Miracle-dz/UniFAR-FTUs huggingface dataset:
Equivalent circuits for transformers based on one-dimensional propagation: accounting for multilayer structure of windings and ferrite losses

In a recent paper, we linked the electromagnetic power propagation in a two-winding transformer to an electrical equivalent circuit. This study pointed out that an electrical equivalent circuit can be associated with each layer (conductor, insulator, ferrite) passed through, and that connecting all of these elementary circuits together leads to the equivalent circuit of the entire component. Propagation was assumed to be one-dimensional, and each winding was supposed to be made of a single layer of turns. In this paper, we first investigate the consequences of the multilayer structure of each winding, following the above approach. Skin and proximity effects are taken into account this way, and theoretical results are compared with experimental data. Then, we focus on the representation of the linear behavior of the ferrite core. On the basis of an experimental approach, it is shown that an accurate equivalent circuit must include a capacitor. This is consistent with the high permittivity of the material.
Color Image Enhancement using Nonlinear Mapping in Color and Transform Domains

Image enhancement is, in fact, one of the most commonly used operations in image processing applications. Several methods, including transform domain based methods, have been proposed in the literature for this purpose. In this paper we propose a new algorithm for color image enhancement using a nonlinear mapping based on the truncated matrix exponential series in RGB color space and transform domains. For the transform domain enhancement operation, two dimensional transform of the given image is taken and the transformation coefficients are nonlinearly mapped using the truncated matrix exponential series formula. The resulting image is then inverse transformed to obtain the enhanced image. Simulation results of the proposed algorithm for different transforms such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), discrete Fourier transform (DFT) are also presented. The results show an improvement in the visual appearance of the images for each transform, but the color domain, DWT and DCT gives better results as compared to others transforms for images considered here in the paper. The results of non-linear mapping in RGB color space are also comparable to the results of DCT and DWT. The quantitative evaluations of the performance of different simulation results using different performance measures are also presented
A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.
Connectivity Evaluation of ESP32 in Outdoor Scenarios

ABSTRACTThe purpose of this paper is to evaluate WiFi (IEEE 802.11 b/g/n)performance of ESP32 modules in outdoor scenarios using an offthe-shelf access point (AP). This was done by measuring its receivedsignal strength indicator (RSSI) and application data transfer ratesat different locations of a relatively open area, prone to possiblesources of interference, in order to simulate a real-life usage situation.The results show that this setup is suitable for medium datarate applications at distances up to 300m from the wireless accesspoint. Based on the acquired RSSI values, the achievable data rateon the PHY layer was also estimated for each point based on thehardware documentation. Throughout the text, challenges facedduring the implementation of the system are shared, as well asthe solutions found, which will be useful for those who wish toreplicate the experiment.
Intervention to Increase Cervical Cancer Screening Behavior among Medically Underserved Women: Effectiveness of 3R Communication Model

Human Papillomavirus (HPV) self-sampling has the potential to increase Cervical Cancer Screening (CCS) and reduce the cervical cancer burden in Medically Underserved Women (MUW). However, interventions promoting self-sampling are limited. We examined the effectiveness of an intervention study in increasing CCS among MUW. We conducted a quasi-experimental intervention study. A face-to-face verbal approach was used to recruit MUW (n = 83, mean age 48.57 ± 11.02) living in a small city in the US. Behavioral intervention based on reframing, reprioritizing, and reforming (3R model) was used to educate the women about CCS in a group format. The women (n = 83) completed pre-and post-intervention assessments, and 10 of them were invited for follow-up interviews. The primary outcome was CCS uptake. Mixed methods analyses were conducted using a t-test for the primary outcome, PROCESS for mediation analysis, and NVivo for interview data. The majority of women (75%) completed self-testing. High-risk HPV among women was 11%, and of those, 57% followed up with physicians for care. We found that the significant increase in the women’s post-intervention screening behaviors was mediated by the increase in knowledge (Indirect Effect [IE] = 0.1314; 95% CI, 0.0104, 0.4079) and attitude (IE = 0.2167; 95% CI, 0.0291, 0.6050) scores, (p < 0.001). Interview analyses offered further explanations why MUW found the intervention messages acceptable (encourages proactive behavior), feasible (simple and easy to understand), and appropriate (helpful and informative). Barriers, including lack of trust and fear of results, were identified. The findings suggest that an intervention that combines the 3R model and self-sampling may increase CCS among MUW.
Study on efficiency of Sunday algorithm

Given the characteristic that the Sunday algorithm is too complex to construct Markov chains,a new method according to the difference of matching number in the algorithms was proposed to compute the average efficiency.Firstly,the naive algorithm was chosen as the foundation,and its accurate average efficiency was computed by Markov chains.Secondly,the difference of the two algorithms was computed by the corresponding knowledge in probability theory.The two results were combined to get the equation which represented the average efficiency of Sunday algorithm.The experimental results show that the estimated value computed by the equation is the average number of the matching times.
Thermal features of Heisenberg antiferromagnets on edge- versus corner-sharing triangular-based lattices: a message from spin waves

We propose a new scheme of modifying spin waves so as to describe the thermodynamic properties of various noncollinear antiferromagnets with particular interest in a comparison between edge- versus corner-sharing triangular-based lattices. The well-known modified spin-wave theory for collinear antiferromagnets diagonalizes a bosonic Hamiltonian subject to the constraint that the total staggered magnetization be zero. Applying this scheme to frustrated noncollinear antiferromagnets ends in a poor thermodynamics, missing the optimal ground state and breaking the local U(1) rotational symmetry. We find such a plausible double-constraint condition for spin spirals as to spontaneously go back to the traditional single-constraint condition at the onset of a collinear Néel-ordered classical ground state. We first diagonalize only the bilinear terms in Holstein-Primakoff boson operators on the order of spin magnitude S and then bring these linear spin waves into interaction in a perturbative rather than variational manner. We demonstrate specific-heat calculations in terms of thus-modified interacting spin waves on various triangular-based lattices. In zero dimension, modified-spin-wave findings in comparison with finite-temperature Lanczos calculations turn out so successful as to reproduce the monomodal and bimodal specific-heat temperature profiles of the triangular-based edge-sharing Platonic and corner-sharing Archimedean polyhedral-lattice antiferromagnets, respectively. In two dimensions, high-temperature series expansions and tensor-network-based renormalization-group calculations are still controversial especially at low temperatures, and under such circumstances, modified spin waves interestingly predict that the specific heat of the kagome-lattice antiferromagnet in the corner-sharing geometry remains having both mid-temperature broad maximum and low-temperature narrow peak in the thermodynamic limit, while the specific heat of the triangular-lattice antiferromagnet in the edge-sharing geometry retains a low-temperature sharp peak followed by a mid-temperature weak anormaly in the thermodynamic limit. By further calculating one-magnon spectral functions in terms of our newly developed double-constraint modified spin-wave theory, we reveal that not only the elaborate modification scheme but also quantum corrections, especially those caused by the O(S 0) primary self-energies, are key ingredients in the successful description of triangular-based-lattice noncollinear antiferromagnets over the whole temperature range of absolute zero to infinity.
A választási reform dinamikája

A választási reform egyre szélesebb körben foglalkoztatja a szakembereket az utóbbi évtizedekben, kiváltképp az annus mirabilistől (1993–94) számítva, amikor is négy leülepedett demokráciában (Olaszország, Izrael, Japán, Új-Zéland) következett be választási reform, amelyek hatására a kutatóknak újra kellett gondolniuk a fogalmat, új alapokra kellett helyezniük a reform elméletét. Korábban, mint független változót, statikus fogalomként kezelték a választási rendszert, ezért a vizsgálat annak következményeire irányult. A 90-es évek reformhullámának köszönhetően kiderült, hogy a választási rendszer sajátos kontextusba ágyazottan jön létre és működik, ezért kihatásai mellett annak okait is kell tanulmányozni, és így a reform függő változóként, dinamikus fogalomként közelítendő meg. E koncepcióváltás jobb megértéséhez hozzájárulhat a változás (a külső környezet, hosszabb időt igénylő, többnyire spontán, kevésbé ellenőrizhető módon történő helyzetmódosulása) és a változtatás (a választási rendszer tervezett módosítása, amely viszonylag rövid előkészületi folyamat után egy időpillanat műve) fogalmak megkülönböztetése, egymásra hatásuk vizsgálata. E különbségtétel tehát egy állapotból egy másik állapotba történő spontán átalakulás, illetve egy állapotból egy másik állapotba történő szándékos átvezetés között húzódik. Jelen tanulmány a választási reform dinamikus voltát hivatott megragadni, amely a változás és változtatás különbségtétele, valamint a választási reformirodalom háromszakaszos fejlődéstörténete révén körvonalazható. E munka elsődleges célja tehát, rámutatni, hogy a választási rendszer és a reform maga nemcsak explanans, hanem explanandum is.
DyLiteRADHAR: Dynamic Lightweight Slowfast Network for Human Activity Recognition Using MMWAVE Radar

Millimeter-wave radar based human activity recognition (RADHAR) exhibits remarkable prospects in the field of device-free sensing. However, most existing RADHAR systems only focus on performance improvement, failing to simultaneously lighten the network parameters. In this paper, we propose a dynamic lightweight SlowFast network named DyLiteRADHAR, which can efficiently extract spatial-temporal features and largely reduce the resource consumption for human activity recognition. Specifically, we design triple-view signal maps (TRIview) as the input by successively concatenating the range-velocity, range-azimuth and range-elevation matrices. Then dynamic lightweight network is presented to learn discriminative representations which integrates dynamic convolution and lightweight shuffle net structure into the SlowFast framework. Experimental results demonstrate that the proposed approach DyLiteRADHAR is able to achieve superiority performance with limited computation complexity.
Speakeraugment: Data Augmentation for Generalizable Source Separation via Speaker Parameter Manipulation

Existing speech separation models based on deep learning typically generalize poorly due to domain mismatch. In this paper, we propose SpeakerAugment (SA), a data augmentation method for generalizable speech separation that aims to increase the diversity of speaker identity in training data, to mitigate speaker mismatch of domain mismatch. The SA consists of two sub-policies: (1) SA-Vocoder, which uses a vocoder to manipulate pitch and formants parameters of speakers. (2) SA-Spectrum, which directly performs pitch-shift and time-stretch on the spectrum of each speech signal. The SA is simple and effective. Experimental results show that using SA can significantly improve the generalization ability of models, especially for: 1) The training set with fewer speakers, e.g., WSJ0-2mix, or 2) The target test set with complex linguistic conditions, e.g., the TIMIT based test set. Moreover, as a data augmentation method, SA has good potential to be applicable to other speech related tasks. We validate this by applying SA in speech recognition, and experimental results show that the generalization ability is also improved.
Fracture Healing in 37 Dogs and Cats with Implant Failure after Surgery (2013–2018)

Simple Summary Identifying risk factors for implant failure in small animal orthopedics could improve outcomes in clinical patients. Abnormal fracture healing has been hypothesized to correlate with implant failure. This study retrospectively reviewed a consecutive series of implant failure cases in dogs and cats. Area moment of inertia (AMI), plate working length, and bone screw density were determined as appropriate for each case. Implant failure occurred in 23% of the fracture cases in the study period and was associated with an increased risk of delayed union, malunion, or non-union of the fracture. Major complications were found in 56% of the fracture repairs and were associated with delayed union. Surgical revision was performed in 49% of implant failure cases. Common problems were implant loosening and failure of low AMI plates. Implant AMI should routinely be considered during preoperative planning. Abstract Implant failure is common in small animal orthopedics, but risk factors are rarely reported. Our objective was to determine whether abnormal fracture healing was associated with implant failure after fracture fixation in dogs and cats in a consecutive series of cases. Thirty-seven client-owned animals (thirty-two dogs, five cats) diagnosed with implant failure after fracture treatment from January 2013–September 2018 were studied. Medical and radiographic records were retrospectively reviewed to identify patients that underwent fracture fixation using open reduction and internal fixation with subsequent radiographic evidence of implant failure. Area moment of inertia (AMI), plate working length, and bone screw density were determined. Implant failure was found in 39 fractures in 37 animals, representing 23% of fracture cases during the study period. Cases of implant failure were at increased risk of delayed union, malunion, or non-union (p < 0.0001). The most common cause of implant failure was loosening (54%); the second most common was plate failure that included low AMI locking plates (28%). Major complications found in 22/39 fractures (56%) were associated with delayed union (p < 0.01). Surgical revision was performed in 49% of implant failure cases. Complications were most frequently identified after treatment of humeral fractures (26%). We conclude mechanical failure of implants increases the risk for delayed or abnormal fracture healing and often requires revision surgery. Implant AMI should be considered during preoperative planning. Locking plates are associated with implant failure if plate bending stiffness is not sufficient, based on findings from this case series.
A Low-Latency Hybrid Multi-Channel Speech Enhancement System For Hearing Aids

This paper summarizes a hybrid multi-channel speech enhancement system for the ICASSP Signal Processing Grand Challenge: Clarity Challenge (Speech Enhancement for Hearing Aids) 2023. The system consists of a rule-based dereverberation module, a multi-channel enhancement module, and a post-processing module. Without using the head rotation information and the enrollment speech, the system can reach an average hearing aid speech perception index (HASPI) score of 0.696 and hearing aid speech quality index (HASQI) score of 0.320 on the official development set. The corresponding scores are 0.729 and 0.316 respectively on the Eval1 set for the challenge ranking.
Leadership style, knowledge sharing and audit quality

PurposeThis paper aims to examine how leadership style in audit firms influences audit quality. The paper further explores the mediating role of knowledge sharing in the relationship between leadership style and audit quality.Design/methodology/approachThe present paper studies the effects of transformational and servant leadership styles on audit quality through knowledge sharing. Data are collected from 396 Iranian external auditors via a questionnaire.FindingsThe results show that both transformational and servant leadership style significantly influence audit quality through knowledge sharing. Moreover, the impact of transformational leadership style is stronger than the impact of servant leadership style.Originality/valueIn audit quality literature, little attention has been devoted to both leadership style and knowledge sharing. This paper develops a parsimonious model which shows how leadership style improves audit quality, and how knowledge sharing strengthens the impact of leadership style on audit quality. The results have important implications particularly for audit industry.
Query Expansion by Prompting Large Language Models

Query expansion is a widely used technique to improve the recall of search systems. In this paper, we propose an approach to query expansion that leverages the generative abilities of Large Language Models (LLMs). Unlike traditional query expansion approaches such as Pseudo-Relevance Feedback (PRF) that relies on retrieving a good set of pseudo-relevant documents to expand queries, we rely on the generative and creative abilities of an LLM and leverage the knowledge inherent in the model. We study a variety of different prompts, including zero-shot, few-shot and Chain-of-Thought (CoT). We find that CoT prompts are especially useful for query expansion as these prompts instruct the model to break queries down step-by-step and can provide a large number of terms related to the original query. Experimental results on MS-MARCO and BEIR demonstrate that query expansions generated by LLMs can be more powerful than traditional query expansion methods.
Is It a Trap? A Large-scale Empirical Study And Comprehensive Assessment of Online Automated Privacy Policy Generators for Mobile Apps

Privacy regulations protect and promote the privacy of individuals by requiring mobile apps to provide a privacy policy that explains what personal information is collected and how these apps process this information. However, developers often do not have sufficient legal knowledge to create such privacy policies. Online Automated Privacy Policy Generators (APPGs) can create privacy policies, but their quality and other characteristics can vary. In this paper, we conduct the first large-scale empirical study and comprehensive assessment of APPGs for mobile apps. Specifically, we scrutinize 10 APPGs on multiple dimensions. We further perform the market penetration analysis by collecting 46,472 Android app privacy policies from Google Play, discovering that nearly 20.1% of privacy policies could be generated by existing APPGs. Lastly, we point out that generated policies in our study do not fully comply with GDPR, CCPA, or LGPD. In summary, app developers must carefully select and use the appropriate APPGs with careful consideration to avoid potential pitfalls.
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation

The Multimodal Sentiment Analysis Challenge (MuSe) 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic), participants predict three continuous emotion targets. This sub-challenge utilises the Hume-Vidmimic dataset comprising of user-generated videos. For the Cross-Cultural Humour Detection Sub-Challenge (MuSe-Humour), an extension of the Passau Spontaneous Football Coach Humour (Passau-SFCH) dataset is provided. Participants predict the presence of spontaneous humour in a cross-cultural setting. The Personalisation Sub-Challenge (MuSe-Personalisation) challenge is based on the Ulm-Trier Social Stress Test (Ulm-TSST) dataset, featuring recordings of subjects in a stressed situation. Here, arousal and valence signals are to be predicted, whereas parts of the test labels are made available in order to facilitate personalisation. MuSe 2023 seeks to bring together a broad audience from different research communities such as audio-visual emotion recognition, natural language processing, signal processing, and health informatics. In this baseline paper, we introduce the datasets, sub-challenges, and provided feature sets. As a competitive baseline system, a Gated Re-current Unit (GRU)-Recurrent Neural Network (RNN) is employed. On the respective sub-challenges' test datasets, it achieves a mean (across three continuous intensity targets) Pearson's Correlation Coefficient of . 4727 for MuSe-Mimic, an Area Under the Curve (AUC) value of . 8310 for MuSe-Humour and Concordance Correlation Coefficient (CCC) values of . 7482 for arousal and . 7827 for valence in the MuSe-Personalisation sub-challenge.
Images of Gaussian and other stochastic processes under closed, densely-defined, unbounded linear operators

Gaussian processes (GPs) are widely-used tools in spatial statistics and machine learning and the formulae for the mean function and covariance kernel of a GP $T u$ that is the image of another GP $u$ under a linear transformation $T$ acting on the sample paths of $u$ are well known, almost to the point of being folklore. However, these formulae are often used without rigorous attention to technical details, particularly when $T$ is an unbounded operator such as a differential operator, which is common in many modern applications. This note provides a self-contained proof of the claimed formulae for the case of a closed, densely-defined operator $T$ acting on the sample paths of a square-integrable (not necessarily Gaussian) stochastic process. Our proof technique relies upon Hille's theorem for the Bochner integral of a Banach-valued random variable.