consensus clustering and exploits a suitable form of the PageRank algorithm. This is very much in the spirit of the original PageRank utilizing the hyperlink structure to learn such probabilities. As opposed to existing approaches for ranking multi -attribute data, our method is not dependent on tuning of critical user-specified parameters. Experiments show the benefits of the proposed method.
Adaptive Multi-Scale Information Flow for Object Detection

Recent CNN-based research reveals that the multi-scale information plays an important role in boosting the performance of object detection. There are several network structures proposed to explore an effective multi-scale feature representation. In these structures, the allocation of information in multi-scale representation has a bias toward very few layers. In this paper, we present a novel module named Adaptive Multi-Scale Information Flow (ASIF) to break the bias and ﬁnd the proper multi-scale representation for each layer. In ASIF, information from different layers in the feature pyramid is weighted and aggregated. The allocation of information is adaptive from each layer to other layers. To ensure both speed and accuracy at the same time, we follow the SSD detection framework and apply ASIF to it. We evaluate the performance of proposed method on PASCAL VOC and MSCOCO datasets. Experiments show that ASIF is superior to many state-of-the-art methods. Given the image size of 320 × 320, the mAP could reach 80.2% (45 FPS) on PASCAL VOC 2007 test, and 29.3% on COCO test-dev2015.
Autonomous Feature Tracing and Adaptive Sampling in Real-World Underwater Environments

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sensors for underwater environmental sensing. We have also developed a compact high resolution fast temperature sensing module for the AUV for microstructure and turbulence measurements in water bodies. In this paper, we describe a number of algorithms and subsystems of the AUV that enable autonomous real-world operation, and present the data gathered in an experimental campaign in collaboration with limnologists. We demonstrate adaptive sampling missions where the AUV could autonomously locate a zone of interest and adapt its trajectory to stay in it. Further, it could execute specific behaviors to accommodate special sensing requirements necessary to enhance the quality of the data collected. In these missions, the AUV could autonomously trace a feature and capture horizontal variation in various quantities, including turbidity and temperature fluctuations, allowing limnologists to study lake phenomena in an additional dimension.
Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems

A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting inverse power method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-power method with super linear convergence, the self-adjusting inverse power method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
Generalised Discriminative Transform via Curriculum Learning for Speaker Recognition

In this paper we introduce a speaker verification system deployed on mobile devices that can be used to personalise a keyword spotter. We describe a baseline DNN system that maps an utterance to a speaker embedding, which is used to measure speaker differences via cosine similarity. We then introduce an architectural modification which uses an LSTM system where the parameters are optimised via a curriculum learning procedure to reduce the detection error and improve its generalisability across various conditions. Experiments on our internal datasets show that the proposed approach outperforms the DNN baseline system and yields a relative EER reduction of 30-70% on both text-dependent and text-independent tasks under a variety of acoustic conditions.
A Family of Matrices for Generating Hermite-Gaussian-Like DFT Eigenvectors

A generating matrix is a matrix such that, when multiplied by an eigenvector of a discrete transform, a new eigenvector is obtained. In this paper, we introduce a family of generating matrices of DFT eigenvectors. We demonstrate that, if a specific initial set of eigenvectors is chosen, using the referred family of matrices, a Hermite-Gaussian-like DFT eigenbasis is obtained. Such an eigenbasis is then employed to define a discrete fractional Fourier transform which numerically approximates the corresponding continuous transform.
Relationship of epicardial fat tissue thickness with oxidant biomarkers in chronic kidney disease.

INTRODUCTION AND OBJECTIVE IMA and MPO are elevated in serum in case of end-stage renal disease. Epicardial fat tissue thickness has been considered an indicator for cardiovascular diseases recently. The present study was aimed to examine the relationship of epicardial fat tissue thickness (EFTT) with IMA and MPO levels in patients with CKD. MATERIALS AND METHODS Predialysis CKD patients admitted to the Nephrology outpatient clinic, patients on haemodialysis and healthy volunteers were included, 111 patients were in the study. EFTT measurement was performed with the transthoracic view using an ECHO device. RESULTS The analysis conducted among the groups in terms of IMA, MPO levels, and EFTT revealed a statistically significant difference (p < 0.001). It was determined to be the lowest in the healthy volunteers, slightly increased in the pre-dialysis group whereas it was quite high in the haemodialysis group. According to the correlation test performed, we observed that IMA, MPO levels, and EFTT were found to be highly correlated to progression of CKD. CONCLUSION We believe that we have introduced three novel follow-up parameters, such as: IMA, MPO, EFTT to literature for the follow-up of CKD. As the levels of IMA MPO and EFTT increase, the severity of CKD increases (Tab. 4, Fig. 1, Ref. 25).
Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. CPU frequency scaling can be used to reduce power dissipation, but also impacts virtual machine (VM) performance and therefore revenue. In this paper, we first propose a non-linear power model that estimates power dissipation of a multi-core CPU physical machine (PM) and second a pricing model that adjusts the pricing based on the VM's CPU-boundedness characteristics. Finally, we present a cloud controller that uses these models to allocate VM and scale CPU frequencies of the physical machine (PM) to achieve energy cost savings that exceed service revenue losses. We evaluate the proposed approach using simulations with realistic VM workloads, electricity price and temperature traces and estimate energy savings of up to 14.57 percent.
High-Temperature Ultrasonic Sensor for Fission Gas Characterization in MTR Harsh Environment

In this paper, we present the first steps in the development of a new generation of ultrasonic sensors devoted to fission gas characterization in a material testing reactor harsh environment. The piezoelectric active material is a sodium bismuth titanate thick film fabricated using a screen-printing method. Characterization of piezoelectric and dielectric properties and material parameter studies based on temperature were carried out. The key results of this paper are demonstrated and discussed in terms of feasibility for application to a new sensor device operating at high temperature (350 °C). An acoustic test in a cylindrical cavity shows high sensitivity to measurements of liquid substances at room temperature. To verify its applicability to gas detection, a theoretical model based on electromechanical properties and impedance matching equations was introduced. This model was used in the studies on gas measurement under room temperature and at 350 °C. It was theoretically shown that the sensor was able to operate for gas in situ under harsh temperature conditions.
Temporal Item Embedding with Static Similarity Regularization for Sequential Recommendation

Recommender systems have attracted a significant amount of research interests in recent years. Traditional methods such as content-based approaches and collaborative filtering approaches mainly focus on modeling the general user preference by using the user's whole purchase history. However, except for user preference, the sequential information should also be considered because human behavior exhibits sequential patterns. The methods considering both sequential item relationship and user preference are called sequential recommendation methods, which are mostly base on Markov Chains. Due to the difficulty of parameter estimation, most prior work only considers the latest interaction, which is insufficient according to our observations. To that end, in this paper, we propose a temporal item embedding method based on word2vec framework to model long purchase history, with each purchased item regarded as a word in sentence. Meanwhile, inspired by the collaborative filtering in traditional recommendations, we assume that similar items should have similar embeddings and propose static similarity regularization (SSR) in recommendation. The regularized item embedding can capture user preference and sequential item relationship simultaneously. Experiments on real-world datasets show that the proposed approach outperforms a spectrum of state-of-the-art algorithms.
Faster ICA Under Orthogonal Constraint

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy over the set of orthogonal matrices, which can quickly separate both super- and sub-Gaussian signals. It returns the same set of sources as the widely used FastICA algorithm. Through numerical experiments, we show that our method is faster and more robust than FastICA on real data.
Structural basis for activation of SAGA histone acetyltransferase Gcn5 by partner subunit Ada2

Significance Regulating gene expression is critical to the normal function of a biological cell, and misregulation of gene expression often leads to a diseased cell. In eukaryotic cells, DNA genetic information is wrapped around histone proteins into chromatin. Modification of histone proteins with the acetyl chemical group (i.e., acetylation) is an important mechanism for activating gene expression. Our biochemical and structural studies of the Gcn5 histone acetyltransferase and its partner protein Ada2 explain how Ada2 activates Gcn5’s enzymatic activity without directly contacting the histone substrate, as previously proposed. Instead, Ada2 indirectly influences binding of the enzyme cofactor acetyl-CoA, a mechanism not previously observed for histone modification enzymes. The Gcn5 histone acetyltransferase (HAT) subunit of the SAGA transcriptional coactivator complex catalyzes acetylation of histone H3 and H2B N-terminal tails, posttranslational modifications associated with gene activation. Binding of the SAGA subunit partner Ada2 to Gcn5 activates Gcn5’s intrinsically weak HAT activity on histone proteins, but the mechanism for this activation by the Ada2 SANT domain has remained elusive. We have employed Fab antibody fragments as crystallization chaperones to determine crystal structures of a yeast Ada2/Gcn5 complex. Our structural and biochemical results indicate that the Ada2 SANT domain does not activate Gcn5’s activity by directly affecting histone peptide binding as previously proposed. Instead, the Ada2 SANT domain enhances Gcn5 binding of the enzymatic cosubstrate acetyl-CoA. This finding suggests a mechanism for regulating chromatin modification enzyme activity: controlling binding of the modification cosubstrate instead of the histone substrate.
Gender Difference on Destination Image and Travel Options: An Exploratory Text-Mining Study

Destination image has gained increasingly attention of tourism researchers. However, little attention has been devoted to study the difference in perception of destination image among different demographic groups using the big data analytics in general, the text mining approach in particular. Therefore, in this paper, we develop a method based on Latent Dirichlet Allocation (LDA) to extract cognitive destination image from travel blogs. Furthermore, we explore the differences in cognitive image and travel options among different gender groups. Through experiments, we extract 26 topics by LDA and find that the female has significantly preferences to natural landscape and rural scenery than the male. Meanwhile, the male prefers historical sites than the female. In terms of the travel options, we find the female usually travel with travel agency, while the male travel by self-drive. Our study contributes to the tourism planning, tourism positioning and marketing for different target groups.
Compliant Manipulation of Free-Floating Objects

Compliant motions allow alignment of workpieces using naturally occurring interaction forces. However, free-floating objects do not have a fixed base to absorb the reaction forces caused by the interactions. Consequently, if the interaction forces are too high, objects can gain momentum and move away after contact. This paper proposes an approach based on direct force control for compliant manipulation of free-floating objects. The objective of the controller is to minimize the interaction forces while maintaining the contact. The proposed approach achieves this by maintaining small constant force along the motion direction and an apparent reduction of manipulator inertia along remaining Degrees of Freedom (DOF). Simulation results emphasize the importance of relative inertia of the robotic manipulator with respect to the free-floating object. The experiments were performed with KUKA LWR4+ manipulator arm and a two-dimensional micro-gravity emulator (object floating on an air bed), which was developed in-house. It was verified that the proposed control law is capable of controlling the interaction forces and aligning the tools without pushing the object away. We conclude that direct force control works better with a free-floating object than implicit force control algorithms, such as impedance control.
learning Effective Factorized Hidden Layer Bases Using Student-Teacher Training for LSTM Acoustic Model Adaptation

Factorized Hidden Layer (FHL) has been proposed for the adaptation of deep neural network (DNN) and Long Short-Term Memory (LSTM) based acoustic models (AMs). In FHL, a speaker-dependent (SD) transformation matrix and an SD bias are included in addition to the standard affine transformation. The SD transformation is a linear combination of rank- l matrices whereas the SD bias is a linear combination of vectors. However, the adaptation of LSTMs is challenging and often reports modest gains. In this paper, we propose to use student-teacher training to estimate more efficient FHL bases for LSTM AMs using an FHL adapted DNN as the teacher model. For both AMI IHM and AMI SDM tasks, FHL achieves 3.2% absolute improvement over the frame-level cross entropy trained LSTM baselines. Moreover, FHL results 3.0% and 3.8% absolute improvements over sequentially trained LSTM baselines for the AMI IHM and AMI SDM tasks respectively.
Weak measurement for improving the efficiency of remote state preparation in noisy

Quantum communication provides a new way for transmitting highly sensitive information. But the existence of quantum noise inevitably affects the security and reliability of a quantum communication system. The technique of weak measurement and its reversal measurement (WMRM) has been proposed to suppress the effect of quantum noise, especially, the amplitude-damping noise. Taking a GHZ based remote state preparation (RSP) scheme as an example, we discuss the effect of WMRM for suppressing four types of quantum noise that usually encountered in real-world, i.e., not only the amplitude-damping noise, but also the bit-flip, phase-flip (phase-damping) and depolarizing noise. And we give a quantitative study on how much a quantum output state can be improved by WMRM in noisy environment. It is shown that the technique of WMRM has certain effect for improving the fidelity of the output state in the amplitude-damping noise, and only has little effect for suppressing the depolarizing noise, while has no effect for suppressing the bit-flip and phase-flip (phase-damping) noise. Our result is helpful for improving the efficiency of entanglement-based quantum communication systems in real implementation.
ESTHER: Extremely Simple Image Translation Through Self-Regularization

Image translation between two domains is a class of problems where the goal is to learn the mapping from an input image in the source domain to an output image in the target domain. It has important applications such as data augmentation, domain adaptation, and unsupervised training. When paired training data are not accessible, the mapping between the two domains is highly under-constrained and we are faced with an ill-posed task. Existing approaches tackling this challenge usually make assumptions and introduce prior constraints. For example, CycleGAN [59] assumes cycle-consistency while UNIT [31] assumes shared latent-space between the two domains. We argue that none of these assumptions explicitly guarantee that the learned mapping is the desired one. We, taking a step back, observe that most image translations are based on the intuitive requirement that the translated image needs to be perceptually similar to the original image and also appear to come from the new domain. On the basis of such observation, we propose an extremely simple yet effective image translation approach, which consists of a single generator and is trained with a self-regularization term and an adversarial term. We further propose an adaptive method to search for the best weight between the two terms. Extensive experiments and evaluations show that our model is signiﬁcantly more cost-effective and can be trained under budget, yet easily achieves better performance than other methods on a broad range of tasks and applications.
Voice Activity Detection Using Neurograms

Existing acoustic-signal-based algorithms for Voice Activity Detection (VAD) do not perform well in the presence of noise. In this study, we propose a method to improve VAD accuracy by employing another type of signal representation which is derived from the response of the human Auditory-Nerve (AN) system. The neural responses referred to as a neurogram are simulated using a computational model of the AN system for a range of Characteristic Frequencies (CFs). Features are extracted from neurograms using the Discrete Cosine Transform (DCT), and are then trained using a Multilayer Perceptron (MLP) classifier to predict the VAD intervals. The proposed method was evaluated using the QUT-NOISE-TIMIT corpus, and the NIST scoring algorithm for VAD was employed as an accuracy measure. The proposed neural-response-based method exhibited an overall better VAD accuracy over most of the existing methods.
The Fast and the Flexible: Training Neural Networks to Learn to Follow Instructions from Small Data

Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from. Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible. In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker. Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary. Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively.
Multi-Scale Recurrent Neural Network for Sound Event Detection

Sound event detection (SED) in real life is an interesting but challenging task due to the polyphonic and long-term dependent nature of sound events. Recently, multi-label recurrent neural networks (RNNs) have shown promises. However, even equipped with long short-term memory (LSTM) or gated recurrent unit (GRU) cells, RNNs are still limited to model the long-term dependency. In this paper, we propose a multiscale RNN to address this issue. By integrating information from different time resolutions, we can better capture both the fine-grained and long-term dependencies of sound events. We experiment on the development sets of Task3 of DCASE2016 and DCASE2017. Compared to our previously proposed single-scale RNN that won the third place among the 13 teams in Task3 of DCASE2017, the proposed multiscale model achieves statistically significantly better performance on the development datasets of both DECASE2016 and DCASE2017.