Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Surprising Facts in Wikidata

To expand their boundary of knowledge, to solve a certain task, or merely to entertain themselves, people on the Web often hunt for surprising facts. While large repositories of structured knowledge like Wikidata hold the promise to provide surprising facts to their users and developers, currently no mechanism exists to identify surprising facts in Wikidata. In this paper, we study the ability of popular embedding models to estimate the surprise level of a fact in Wikidata. We formulate a novel task of Surprising Fact Identification , and we create two associated benchmarks: Trivia and Survey . We propose two scalable methods based on outlier detection and link prediction to estimate surprise scores for any statement in a graph like Wikidata. We evaluate our methods with various embedding models on the two benchmarks. We perform further analysis of the predictions for outlier and non-outlier facts to investigate to what extent link prediction models regress to the mean.
TransHExt: a Weighted Extension for TransH on Weighted Knowledge Graph Embedding

Many methods have been proposed for embedding ordinary knowledge graphs (KG), which greatly promote various applications. However, current knowledge graph embedding algorithms cannot encode weighted knowledge graphs (WKG), which are a generalized form of the ordinary KG. This paper gives a promising approach that enables to extend existing models to encode weighted knowledge graphs. Taking TransH as an example, we propose TransHExt. TransHExt shows competitive performance in both link prediction task and weight prediction task.
On the Rabinowicz like criterion of formation of wear particles in a system with a soft surface layer

In 1958, Ernest Rabinowicz suggested a simple criterion distinguishing the regimes of plastic smoothing and formation of wear particles in a contact of homogeneous sliding bodies. However, he did not consider any detailed mechanism of either plastic smoothing or debris formation. In a recent paper in Nature Communications, Molinari et al. have confirmed the criterion using explicit mesoparticle simulation. The work of Molinari's group provides a strong support to the general concept of Rabinowicz which is based on the consideration of competition of plastic deformation and fracture. It is interesting to apply this concept to more general configurations than those considered by Rabinowicz and Molinari's group. An important case is a system having a very soft surface layer. In the present paper, the analysis similar to that of Rabinowicz is applied to materials with soft layer. It is shown that in this case, too, both cases of plastic deformation and debris formation may occur. It would be interesting to verify this prediction by explicit meso-particle simulations similar to those of Molinari's group.
FS2KG: From File Systems to Knowledge Graphs (Demo)

The tree-structured and semantics-neutral approach of file systems is the dominant method for organizing information, decades now. In this paper we elaborate on the following two questions: (a) can a file system structure be benefited by a Knowledge Graph (KG), (b) can the construction of a KG be facilitated by the file system? To this end we propose an automatic method for producing KGs from folder structures, which can be configured through small, and easy to write, configuration files that can be placed in the desired folders to guide the KG construction. We present FS2KG , an implementation of the approach. The approach can facilitate the rapid creation of KGs, as well as various file system related tasks.
Non-conventional screening of the Coulomb interaction in low-dimensional and finite-size systems

We study the screening of the Coulomb interaction in non-polar systems by polarizable atoms. We show that in low dimensions and small finite-size systems this screening deviates strongly from that conventionally assumed. In fact in one dimension the short-range interaction is strongly screened and the long-range interaction is anti-screened thereby strongly reducing the gradient of the Coulomb interaction and therefore the correlation effects. We argue that this effect explains the success of mean-field single-particle theories for large molecules.
Theory of the field-effect mobility in amorphous organic transistors

The field-effect mobility in an organic thin-film transistor is studied theoretically. From a percolation model of hopping between localized states and a transistor model an analytic expression for the field-effect mobility is obtained. The theory is applied to describe the experiments by Brown et al. [Synth. Met. 88, 37 (1997)] on solution-processed amorphous organic transistors, made from a polymer (polythienylene vinylene) and from a small molecule (pentacene). Good agreement is obtained, with respect to both the gate voltage and the temperature dependence of the mobility.
A 100 Liter Capacity Solar Water Heater for a Household of Three Individuals

Global energy consumption has increased steadily over the last few decades and has recently been marked by especially dramatic growth rates in many developing countries, such as China, India, and Brazil. Heating accounts for 40% to 50% of the world’s energy demand and most of the energy supply for heating currently comes from fossil fuels. To reduce the consumption of fossil fuels for heating and the emission of greenhouse gases, solar energy has been accepted as one of the most promising alternative energy sources because it is free and environmentally clean. One of the most widely-known solar thermal applications is the solar water heating (SWH) system. According to the Renewable Energy Policy Network (2010), approximately 70 million houses use SWH systems worldwide. Namely, SWH systems have been recognized as one of the most cost-effective systems to many engineers, investors, and users. The proper design of SWH systems is important to assure good performance and maximize the economic benefits of these systems. This experiment achieved rising the cold water temperature from 24°C to 51°C which is a significant increase. The ambient temperature varied from 24°C to 28°C. The outlet temperature of the heated water varied from 23°C from 8 am to 42°C at 6 pm on the same day. It was observed during the experiment that varying the color of the collector plate from red to black has great effect in the outlet temperature. As the Insolation increased from 10am to 3pm the outlet temperature of the water increased.
Statistical Signatures of Nanoflare Activity. I. Monte Carlo Simulations and Parameter-space Exploration

Small-scale magnetic reconnection processes in the form of nanoflares have become increasingly hypothesized as important mechanisms for the heating of the solar atmosphere, driving propagating disturbances along magnetic field lines in the Sun’s corona, and instigating rapid jetlike bursts in the chromosphere. Unfortunately, the relatively weak signatures associated with nanoflares place them below the sensitivities of current observational instrumentation. Here we employ Monte Carlo techniques to synthesize realistic nanoflare intensity time series from a dense grid of power-law indices and decay timescales. Employing statistical techniques, which examine the modeled intensity fluctuations with more than 107 discrete measurements, we show how it is possible to extract and quantify nanoflare characteristics throughout the solar atmosphere, even in the presence of significant photon noise. A comparison between the statistical parameters (derived through examination of the associated intensity fluctuation histograms) extracted from the Monte Carlo simulations and Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 171 and 94 Å observations of active region NOAA 11366 reveals evidence for a flaring power-law index within the range of 1.82 ≤ α ≤ 1.90, combined with e-folding timescales of 385 ± 26 and 262 ± 17 s for the SDO/AIA 171 and 94 Å channels, respectively. These results suggest that nanoflare activity is not the dominant heating source for the active region under investigation. This opens the door for future dedicated observational campaigns to not only unequivocally search for the presence of small-scale reconnection in solar and stellar environments but also quantify key characteristics related to such nanoflare activity.
A Randomized, Dose-Finding, Proof-of-Concept Study of Berberine Ursodeoxycholate in Patients With Primary Sclerosing Cholangitis

INTRODUCTION: Primary sclerosing cholangitis (PSC) is a fibroinflammatory disease of the bile ducts leading to cirrhosis and hepatic decompensation. There are no approved pharmaceutical therapies for PSC. Berberine ursodeoxycholate (HTD1801) is an ionic salt of berberine and ursodeoxycholic acid with pleiotropic mechanisms of action. METHODS: An 18-week proof-of-concept study was conducted to assess the safety and efficacy of HTD1801 in PSC. This study had three 6-week periods: (i) a placebo-controlled period, (ii) a treatment extension period, and (iii) a randomized treatment withdrawal period. The primary end point was change from baseline in alkaline phosphatase (ALP) at week 6. RESULTS: Fifty-five patients were randomized and treated; 35 (64%) had inflammatory bowel disease and 22 (40%) had previously received ursodeoxycholic acid. Patients were initially randomized to placebo (n = 16), HTD1801 500 mg BID (n = 15), or HTD1801 1000 mg BID (n = 24). At baseline, mean (range) ALP values were 414 U/L (138–1,048), 397 U/L (237–773), and 335 U/L (122–882) for the placebo, HTD1801 500 mg BID, and HTD1801 1,000 mg BID groups, respectively. At week 6, a significant decrease in ALP was observed with HTD1801 (least square mean; HTD1801 500 mg BID = −53 U/L, P = 0.016; HTD1801 1000 mg BID = −37 U/L, P = 0.019) compared with placebo (98 U/L). ALP reductions were sustained through week 18 in those who remained on therapy, whereas ALP increased in those who crossed over to placebo during period 3. HTD1801 was generally well tolerated; 4 patients experienced serious adverse events, none attributed to HTD1801. DISCUSSION: HTD1801 is associated with significant improvement in ALP and warrants further study as a treatment for PSC.
An Architecture Supporting Real-Time and Retrospective Environmental Data Management

The Real-Time Environmental Information Network and Analysis System (REINAS) is a distributed database environment supporting both real-time and retrospective regional scale environmental science. Continuous real-time data is acquired from dispersed sensors and input to a logically integrated but physically distributed database. An integrated problem-solving environment supports visualization and modeling by users requiring insight into historical, current, and predicted oceanographic and meteorological conditions. REINAS supports both collaborative and single-user scientific work in a distributed environment. The goals and design of key data management aspects of REINAS are described
Blame and Praise: Responsibility Attribution Patterns in Decision Chains

How do people attribute responsibility when an outcome is not caused by a single person but results from a decision chain involving several people? We study this question in an experiment, in which five voters sequentially decide on how to distribute money between them and five recipients. The recipients can reward or punish each voter, which measures responsibility attribution. In the aggregate, we find that responsibility is attributed mostly according to the voters’ choices and the pivotality of the decision, but not for being the initial voter. On the individual level, we find substantial heterogeneity with three overall patterns: Little to no responsibility attribution, pivotality-driven, and focus on choices. These patterns are similar when praising voters for good outcomes and blaming voters for bad outcomes.
Characterization of Unstable Pixels Using a Mixture Model: Application to HST WFC3 IR

Many IR datasets are taken with two dithers per filter, complicating the automated recognition of pixels with unstable response. Much data from the HST cameras NICMOS and WFC3 IR fall into this category, and future JWST and WFIRST data are likely to as well. It is thus important to have an updated list of unstable pixels built from many datasets. We demonstrate a simple Bayesian method that directly estimates the fraction of the time the output of each pixel is unstable. The last major update for WFC3 IR was a 2012 instrument science report (ISR WFC3 2012-10, Hilbert 2012), so we compute a new list. Rather than reproduce the old analysis on newer data, we use our new method. By visual inspection, our method identifies unstable pixels with better purity and completeness.
Towards a Block-Level ML-Based Python Vulnerability Detection

: Computer software is driving our everyday life, therefore their security is pivotal. Unfortunately, security ﬂaws are common in software systems, which can result in a variety of serious repercussions, including data loss, secret information disclosure, manipulation, or system failure. Although techniques for detecting vulnerable code exist, the improvement of their accuracy and effectiveness to a practically applicable level remains a challenge. Many existing methods require a substantial amount of human experts labor to develop attributes that indicate vulnerabilities. In a previous work, we have shown that machine learning is suitable for solving the issue automatically by learning features from a vast collection of real-world code and predicting vulnerable code locations. Applying a BERT-based code embedding, LSTM models with best hyperparameters were able to identify seven different security ﬂaws in Python source code with high precision (average of 91%) and recall (average of 83%). Upon the encouraging ﬁrst empirical results, we go beyond in this paper and discuss the challenges of applying these models in practice and outlining a method that solves these issues. Our goal is to develop a hands-on tool for developers that they can use to pinpoint potentially vulnerable spots in their code.
Political Communication Patterns through Social Media: A Case of an Indonesian Presidential Staff Twitter Account

Given the current technological developments, social media has become a necessity and a new tool that can complement services. Social media can influence the understanding of political communication and its impact on the public. Every element of society uses social media for various purposes, including the government. One who is quite active on Twitter and has repeatedly drawn controversy is the Presidential Chief of Staff, Retired General Moeldoko. The research investigates the pattern of political communication carried out by the Presidential Chief of Staff by focusing on his Twitter account and using the components of the effectiveness of political communication and media effects. The research applies a qualitative approach supported by the NVivo 12 Plus software. The data are collected using the NCapture for NVivo feature on the Presidential Chief of Staff’s Twitter account (@Dr Moeldoko). The results show that Moeldoko’s communication through Twitter is generally for the media or fellow government agencies. His communication is also a way to provide information about government programs. In addition, the research also finds that the alleged ineffectiveness in Twitter management is based on a decrease in account activity. It can be concluded that his pattern of communication is general, vertical, and not participatory.
Source Selection for SPARQL Endpoints: Fit for Heterogeneous Federations of RDF Data Sources?

To answer queries over a federation of multiple data sources, a preliminary task is source selection; i.e., identify the federation members that can answer each part of the query and decompose the query into subqueries assigned to each federation member. Existing source selection approaches for federations of RDF data sources have been developed based on the assumption that the federation member are SPARQL endpoints. This paper presents an analytical study that investigates whether these approaches are still effective in the context of federations that are heterogeneous in terms of the types of data access interface. In particular, we identify what information about the data of the federation members is required by the approaches and analyze the possibilities and the effort of obtaining this information via the different types of data access interfaces. We find that almost all existing source selection approaches can be adopted for heterogeneous federations but obtaining the required information may not be practical.
Integrative Bioinformatics Analysis of Transcriptomic Data Reveals Hub Genes as Diagnostic Biomarkers for Non-Muscle vs. Muscle Invasive Bladder Cancer

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high socioeconomic impact. BCa can manifest in the form of nonaggressive and usually non-muscle invasive (NMIBC) tumors that recur and require chronic invasive surveillance, or aggressive and muscle invasive (MIBC) tumors with high associated mortality. These two subtypes exhibit distinct prognosis and require different therapeutic approaches. In the present study, we conducted an integrative bioinformatics analysis, combining transcriptomic data from various microarray experiments, in order to reveal a common signature of differentially expressed genes (DEGs) between the two subtypes. Subsequently, we constructed the protein-protein interaction (PPI) network of the DEGs and defined the hub genes based on 11 topological analysis methods. Then, the most significant hub genes were identified using LASSO logistic regression algorithm. The selected genes were finally used as features in supervised classification algorithms, namely support vector machines and random forests, for BCa subtype discrimination. The models' evaluation showed area under the curve (AUC) values up to 96% as regards separating NMIBC from MIBC tumors. Genes driving the separation between tumor subtypes may prove to be important biomarkers for BCa development and progression, and eventually candidates for therapeutic targeting.
Novel optimized quantitative trading strategy based on wavelet denoising and LSTM

Due to its unique advantages, quantitative investment is playing an increasingly important role in today's global financial market. With the continuous development of deep learning algorithms and quantitative trading technology, the combination of deep learning and quantitative trading has important research value and wide application requirements. Today, when the digital currency is hot, this paper selects Bitcoin as the transaction object, and innovatively adopts the wavelet denoising algorithm and the combination of the Long-Short-Term Memory (LSTM) (RNN with memory) deep learning model and the traditional quantitative model to establish quantitative transactions. Model. The model is both scientific, optimized and pioneering. The experimental results show that the 15-day window deep learning model has a significant optimization effect on the model of traditional quantitative investment, and is superior to other strategies in terms of strategy return and prediction accuracy. Through the Back-testing-model test, the simple annual ROI of the investment strategy is 738.01% during the trading cycle.
Nonlinear Contact Modeling and Haptic Characterization of the Ovine Cervical Intervertebral Disc

Haptic systems provide a sense of touch by applying forces and motions in order to create a real-world experience using computer simulations for educational and surgical purposes. Identification of realistic physical models and the advancement of simulators are essential for the effectiveness of haptic systems. In this study, a modified nonlinear Kelvin-Voigt model was developed to represent ovine intervertebral disc tissue as an approach to make the cervical spine surgical simulations and training practices more realistic. The proposed mechanical model was identified by three relaxation tests and three compressive tests with different constant velocities. A third-order polynomial function was employed to provide a tool-tissue interaction dynamic model. Finally, the closed-loop stability of the proposed model in an admittance-type virtual reality system suggested by previous literature was shown by simulation. Results approved that the adaptive controller using the admittance-type virtual environment could offer a high impedance contact for high-performance haptic applications.
Robust Self-Attention ConvLSTM-based Traffic Flow Prediction Model

Traffic flow forecasting has been receiving a lot of attention because of its important role in traffic control and management. Accurate traffic forecasting is critical to improving the performance of intelligent transportation systems. However, accurate traffic forecasting still faces the following challenges, including modeling the dynamics of traffic data along the temporal and spatial dimensions, significant differences in peak hour/peak hour traffic, and traffic flow data affected by partial noise. In this paper, we propose a hybrid and robust model with Self-Attention ConvLSTM networks and localized stochastic sensitive (LSS) for traffic flow prediction. The proposed model extracts features with long-range spatiotemporal dependencies with Self-Attention ConvLSTM. To further explore the long-term temporal features, we utilize LSTM module to extract daily and weekly periodic features as assistive features. The LSS reduces sensitivity to unseen samples around training samples and avoids large output fluctuations due to the noise or change of the data. Experiments on real traffic flow datasets show that the proposed method yields better prediction performance compared to other contrast methods.
Quasi-constant Phase Shifter by Stable Band-Limited Non-foster Circuits

A quasi-constant phase shifter enabled by band-limited non-Foster circuits is proposed in this paper. The proposed circuit provides wide band phase shifting with phase mismatch < 2.7°, amplitude mismatch < 1 dB over 10 GHz bandwidth, while supporting flexible phase adjustability. The band-limited non-Foster circuit structure is presented by using two side transistors to compensate the effects of cross-coupled transistors in Linvill’s structure [1], which shows positive capacitance at low frequencies and negative capacitance at high frequencies. To analyze the stability of band-limited non-Foster circuits and guide design optimization, Impulse Response Function (IRF) method is proposed, which takes into account circuit frequency dependent effects, distinct from conventional methods.
ReRobApp: A modular and open-source software framework for robotic rehabilitation and human-robot interaction

Physical therapy combined with robotic, electrical, and/or multimedia systems is a promising approach for the development of innovative, engaging, and more effective reha-bilitation tasks. To foster the development of new rehabilitation strategies and protocols, we propose a fully-customizable and open-source software framework named ReRobApp for robotic rehabilitation and other human-robot interaction applications. ReRobApp allows, e.g., an easy integration of different robotic devices and sensors in the same rehabilitation task, and the development of control strategies based on an block programming intuitive architecture. To showcase the versatility of our proposed framework, we performed case studies using serious games, functional electrical stimulation, electromyography, and two different lower limb robotic devices for ankle rehabilitation in a single multiplayer task with different subjects.