Miracle-dz/UniFAR-FTUs huggingface dataset:
On zero-free regions for the anti-ferromagnetic Potts model on bounded-degree graphs

For a graph $G=(V,E)$, $k\in \mathbb{N}$, and a complex number $w$ the partition function of the univariate Potts model is defined as \[ {\bf Z}(G;k,w):=\sum_{\phi:V\to [k]}\prod_{\substack{uv\in E \\ \phi(u)=\phi(v)}}w, \] where $[k]:=\{1,\ldots,k\}$. In this paper we give zero-free regions for the partition function of the anti-ferromagnetic Potts model on bounded degree graphs. In particular we show that for any $\Delta\in \mathbb{N}$ and any $k\geq e\Delta+1$, there exists an open set $U$ in the complex plane that contains the interval $[0,1)$ such that ${\bf Z}(G;k,w)\neq 0$ for any $w\in U$ and any graph $G$ of maximum degree at most $\Delta$. (Here $e$ denotes the base of the natural logarithm.) For small values of $\Delta$ we are able to give better results. As an application of our results we obtain improved bounds on $k$ for the existence of deterministic approximation algorithms for counting the number of proper $k$-colourings of graphs of small maximum degree.
ALSTM: Adaptive LSTM for Durative Sequential Data

Long short-term memory (LSTM) network is an effective model architecture for deep learning approaches to sequence modeling tasks. However, the current LSTMs can't use the property of sequential data when dealing with the sequence components, which last for a certain period of time. This may make the model unable to benefit from the inherent characteristics of time series and result in poor performance as well as lower efficiency. In this paper, we present a novel adaptive LSTM for durative sequential data which exploits the temporal continuance of the input data in designing a new LSTM unit. By adding a new mask gate and maintaining span, the cell's memory update is not only determined by the input data but also affected by its duration. An adaptive memory update method is proposed according to the change of the sequence input at each time step. This breaks the limitation that the cells calculate the cell state and hidden output for each input always in a unified manner, making the model more suitable for processing the sequences with continuous data. The experimental results on various sequence training tasks show that under the same iteration epochs, the proposed method can achieve higher accuracy, but need relatively less training time compared with the standard LSTM architecture.
Data2Services: enabling automated conversion of data to services

While data are becoming increasingly easy to find and access on the Web, significant effort and skill is still required to process the amount and diversity of data into convenient formats that are friendly to the user. Moreover, these efforts are often duplicated and are hard to reuse. Here, we describe Data2Services, a new framework to semi-automatically process heterogeneous data into target data formats, databases and services. Data2Services uses Docker to faithfully execute data transformation pipelines. These pipelines automatically convert target data into a semantic knowledge graph that can be further refined to conform to a particular data standard. The data can be loaded in a number of databases and are made accessible through native and autogenerated APIs. We describe the architecture and a prototype implementation for data in the life sciences.
Using Insider Swapping of Time Intervals to Perform Highly Invisible Network Flow Watermarking

Network flow watermarking (NFW) is an emerging flow correlation technique to deanonymize an anonymous communication system or detect stepping stones, in which a watermark is encoded into a network flow by manipulating some flow characteristics, predominantly by altering timing information. Although interval-based NFWs that employ time intervals as carrier have proven to be capable of resisting moderate network interference, they are vulnerable to some statistic-based attacks, which may expose the very existence of watermark and enable attackers to damage or remove watermark from the observed flow. In this study, using insider swapping of time intervals and an adaptive centroid quantization framework, we design a highly invisible NFW scheme, which is undetectable by multi-flow attacks (MFA), Kullback-Leibler divergence (KLD) test, Kolmogorov-Smirnov (K-S) test, and spread spectrum flow watermark (SSFW) detection. Experimental results using real traffic and public dataset show that the proposed NFW scheme can outperform three typical NFW schemes on invisibility while maintaining a strong interference-resistance capability of network jitter, packet loss, and dummy packet insertion.
A Mini Review: Recent Advances in Surface Modification of Porous Silicon

Porous silicon has been utilized within a wide spectrum of industries, as well as being used in basic research for engineering and biomedical fields. Recently, surface modification methods have been constantly coming under the spotlight, mostly in regard to maximizing its purpose of use. Within this review, we will introduce porous silicon, the experimentation preparatory methods, the properties of the surface of porous silicon, and both more conventional as well as newly developed surface modification methods that have assisted in attempting to overcome the many drawbacks we see in the existing methods. The main aim of this review is to highlight and give useful insight into improving the properties of porous silicon, and create a focused description of the surface modification methods.
Self-report sleep quality combined with sleep time variability distinguishes differences in fatigue, anxiety, and depression in individuals with multiple sclerosis: A secondary analysis

Background Nearly 70% of individuals with multiple sclerosis report sleep disturbances or poor sleep quality. Sleep disturbances may exacerbate or complicate the management of multiple sclerosis-related symptoms. While sleep variability has been associated with several health outcomes, it is unclear how sleep variability is associated with multiple sclerosis-related symptoms. Objective The purpose of this study was to determine how total sleep time variability combined with self-reported sleep quality is associated with fatigue, depression, and anxiety in individuals with multiple sclerosis. Methods This study involved a secondary analysis of actigraphy data and questionnaires to assess sleep quality, fatigue, anxiety, and depression. Results There were significant differences between the Good Sleepers (good sleep quality/low sleep time variability; n=14) and Bad Sleepers (poor sleep quality/high sleep time variability; n=23) in overall fatigue (p=0.003), cognitive (p=0.002) and psychosocial fatigue (p=0.01) subscales, and in trait anxiety (p=0.007). There were significant differences in state (p=0.004) and trait (p=0.001) anxiety and depression (p=0.002) between the Good Sleepers and Poor Reported Sleepers (poor sleep quality/low sleep time variability; n=24). Conclusion These results indicate different factors are associated with poor sleep quality in individuals with low versus high total sleep time variability. Considering the factors that are associated with sleep quality and variability may allow for better tailoring of interventions aimed at improving sleep issues or comorbid conditions.
Social Recommendation Based on Implicit Friends Discovering Via Meta-Path

With the growing popularity of online social platforms, it has been universally recognized that incorporating social relations into recommender systems can usually alleviate the problem of data sparsity. However, social recommender systems based on explicit relations are not as successful as expected due to the noise and the social cold issue of explicit social links. The intuition of utilizing explicit relations is that users share similar preferences if they are friends in the social network. In fact, quite a lot of users who are distant from each other in the social network also have similar tastes. The user item network and the user social network can provide useful information that can complement each other, so that exploring the implicit friends using the heterogeneous network they formed would be more helpful. In this paper, we propose an approach IFSR to discover implicit friends over the heterogeneous network to improve the performance of social recommendation. To find out reliable implicit ties, we first model the system as a heterogeneous network upon which both the preferences and social information are coupled. Over the HIN, similarities between each pair of users can be quantified through network embedding based representation learning. To reduce the computational cost while preserving the information embedded in the original networks and uncover the latent information hiding in the HIN, several meaningful meta-paths over the HIN are designed to guide the process of random walks. Finally, the Top-K implicit friends are incorporated into a social bayesian ranking model to enhance the performance of item ranking. Experimental results on three datasets demonstrate IFSR outperforms the state-of-the-art methods and illustrate why the implicit friends are advantageous for social recommendation.
Creation of Numerical Constants in Robust Gene Expression Programming

The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimization. The prevailing attempts to resolve this issue either employ separate real-valued local optimizers or special numeric mutations. While the former yield better accuracy than the latter, they add to implementation complexity and significantly increase computational cost. In this paper, we propose a special numeric crossover operator for use with Robust Gene Expression Programming (RGEP). RGEP is a type of genotype/phenotype evolutionary algorithm closely related to GP, but employing linear chromosomes. Using normalized least squares error as a fitness measure, we show that the proposed operator is significantly better in finding highly accurate solutions than the existing numeric mutation operators on several symbolic regression problems. Another two important advantages of the proposed operator are that it is extremely simple to implement, and it comes at no additional computational cost. The latter is true because the operator is integrated into an existing crossover operator and does not call for an additional cost function evaluation.
Predicting Low Risk for Sustained Alcohol Use After Early Liver Transplant for Acute Alcoholic Hepatitis: The Sustained Alcohol Use Post–Liver Transplant Score

Early liver transplant (LT) for alcohol‐associated disease (i.e., without a specific sobriety period) is controversial but increasingly used. Using the multicenter American Consortium of Early Liver Transplantation for Alcoholic Hepatitis (ACCELERATE‐AH) cohort, we aimed to develop a predictive tool to identify patients pretransplant with low risk for sustained alcohol use posttransplant to inform selection of candidates for early LT. We included consecutive ACCELERATE‐AH LT recipients between 2012 and 2017. All had clinically diagnosed severe alcoholic hepatitis (AH), no prior diagnosis of liver disease or AH, and underwent LT without a specific sobriety period. Logistic and Cox regression, classification and regression trees (CARTs), and least absolute shrinkage and selection operator (LASSO) regression were used to identify variables associated with sustained alcohol use post‐LT. Among 134 LT recipients for AH with median period of alcohol abstinence pre‐LT of 54 days, 74% were abstinent, 16% had slips only, and 10% had sustained alcohol use after a median 1.6 (interquartile range [IQR]: 0.7‐2.8) years follow‐up post‐LT. Four variables were associated with sustained use of alcohol post‐LT, forming the Sustained Alcohol Use Post‐LT (SALT) score (range: 0‐11): >10 drinks per day at initial hospitalization (+4 points), multiple prior rehabilitation attempts (+4 points), prior alcohol‐related legal issues (+2 points), and prior illicit substance abuse (+1 point). The C statistic was 0.76 (95% confidence interval [CI]: 0.68‐0.83). A SALT score ≥5 had a 25% positive predictive value (95% CI: 10%‐47%) and a SALT score of <5 had a 95% negative predictive value (95% CI: 89%‐98%) for sustained alcohol use post‐LT. In internal cross‐validation, the average C statistic was 0.74. Conclusion: A prognostic score, the SALT score, using four objective pretransplant variables identifies candidates with AH for early LT who are at low risk for sustained alcohol use posttransplant. This tool may assist in the selection of patients with AH for early LT or in guiding risk‐based interventions post‐LT.
Research on Influencing Factors of Radical Technology Innovation-Based on Structural Equation Model

This paper defines the concept and connotation of radical innovation, and discusses the theoretical and practical significance of radical innovation. This paper analyzes the types and characteristics of radical innovation, and expounds the process and management mode of radical innovation. Based on the hypothesis of the relationship between the radical factor and the innovation performance, the hypothesis model of the relationship between the two is constructed, and the model and the correlation hypothesis are tested by using the structural equation model combined with the empirical data. The results show that the innovative culture, management flexibility, industrial cluster scale, innovation culture and the degree of internal fusion have a significant positive impact on the radical innovation. The conclusions of the study may provide reference and inspiration for enterprises to effectively implement improve radical innovation performance.
A Concise Social Network Representation with Flow Hierarchy Using Frequent Interactions

In this paper we introduce the flow hierarchy derived from frequent interactions to represent complex social networks in a concise way. Frequent interactions extract the impactful users while the flow hierarchy visualizes the dependencies and identifies different roles such as leaders, topic experts, information disseminators, emerging leaders and active followers. It is highly applicable in intelligent systems which involve user ranking, expert searching, recommendation, viral marketing, political campaigning, disaster management and many more. We present novel methods of deriving flow hierarchy considering the temporal dimension of interactions among users, flow directions and structural dependencies. We empirically evaluate proposed methods using real-world social network interaction datasets related to citation and retweet networks. Empirical analysis reveals that a hierarchy derived from frequent social interactions is effective in extracting the impactful users and their position in the network. Baseline results with user-centric measures show the efficacy of the proposed methods in finding a concise network representation.
LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese

In Vietnamese dependency parsing, several methods have been proposed. Dependency parser which uses deep neural network model has been reported that achieved state-of-the-art results. In this paper, we proposed a new method which applies LSTM easy-first dependency parsing with pre-trained word embeddings and character-level word embeddings. Our method achieves an accuracy of 80.91% of unlabeled attachment score and 72.98% of labeled attachment score on the Vietnamese Dependency Treebank (VnDT).
Neurophysiological Effects of Dorsal Root Ganglion Stimulation (DRGS) in Pain Processing at the Cortical Level

Dorsal root ganglion stimulation (DRGS) has been used successfully against localized neuropathic pain. Nevertheless, the effects of DRGS on pain processing, particularly at the cortical level, remain largely unknown. In this study, we investigated whether positive responses to DRGS treatment would alter patients’ laser‐evoked potentials (LEP).
Relevance of Whole-Body Vibration Exercises on Muscle Strength/Power and Bone of Elderly Individuals

Beneficial effects are associated with whole-body vibration exercises (WBVEs). Increases in muscular strength/power, flexibility, and gait speed; improvements in bone mineral density, balance, and the quality of life; and decreased pain and risk of falls are reported. The aim is to present a review about the importance of WBVE for elderly individuals, considering clinical studies and meta-analyses, on bone and muscle strength/power. There is evidence supporting beneficial effect of WBVE in postmenopausal women (PW); however, effects in PW with osteoporosis are unclear. Age-related decrease in muscle mass and function contribute to undesirable health conditions, including death risk. The WBVEs improve muscle strength/power, functional independence measure, balance, and various fall risk factors, and mobility, measured by Timed Up and Go test, increased significantly after WBVE. An explanation for the absence of positive effects in some outcomes could be related to discrepancies in WBVE protocols as well as the populations tested. It is concluded that WBVE is effective for counteracting the loss of muscle strength associated with sarcopenia in elderly individuals. Balance and leg and plantar flexor strength improvements due to WBV indicate benefit to reduce risk and incidence of falls, frailty, and fracture risks. However, long-term feasibility of WBVE for musculoskeletal and bone health in elderly individuals needs further investigation.
Wearable System for Sensory Substitution for Prosthetics

This paper presents a wearable system prototype based on commercial off-the-shelf components for sensory substitution for prosthetics. It provides a wireless portable interface electronics for tactile sensor array. The system transmits the acquired tactile information to the prosthetic user through Electrotactile stimulation. An experimental setup has been carried out to validate the proposed prototype: results demonstrate the correct functionality when different input stimuli have been tested and conveyed to the stimulator. The proposed system overcomes similar state of art solutions dealing with higher number of input channels maintaining the real time functionality.
Digital Calibration of Low-Voltage and Low-Power Analog ICs

In this paper, a digital calibration method for analog integrated circuits in nanotechnologies is proposed. Calibration was applied to a variable-gain amplifier (VGA), and it was aimed at the input offset voltage. The first part of the paper brings motivation for our research. Consequently, fundamentals of the proposed method and its implementation in 130 nm CMOS technology node are explained. The whole system operates with the supply voltage of 0.6 V. Then, verification results obtained mainly by simulations and partly obtained by the measurement of prototype chips. In all simulations, Monte Carlo analysis was used in the temperature range from −20°C to 60°C. With the use of calibration, the input offset voltage distribution mean value ranges between $299\mu V$to $413\mu V$, and standard deviation ranges between $356\mu V$ - to $802\mu V$. The measurement results of the VGA without calibration shows, that input offset voltage distribution over 60 samples reaches mean value of $403\mu V$ and standard deviation of 3.45 mV.
Lactose intolerance but not lactose maldigestion is more frequent in patients with irritable bowel syndrome than in healthy controls: A meta‐analysis

Irritable bowel syndrome (IBS) affects 10%‐20% of the adult population and is characterized by abdominal symptoms without relevant organic disease. There are numerous clinical trials available investigating the relationship between IBS, lactose maldigestion (LM), and lactose intolerance (LI), but there have been no meta‐analyses on this topic yet. We aimed to assess the prevalence of LM, objective and subjective (self‐reported) LI in IBS patients compared to healthy controls (HC) without IBS.
Building a Named Entity Annotated Bilingual English-Vietnamese Corpus

Bilingual corpora play an essential role in Cross-lingual Natural Language Processing tasks such as Machine Translation, Information Extraction, and Information Retrieval. Nevertheless, manually building such corpora is a time-consuming and expensive task, especially for resource-limited languages like Vietnamese. In this paper, we propose a symmetric Named Entity Alignment method to automatically construct a named entity annotated bilingual English-Vietnamese corpus. Our system uses expansion heuristics for candidate generation and bilingual features for candidate selection. The proposed system outperforms the state-of-the-art method in English-Vietnamese Named Entity Alignment, increasing F1-score from 82.68% to 86.57%. Moreover, Vietnamese Named Entity Recognition performance improves by 23.41% in terms of F1-score compared to Conditional Random Field model (StanfordNER). Our method can be generalized to develop corpora in other resource-limited languages.
Holographic acoustic tweezers

Significance Holographic optical tweezers use focused light to manipulate multiple objects independently without contact. They are used in tasks such as measuring the spring constant of DNA, the pulling force of the kinesin protein, or to trap matter in exotic states. Differently, acoustic tweezers use sound radiation forces to trap particles at a larger scale (i.e., from micrometers to centimeters). However, previous implementations did not provide individual control. We present the realization of holographic acoustic tweezers. Using an array of sound emitters, we engineer the generated sound field to manipulate multiple particles individually. This enables applications in contactless assembly both at the micrometer and centimeter scale as well as the creation of displays in which the pixels are levitating particles. Acoustic tweezers use sound radiation forces to manipulate matter without contact. They provide unique characteristics compared with the more established optical tweezers, such as higher trapping forces per unit input power and the ability to manipulate objects from the micrometer to the centimeter scale. They also enable the trapping of a wide range of sample materials in various media. A dramatic advancement in optical tweezers was the development of holographic optical tweezers (HOT) which enabled the independent manipulation of multiple particles leading to applications such as the assembly of 3D microstructures and the probing of soft matter. Now, 20 years after the development of HOT, we present the realization of holographic acoustic tweezers (HAT). We experimentally demonstrate a 40-kHz airborne HAT system implemented using two 256-emitter phased arrays and manipulate individually up to 25 millimetric particles simultaneously. We show that the maximum trapping forces are achieved once the emitting array satisfies Nyquist sampling and an emission phase discretization below π/8 radians. When considered on the scale of a wavelength, HAT provides similar manipulation capabilities as HOT while retaining its unique characteristics. The examples shown here suggest the future use of HAT for novel forms of displays in which the objects are made of physical levitating voxels, assembly processes in the micrometer and millimetric scale, as well as positioning and orientation of multiple objects which could lead to biomedical applications.