Miracle-dz/UniFAR-FTUs huggingface dataset:
A Closer Look at Variance Implementations In Modern Database Systems

Variance is a popular and often necessary component of aggregation queries. It is typically used as a secondary measure to ascertain statistical properties of the result such as its error. Yet, it is more expensive to compute than primary measures such as SUM, MEAN, and COUNT. There exist numerous techniques to compute variance. While the definition of variance implies two passes overthe data, other mathematical formulations lead to a singlepass computation. Some single-pass formulations, however, can suffer from severe precision loss, especially for large datasets. In this paper, we study variance implementations in various real-world systems and find that major database systems such as PostgreSQL and most likely System X, a major commercial closed-source database, use a representation that is efficient, but suffers from floating point precision loss resulting from catastrophic cancellation. We review literature over the past five decades on variance calculation in both the statistics and database communities, and summarize recommendations on implementing variance functions in various settings, such as approximate query processing and large-scale distributed aggregation. Interestingly, we recommend using the mathematical formula for computing variance if two passes over the data are acceptable due to its precision, parallelizability, and surprisingly computation speed.
Learning to Communicate Efficiently with Group Division in Decentralized Multi-agent Cooperation

Recent advances in multi-agent reinforcement learning show that agents can spontaneously learn when and what to communicate with each other to support effective cooperation. However, the existing approaches assume a fully-connected network with unlimited bandwidth, which is impractical in many real-world scenarios. For instance, in many multi-robot applications, robots are connected only through an unstable wireless network with limited bandwidth. Therefore, we must enable the agents to learn communication strategy that takes the consumption of network resources into account. This paper proposes a group division-based attentional communication model (GDAC), which can divide agents into groups according to their "attention" in the learned communication strategy. According to the novel "attention" mechanism, agents can be dynamically grouped according to their task relevance, and the communication only takes places inside the same group. As a result, it avoids a fully-connected communication architecture and can significantly reduce the bandwidth consumption at runtime. This model has been successfully applied to the environmental exploration task with a group of agents. The results show that GDAC could effectively reduce the total amount of communication message and yield improved performance over the existing fully-connected communication architecture.
3D Human Motion Editing and Synthesis: A Survey

The ways to compute the kinematics and dynamic quantities of human bodies in motion have been studied in many biomedical papers. This paper presents a comprehensive survey of 3D human motion editing and synthesis techniques. Firstly, four types of methods for 3D human motion synthesis are introduced and compared. Secondly, motion capture data representation, motion editing, and motion synthesis are reviewed successively. Finally, future research directions are suggested.
Exploring psychophysiological markers of vulnerability to somatic illnesses in females.

OBJECTIVE To examine the association between biological stress regulation and somatic complaints in young girls prior to the onset of clear psychopathology such as somatization disorder. METHODS Salivary cortisol, heart rate variability (HRV), and negative mood were assessed in 48 12-year-old girls in response to the Trier Social Stress Test for Children (TSST-C). Parent and child report on the Children's Somatization Inventory was used to identify girls with high and low somatic complaints. RESULTS Girls with high levels of somatic complaints had significantly higher initial levels of cortisol, which decreased over time, and showed a trend for a more limited HRV in response to the TSST-C than girls with low levels of somatic complaints. CONCLUSIONS High levels of cortisol and possibly low HRV among girls with somatic complaints may interfere with flexibility in responding to typical psychosocial stressors, which may increase vulnerability to the onset of somatic illnesses in females.
Aligning Chips Face-to-Face for Dense Capacitive and Optical Communication

We report a new method that precisely self-aligns face-to-face semiconductor chips or wafers to enable communication between the chips using electromagnetic waves. Our alignment mechanism takes advantage of miniaturized versions of two of nature's idealized shapes: an inverse pyramidal shape defined by a self-terminating wet-etch process in silicon and micro-spheres with radii accurate to submicron accuracy. This approach allows chips to be packaged using passive alignment that is self locating and reaches nearly one micron level of chip misalignment tolerance. Packages for applications to capacitive and optical connections are presented. Additionally, we describe a physical architecture for a multi-chip array packages with ¿bridge¿ and ¿island¿ chips where the function of the bridge is to transfer electromagnetic signals between island chips using either capacitive or optical proximity communication. The bridge chip can provide a predetermined amount of compliance to help maintain alignment and thereby accommodate topology variants in first level package or in chip thickness when required. Experimental packages providing precise alignment between 1-D arrays and 2-D arrays of chips are presented. We show that our precision alignment mechanism enables high fidelity 10 Gb/s optical-proximity-communication with reflecting mirrors micro-machined into Silicon and co-integrated to low loss silicon-on-insulator waveguides for chip-to-chip communication. The alignment mechanism was also applied to a demonstration of chip-to-chip capacitive proximity communication in a linear array of six chips. Alignment measurements on a 4 × 4 array of chips are reported.
Training Deep Face Recognition for Efficient Inference by Distillation and Mutual Learning

Currently most of deep face recognition algorithms utilize heavy networks to achieve the state-of-the-art performance. In most scenarios, the more challenging task is to achieve the relative high accuracy with low computational cost especially for embedded devices. In this paper, we propose a lightweight network for face recognition using distillation and deep mutual learning. In proposed methods a new indicator is designed to monitor the model convergence and an assessment criteria is developed to evaluate the Labeled Faces in the Wild(LFW) dataset. Experiments show that our models work better than networks trained directly and other mobile face recognition solutions.
Wnt canonical pathway restricts graded Shh/Gli patterning activity through the regulation of Gli3 expression

Dorsoventral patterning of the vertebrate nervous system is achieved by the combined activity of morphogenetic signals secreted from dorsal and ventral signalling centres. The Shh/Gli pathway plays a major role in patterning the ventral neural tube; however, the molecular mechanisms that limit target gene responses to specific progenitor domains remain unclear. Here, we show that Wnt1/Wnt3a, by signalling through the canonical β-catenin/Tcf pathway, control expression of dorsal genes and suppression of the ventral programme, and that this role in DV patterning depends on Gli activity. Additionally, we show that Gli3 expression is controlled by Wnt activity. Identification and characterization of highly conserved non-coding DNA regions around the human Gli3 gene revealed the presence of transcriptionally active Tcf-binding sequences. These indicated that dorsal Gli3 expression might be directly regulated by canonical Wnt activity. In turn, Gli3, by acting as a transcriptional repressor, restricted graded Shh/Gli ventral activity to properly pattern the spinal cord.
Precise Runahead Execution

Runahead execution improves processor performance by accurately prefetching long-latency memory accesses. When a long-latency load causes the instruction window to fill up and halt the pipeline, the processor enters runahead mode and keeps speculatively executing code to trigger accurate prefetches. A recent improvement tracks the chain of instructions that leads to the long-latency load, stores it in a runahead buffer, and executes only this chain during runahead execution, with the purpose of generating more prefetch requests. Unfortunately, all prior runahead proposals have shortcomings that limit performance and energy efficiency because they release processor state when entering runahead mode and then need to refill the pipeline to restart normal operation. Moreover, runahead buffer limits prefetch coverage by tracking only a single chain of instructions that leads to the same long-latency load. We propose precise runahead execution (PRE) which builds on the key observation that when entering runahead mode, the processor has enough issue queue and physical register file resources to speculatively execute instructions. This mitigates the need to release and re-fill processor state in the ROB, issue queue, and physical register file. In addition, PRE pre-executes only those instructions in runahead mode that lead to full-window stalls, using a novel register renaming mechanism to quickly free physical registers in runahead mode, further improving efficiency and effectiveness. Finally, PRE optionally buffers decoded runahead micro-ops in the frontend to save energy. Our experimental evaluation using a set of memory-intensive applications shows that PRE achieves an additional 18.2% performance improvement over the recent runahead proposals while at the same time reducing energy consumption by 6.8%.
Global anomaly detection in crowded scenes based on optical flow saliency

In this paper, an algorithm of global anomaly detection in crowded scenes using the saliency in optical flow field is proposed. Before the process of extracting the histogram of maximal optical flow projection (HMOFP), the scale invariant feature transforms (SIFT) method is utilized to get the saliency map of optical flow field. On the basis of the HMOFP feature of normal frames, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy after a process of selecting the training samples, which is better than the dictionary simply composed by the HMOFP feature of the whole training frames. In order to detect whether a frame is normal or not, we use the ℓ1-norm of the sparse reconstruction coefficients (i.e., the sparse reconstruction cost, SRC) to show the anomaly of the testing frame, which is simple but very effective. The experiment results on UMN dataset and the comparison to the state-of-the-art methods show that our algorithm is promising.
Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.
Smart Learning for Cyber Psychosocial and Physical Computation

With the fast integrative development of the Internet, the Internet of things, and mobile communications, it has become an interesting issue to study human’s psychology and behaviors from the big data of the interrelated spaces based on machine learning. This article analyzes the new requirements of machine learning for CPP (Cyber Psychosocial and Physical Computation), and presents the concept, framework, method, and application of smart learning, which refers to the comprehensive learning ability of rational intelligence and emotional intelligence. In order to better understand the relationships among different spaces and reduce the computation cost by prior-knowledge, the authors propose the above learning process to be built up on the basis of neural mechanism and context awareness. Research of this article aims to provide a reference for the development of machine learning to deal with human’s psychology and behaviors under the ubiquitous environment.
A Front-End Speech Enhancement System for Robust Automotive Speech Recognition

This paper presents a front-end speech enhancement approach to robust speech recognition in automotive environments. It combines model-based voice activity detection (VAD), relative transfer function (RTF) based generalized sidelobe cancelation, and single-channel post filtering to enhance the speech signal of interest, thereby improving the robustness of speech recognition. First, we choose four typical driving scenarios, which include most of the noise types in automobiles to record training data. The recorded data are then used to train Gaussian mixture models (GMMs) for both speech and noise. The trained GMMs are subsequently used to estimate the speech presence probability on a frame-by-frame basis. This speech presence probability is then served as the basic information for RTF estimation, adaptive beamforming, and post-filtering. Experiments are conducted in real automotive environments and the results show that the developed method can significantly improve the performance of both VAD and automatic speech recognition (ASR).
Identification and Determination of the Bloodstains Dry Time in the Crime Scenes Using Laser-Induced Breakdown Spectroscopy

The bloodstain is one kind of the most important evidences in crime scenes. Laser-induced breakdown spectroscopy (LIBS) technology was used in this paper to distinguish bloodstains from analogs and determine the time since deposited (TSD) of bloodstains. Combined with support vector machine classifier, the identification results of bloodstains from analogs achieved 96% in 20 s. Four kinds of substrates were chosen to simulate the grounds and lethal tools in crime scenes. The linear relationship between single spectral line intensity and TSD was analyzed. The Pearson linear correlation coefficient (Pearson's r) calculated on the stone bricks calibration result is higher than 0.9. The spectral line intensity ratio was proposed to improve the linear calibration relationship with TSD. Building the calibration relationship between intensity ratios and TSDs, the Pearson's r and R2 values for results on all four substrates were improved. For stone bricks, ceramic tiles, and copper plates, the correlation coefficients were all higher than 0.95. Especially for results on stone bricks, the Pearson's r achieved 0.99024 and the R2 value achieved 0.97571. The optimal single lines for calibration on four substrates were all related to Fe element and the optimal line intensity ratios were all ratios of an Fe line to a line of other elements.
Summary of Research on Seismic Behavior of Precast Column System With the Same Effect as the Cast-In-Place Column

This paper introduces five splicing methods between the precast columns and the cap beam or footing: Wet Joint connection, socket connection, grouted splice sleeve connection, grouted ducts connection, UHPC connection, and the grouted ducts connection between each section of the column, reviewing various types of connection structure, construction characteristics and seismic performance from two aspects of experimental research and engineering application, in order to provide reference for domestic research peers and promote the application of the precast column system with the same effect as the cast-in-place column (CIP) in China.
L2 Mispronunciation Verification Based on Acoustic Phone Embedding and Siamese Networks

Non-native mispronunciation verification with instructive feedback are desired in computer assisted pronunciation training (CAPT) system, as it benefits second language (L2) learners for improving their pronunciation. We proposed an approach of evaluating L2 learners’ goodness of pronunciation based on phone embedding and Siamese networks to address important research issues in CAPT: mispronunciation verification and pronunciation evaluation. A pair of acoustic feature vectors of phone segments with a pair-wise label was used as system inputs, and the feature vectors were encoded into high-level representation in form of phone embeddings by Siamese networks, consequently, each types of phones were expected to be differentiated by their embeddings’ similarities. As a result, Siamese networks with hinge cosine similarity which achieved an accuracy of 89.93% and outperformed the other methods, and diagnostic accuracy was 89.19% in pronunciation errors verification task with the best approach.
Speech Super-Resolution Using Parallel WaveNet

Audio super-resolution is the task to increase the sampling rate of a given low-resolution (i.e. low sampling rate) audio. One of the most popular approaches for audio super-resolution is to minimize the squared Euclidean distance between the reconstructed signal and the high sampling rate signal in a point-wise manner. However, such approach has intrinsic limitations, such as the regression to mean problem. In this work, we introduce a novel auto-regressive method for the speech super-resolution task, which utilizes WaveNet to model the distribution of the target high-resolution signal conditioned on the log-scale mel-spectrogram of the low-resolution signal. As an auto-regressive neural network, WaveNet uses the negative log-likelihood as the objective function, which is much more suitable for highly stochastic process such as speech waveform, instead of the Euclidean distance. We also train a parallel WaveNet to speed up the generating process to real-time. In the experiments, we perform speech super-resolution by increasing the sampling rate from 4kHz to 16kHz on the VCTK corpus. The proposed method can achieve the improvement of ∼2 dB over the base-line deep residual convolutional neural network (CNN) under the Log-Spectral Distance (LSD) metric.
Cross-Dialectal Perception of the Third-Tone Sandhi in Standard Chinese – Evidence from Eye Movements

It is well-known that in Standard Chinese, the sandhi-derived T3 is realized with a risingf0 pattern that is typically identical with the lexical T2. However, it is less understood on how dialect backgrounds play a role in T3 sandhi perception. The present study set out to shed some light on this issue with evidence with eye movements by examining the participants’ looks to both targets and competitors within the Visual World Paradigm when they heard T3T3 stimuli as well as their T2T3 near-minimal-pair counterparts. We recruited participants from both Beijing and Nanjing. Our results show that, due to the modulation of their native tonal system, Nanjing listeners showed clear early advantages in the online processing of tones in Standard Chinese, as reflected with more looks to the target as compared to the Beijing group.
Proximity-based D2D Mode Selection Scheme for LTE Networks

Device-to-Device (D2D) communication in cellular network is defined as the direct communication between mobile users involving neither eNB nor core networks. D2D improves spectrum efficiency and saves transmission power. In this paper, we propose a mode selection scheme that consists of Cellular Mode, D2D Mode and Relay Mode. We organize the D2D pair through proximity discovery, and UEs make all decisions of D2D grouping. Our method can reduce eNB's load of network control without causing heavy computation to UEs. By applying three modes in the network, it can minimize the drawback of D2D communication underlay and maximize the spectrum efficiency and system throughput over the LTE cellular networks. We use MATLAB simulator to evaluate our proposed scheme, the results show that our method not only upgrades the system throughput but also reduces the impact to cellular users caused by adding D2D communication to the network.
Henry Darcy's Perfection of the Pitot Tube

Starting in 1856 Henry Darcy, with the assistance of Henry Bazin, published four works that show various forms of an improved Pitot tube design. Although Henri Pitot had invented the device in 1732, theoretical and design weaknesses had kept it little more than a scientific toy. Darcy's improved instruments provided accurate and easy measurements of point velocity for the first time, which allowed advances in open channel and pipe flow hydraulics. His final design for the instrument tip is reflected today in all of our modern instruments. A reproduction of Darcy's published 1858 design was completed and shown to work as reported. Darcy's contribution to the development of the device equaled or exceeded Pitot's initial work, thus making it appropriate to refer to the modern instrument as the "PitotDarcy tube".
Evaluating Modeling Units and Sub-word Features in Language Models for Turkish ASR

Turkish is a morphologically rich language, which leads to serious data sparsity problems in language modeling for automatic speech recognition (ASR) tasks. Using sub-words as language modeling units and incorporating sub-word features into word-level models are two strategies to alleviate the problem. In this paper, we propose a novel model architecture which can incorporate sub-word features directly. And we use a CNN to learn (sub)word embeddings as sub-word features from character or sub-word level input. We evaluate the proposed model on Turkish ASR task. We choose word and morph (sub-word) as language modeling unit respectively. Results show that the consistency between language modeling units and ASR system units is important for the effectiveness of rescoring. And the proposed method reduces the word error rate (WER) of word and statistical sub-word level system by absolute 1.56% and 1.87%.
From Speech Signals to Semantics — Tagging Performance at Acoustic, Phonetic and Word Levels

Spoken language understanding (SLU) is to decode the semantic information embedded in speech input. SLU decoding can be significantly degraded by mismatched acoustic/language models between training and testing of a decoder. In this paper we investigate the semantic tagging performance of bidirectional LSTM RNN (BLSTM-RNN) with input at acoustic, phonetic and word levels. It is tested on a crowdsourced, spoken dialog speech corpus spoken by non-native speakers in a job interview task. The tagging performance is shown to be improved successively from low-level, acoustic MFCC, midlevel, stochastic senone posteriorgram, to high-level, ASR recognized word string, with the corresponding tagging accuracies at 70.6%, 82.1% and 85.1%, respectively. With a score fusion of the three individual RNNs together, the accuracy can be further improved to 87.0%.