Miracle-dz/UniFAR-FTUs huggingface dataset:
Transposon dynamics in the emerging oilseed crop Thlaspi arvense

Genome evolution is partly driven by the mobility of transposable elements (TEs) which often leads to deleterious effects, but their activity can also facilitate genetic novelty and catalyze local adaptation. We explored how the intraspecific diversity of TE polymorphisms is shaping the broad geographic success and adaptation capacity of the emerging oil crop Thlaspi arvense. We achieved this by classifying the TE inventory of this species based on a high-quality genome assembly, age estimation of retrotransposon TE families and a comprehensive assessment of their mobilization potential. Our survey of TE insertion polymorphisms (TIPs) captured 280 accessions from 12 regions across the Northern hemisphere. We quantified over 90,000 TIPs, with their distribution mirroring genetic differentiation as measured by single nucleotide polymorphisms (SNPs). The number and types of mobile TE families vary substantially across populations, but there are also shared patterns common to all accessions. We found that Ty3/Athila elements are the main drivers of TE diversity in T. arvense populations, while a single Ty1/Alesia lineage might be particularly important for molding transcriptome divergence. We further observed that the number of retrotransposon TIPs is associated with variation at genes related to epigenetic regulation while DNA transposons are associated with variation at a Heat Shock Protein (HSP19). We propose that the high rate of mobilization activity can be harnessed for targeted gene expression diversification, which may ultimately present a toolbox for the potential use of transposition in breeding and domestication of T. arvense.
Nonuniform exponential dichotomies and admissibility

In this paper we consider the relation between the notions of exponential stability and admissibility, in the general context of nonuniform exponential behavior. In particular, we show that with respect to certain adapted norms related to the nonuniform behavior, if any $L^p$ space, with $p\in(1,\infty]$, is admissible for a given evolution process, then this process is a nonuniform exponential dichotomy. In addition, for each nonuniform exponential dichotomy we provide a collection of admissible Banach spaces, also defined in terms of the adapted norms.
Scalar Implicature in Chinese Child Language:An Experimental Study

Neo-Griceanism holds that,scalar terms like quantifiers and numerals have a lower bounded semantics,while in communication an upper bounded pragmatic meaning — scalar implicature is added,yielding a bilaterally bounded reading.However,an investigation into 30 Chinese children's comprehension reveals that,while they favor a lower bounded interpretation for quantifiers,the preschool subjects do comprehend numerals in an adult-like way,i.e.,the exact reading of numerals is favored by both adults and children.The results of the present study suggest that numerals may not be of a scalar type,and they don't possess a lower bounded semantics as Horn(1972) and Levinson(1983,2000) proposed.
The Sociocultural Context of Borderline Traits

Ongoing research on the prevalence of borderline personality disorder (BPD) has suggested that there is a similar prevalence rate across different racial groups and gender identities. Less is known about the intersection of race and gender in relation to the presentation of borderline traits across the diagnostic continuum. The present study sought to address this gap in the literature by investigating the impact of race and gender on the expression of borderline traits across the continuum of borderline personality disorder. In order to accomplish this, the present study tried to a) replicate the findings of De Genna and Feske (2013) in a sample of White and Black women, b) expand those findings to a sample of White and Black men, and c) explore the potential for a three-way interaction as an explanation for previous findings. A total of 132 participants (n = 33 male and n = 99 female) identifying as either Black (n = 55) or White (n = 74) participated in this online, cross-sectional study. Participants completed a series of questionnaires to determine differences in externalizing and internalizing symptoms that commonly co-occur with borderline traits across various racial and gender identities. The findings revealed that the effects of borderline traits vary based on gender and race for some externalizing traits (overall aggression, physical aggression, and verbal aggression). Results indicate that racial and gender differences should be considered during the diagnostic process for BPD. Future research should investigate the nuanced social mechanisms behind the observed differences.
Robust nonlinear adaptive observer design using dynamic recurrent neural networks

A robust adaptive observer for a class of nonlinear systems is proposed based on a generalized dynamic recurrent neural networks (DRNN), which does not require off-line training phase. The observer stability and boundedness of the state estimates and NN weights are proven. No exact knowledge of the nonlinear matching uncertain function, such as output matching or linear-parameterized condition in the observed system, are assumed. Simulation results show the effectiveness of the proposed DRNN observer.
On improving the least squares Monte Carlo option valuation method

This paper studies various possible approaches to improving the least squares Monte Carlo option valuation method. We test different regression algorithms and suggest a variation to estimating the option continuation value, which can reduce the execution time of the algorithm by one third. We test the choice of varying polynomial families with different number of basis functions. We compare several variance reduction techniques, and find that using low discrepancy sequences can improve the accuracy up to four times. We also extend our analysis to compound and mutually exclusive options. For the latter, we propose an improved algorithm which is faster and more accurate.
Detection of Web-Attack using DistilBERT, RNN, and LSTM

The rise in usage of the Internet has tremendously helped those who use web applications. Web-based applications are becoming more susceptible to numerous security risks and network vulnerabilities as online attacks continue to develop. Malicious code or contents could be embedded in requests from HTTP causing attacks like SQL injections etc.In this research, an online intrusion detection system is presented to tackle the rise in web application attacks. Our web intrusion detection system uses a Distil-BERT, RNN, and LSTM model to identify attacks with body, URL, and User-data. The experimental findings demonstrate that our model successfully classifies the attacks with body, URL, and user data with a 94% accuracy.
Towards A Unique Online Healthcare Information (OHI) Recommender: A Preliminary Case Study

As online healthcare consumerism grows, more and more internet users tend to rely upon web contents hosted by different online healthcare service providers and third-party websites, for making healthcare service related decisions, like choosing providers. With an increasing number of healthcare consumers browsing provider information online and using that data to drive decisions, trust implications entailing such actions become a relevant and significant point of discussion. Currently there are no standards or benchmarks in relation to assessing the reliability of online healthcare information (OHI), including provider profiles and web-based explicit contents, plus online website services for finding providers. In this paper, we present a unique approach towards designing an online recommender tool for verifying OHI that applies information assurance to web contents and acts as an advisor for both users and providers. We introduce our multi-dimensional trust model that drives our OHI recommender, which accounts for multi-layered trust antecedents, like security, assurance, social presence, verification, reputation, and familiarity. We describe our preliminary proof of concept (POC) prototype implementation of the OHI recommender, and discuss how it works, including how it generates an overall trust score for a particular OHI web page. We demonstrate how our OHI recommender can be used for advising on how to improve the online contents for achieving a higher trust assessment, and for better meeting user trust expectations. We report the results obtained by testing our POC prototype of the OHI recommender on selected OHI provider websites. Through this work, we look to aid more reliable design of online healthcare profiles, that can win consumer trust by providing information assurance and insights.
A Novel System For Blind, Deaf And Dumb People Assistant Using Raspberry Pi

In this world, the way of conveying information between deaf, dumb, and blind people and normal people is very difficult. It is very difficult to share their thoughts and information with others without any assistive devices. In the available scheme, the Arduino microcontroller is used to do some specific operations. It had some disadvantages, such as compiling time, execution time, and the microcontroller doesn't process speech to text conversion processes. In order to overcome the drawbacks faced in the available scheme, Raspberry Pi and sensors are used in the proposed scheme. With the help of the speech-to-text conversion process, deaf people can easily understand what normal people say. Also, there is no fear of traffic for blind people with the help of ultrasonic sensors, and with the accelerometer sensor, dumb people can easily convey their messages to normal people.
BinaryRTS: Cross-language Regression Test Selection for C++ Binaries in CI

Continuous integration (CI) pipelines are commonly used to execute regression tests before pull requests are merged. Regression test selection (RTS) aims to reduce the required testing effort and feedback time for developers. However, existing RTS techniques are imprecise for tests with cross-language links to compiled C++ binaries or unsafe if tests use external files. This is problematic because modern software in fact involves several programming languages and (non-)code artifacts such as configuration files. In this paper, we present BinaryRTS, a novel RTS technique that leverages dynamic binary instrumentation to collect the covered functions and accessed external files for each test. BinaryRTS then selects tests depending on changes issued to C++ binaries or external (non-)code artifacts. When evaluating BinaryRTS in our large-scale industrial context, we are able to exclude on average up to 74% of tests without missing real failures. We release BinaryRTS as the first publicly available RTS tool for software involving C++ code.
Symmetry and non-existence of solutions for a nonlinear system involving the fractional Laplacian

In this paper, we consider the following system of pseudo-differential nonlinear equations in $R^n$ \begin{equation} \left\{\begin{array}{ll} (-\Delta)^{\alpha/2} u_i (x)= f_i( u_1(x), \cdots u_m(x)), & i=1, \cdots, m, \\ u_i \geq 0 , & i=1, \cdots, m, （1） \end{array} \right. \label{b1} \end{equation} where $\alpha$ is any real number between $0$ and $2$. We obtain radial symmetry in the critical case and non-existence in the subcritical case for positive solutions. To this end, we first establish the equivalence between (1) and the corresponding integral system $$ \left\{\begin{array}{ll} u_i(x) = \int_{R^n} \frac{c_n}{|x-y|^{n-\alpha}} f_i( u_1(y), \cdots, u_m(y)), & i=1, \cdots, m, \\ u_i(x) \geq 0, & i=1, \cdots, m. \end{array} \right. $$ A new idea is introduced in the proof, which may hopefully be applied to many other problems. Combining this equivalence with the existing results on the integral system, we obtained much more general results on the qualitative properties of the solutions for (1).
Software Testing Research Challenges: An Industrial Perspective

There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. In this paper we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.
Authors and Collaborator Groups Ranking Analysis on SYNASC using Centrality Measures

The constant increase of published papers and participating authors in conferences has risen the interest in studies related to conference and co-authorship analysis techniques. There are numerous different studies in the literature, which offer distinct perspectives regarding the author ranking, impact, collaborations and research interests through various analysis techniques. In our research, we are introducing a new collaborator group ranking analysis based on four different centrality measures, using published papers data from the SYNASC conference during the 2005-2021 period, with which we construct dynamic co-authorship networks. By using the proposed approach, we aim to study collaboration properties at micro-level, which could also determine the impact of the authors and collaborator groups on the conference itself. The results which we obtained are highlighting that some of the authors and collaborator groups excelled in multiple ranking types, by having a constant top ranking, thus also suggesting a significant overall impact, developed over time, on the conference.
Stochastic invertibility and related topics

I this work we investigate a notion of stochastic invertibility on Wiener space. Rougghly speaking a morphism of probability spaces with values on the Wiener space, which is further adapted, can be canonically associated to the laws of the solutions to some stochastic differential equations. One of the main properties of this morphism is to be invertible (i.e. to be an isomorphism of probability spaces) if and only if the underlying stochastic differential equation has a unique strong solution. Since it may be seen as a Brownian motion, we cal it the Brownian transform of the associated law, and we will study the invertibility of this Brownan transform. We will see that this notion, whose origins may be found in earlier results related to stochastic mechanics, extends and enlightens the notion of invertibility of adapted shifts on Wiener space which was investigated by Ustunel and Zakai in their recent papers, where this notion already appears clearly between the lines. Moreover, from the origin many problems arising in various fields are deeply related to this notion. This opens to a wide spectrum of applications, some of them being very concrete. We will investigate problems of various origins such as statistical physics, information theory, filtering, but also stochastic control and optimal transport. For instance, we will prove a very general result of pathwise uniqueness for the stochastic picture of euclidean quantum mechanics, and we will extend Shannon’s inequality to any abstract Wiener spaces. We also show how this notion of invertivility fits naturally in stochastic differencial geometry.
Design of a 60 GHz Active Quasi-Circulator in 22nm FDSOI Process

In this work, the design of an active quasi-circulator at 60 GHz ISM band in 22 nm FD-SOI technology is presented. A divider-combiner based circulator is designed to obtain low noise figure, high isolation and high power handling. An in-phase divider and out-of-phase combiner based quasi-circulator is designed with impedance tuner and phase tuner. Here, the common source impedance tuner and capacitive tuner are implemented to improve the isolation between the transmitter and receiver. Power handling is measured in terms of the 1 dB compression point. A noise figure of 6.5 dB, power handling of 8 dBm and an isolation of 22 dB is obtained. The power consumption is measured to be 6 mW.
Signal Integrity Analysis of the Structure with Single Modal Reservation Before and After Failures

To improve the reliability of radio electronic devices, engineers use redundancy of the most relevant circuits. The simplest and most common solution is classical cold redundancy in which the primary and redundant circuits are located far from each other. Meanwhile, locating the primary and redundant circuits close together makes it possible to use modal distortions to protect both circuits from unwanted ultra-wideband interference. This approach is known as modal redundancy or modal reservation (MR). This paper presents the results of signal integrity analysis for a structure with single MR before and after failures. It is shown that the bandwidth after failures changes. This affects the rate of data that can be excited to the input of the device without significant distortion of the useful signal. The authors used pseudorandom binary sequences with a data rate of 50 and 200 Mbps. It is experimentally shown that failures in the redundant circuit do not significantly degrade the quality of signal integrity in the bandwidth. Thus, the data-dependent jitter changes from 12 (before failures) to 19 ps (after the worst failure). In all investigated variants, the eye stays open; as a result, the bit error rate will be low. The mismatch of the redundant circuit has the largest effect on the noise amplitude. As a result, the signal-to-noise ratio decreases from 57 (before failures) to 28 (after the worst failure). To confirm the measurements, we performed electrodynamic simulations. Their results are in good agreement with the experimental data.
Semantically Rich Differential Access to Secure Cloud EHR

Existing Cloud-based Electronic Health Record (EHR) services face challenges in handling heterogeneous data and maintaining performance with large records since they often use a relational database or only partially store information in a graph database. We have developed a novel approach that allows fine-grained field-level security for Cloud EHRs to protect patient privacy and data security. Our graph-based EHR has been developed by integrating Attribute-based Encryption (ABE) with ontology reasoning using Semantic Web technologies. The novelty of our approach lies in providing differential access to an EHR by using a comprehensive knowledge graph that stores all medical data as encrypted nodes, thereby handling heterogeneous patient data while preserving good performance. In this paper, we describe our system in detail, along with the results demonstrating that the system maintains consistent data retrieval performance with different data sizes and allows real-time updates on the data while supporting queries.
Batching Non-Conflicting Mutations for Efficient, Safe, Parallel Mutation Analysis in Rust

Rust is a relatively young, memory safe systems programming language which is increasingly being adopted by projects requiring both performance, and safety. While automated testing is built into the language, tool support for mutation analysis is almost non-existent, having not been the subject of past research. This leaves Rust developers without a way to determine test thoroughness. To address this problem, we design a mutation analysis process for Rust that overcomes challenges related to generating viable mutations due to the strictness of the language in terms of its type system, memory restrictions, and the potential to introduce undefined behavior in unsafe code blocks. Our technique efficiently evaluates mutations simultaneously through a process we refer to as "batching" — the use of static analysis to determine mutations that are non-conflicting, and therefore are able to be evaluated together. Batching enables our technique to maximize thread usage, executing more tests in parallel, and further reducing the time required to evaluate mutations. We implemented these techniques into a tool, mutest-rs, which we empirically evaluated on a diverse set of common subject libraries and Rust programs, and found that our batching method for increasing parallelism is able to reduce the overall runtime of mutation analysis by up to 66.4%, compared to not applying batching.
Deep-Learning Based Classification of Engagement for Child-Robot Interaction

This work aims to describe the research and development of an engagement classification system based on emotion recognition through the use of facial expressions, to be used in robotic applications for children interacting with a NAO humanoid robot during therapy sessions. An emotion classification model based on different Convolution Neural Networks (CNN) architectures was first fed by faces provided from different public datasets to train and validate the emotion classification models. Two engagement classification methodologies are proposed: Method-1 first classifies 7-classes of emotions, using the ConvNeXt network, and then infers the levels of engagement based on the affective model developed by [1]; Method-2 employs the same classification network to directly classify the four levels of engagement. The results obtained for both engagement classification methodologies using the CAFE dataset proved their effectiveness, however Method-1 performs slightly better in terms of engagement classification accuracy. A novel child-robot activity with a NAO humanoid robot, that consists in a breathing-based relaxation exercise, is also proposed and the results of offline classification system showed to be promising, and with potential to be applied in psychology therapies in the future.
CHI2CV : Feature Selection using Chi-Square with Cross-Validation for Intrusion Detection System

The internet technology has become essential needs among communities. However, this digital era threatens the security of important data and information. There are irresponsible parties that attempt to send intrusion attacks on computer networks. So, we need a security system, like Intrusion Detection System (IDS). Unfortunately, the attack types are getting more and more fast growing. There needs to be an effective intrusion detection model with a high degree of accuracy, namely by selecting the best features that can be used for the attack detection process. The feature selection method that we propose is the use of the chi-square test to determine which features are the most irrelevant to be discarded. Then from the remaining features, we need to choose the best features that can optimize the performance of attack detection on computer networks. For this reason, this research proposes a combination of chi-square and cross validation. The experimental results indicate that this proposed method has a significant impact on increasing the accuracy of detection of attacks on the network, from 95.51% to 96.70%.