Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Control Method of Multi-Energy Flow System Considering Wind Power Consumption

In recent years, global energy shortages and environmental pollution have intensified. With the large-scale development of new energy wind energy, there is also a major problem-insufficient absorption capacity. In order to promote large-scale wind power consumption, a model predictive control method based on wind hydrogen coupled power generation system is proposed. Firstly, the mathematical models of equivalent state of charge in wind power, hydrogen energy storage systems (HESS), and gas storage tanks are analyzed. Secondly, the optimization objective function is to maximize the local wind energy consumption and minimize the energy interaction between the main grid. Establish an SSM prediction model based on the MPC strategy. Genetic optimization algorithm is used for rolling solution. Aiming at the interference and prediction error generated during the operation of the system, a feedback mechanism is introduced to embed it into the MPC framework. Then, the rolling time domain method is used to compensate for system interference. Finally, a case study was conducted based on actual measurement data from a certain area in the Netherlands. By comparing the wind power dissipation effects of the system during operation, it is verified that the proposed method can effectively reduce interactive power consumption. Maximize the local consumption of wind power.
Purification of Selective Laser Melting Additive Manufactured Niobium for Superconducting RF-Applications Transactions on Applied Superconductivity

Superconducting radio frequency (SRF)–cavities in the field of particle accelerators are mostly manufactured from bulk or sheet niobium (Nb). Complex shapes of SRF-components like high order mode-couplers or cavities working at high fields are high in cost and lead-time. The fabrication of these parts via additive manufacturing promises a more effective production and higher flexibility in geometries. At CERN, additive manufacturing with pure Nb by selective laser melting (SLM) is under development for future particle accelerator components. In comparison with conventionally produced Nb SRF-components, made of highly pure Nb, the SLM-manufactured Nb material contains considerable impurities due to raw material processing and the SLM-process conditions itself. Contaminations with light elements, such as oxygen and nitrogen, introduced into the material's structure by the powder and the SLM process, contribute to an important lowering of the residual resistance ratio (RRR). In order to increase the RRR-values for conventionally manufactured SRF-cavities, titanium (Ti) gettering-heat treatments were investigated since the 1980s. This paper introduces the first representative Nb-material realized at CERN by additive manufacturing by SLM and the current studies on the post-treatment of sample material by vacuum firing and Ti-gettering-heat treatments. The samples are analyzed by mass spectroscopy and RRR-measurements in correlation with different process parameters. First trials with Ti-gettering heat treatments demonstrate an increase in the RRR from 7 to above 100, whereas the oxygen content could be reduced from 600 to 17 ppm. These results show that a purification post-treatment of SLM-manufactured Nb-components is feasible and pave the way for functioning SRF-components made by additive manufacturing.
Hybrid Cascaded and Feature-Level Fusion Scheme for Multi-Modal Indoor Localization

Smartphone based indoor localization has been widely explored for meeting the demand of high-precision low-cost indoor localization. Previous methods focus mainly on improving the localization accuracy of single sensor based localization, which may hinder their applications. In this article, we propose a novel encoder-decoder architecture for high-precision low-cost indoor localization. We first leverage two modal-specific encoders for feature extraction. Then, we propose two feature-level fusion strategies for feature fusion. Finally, we leverage two task-specific decoders for both position and orientation prediction. During training, we adopt WiFi-aided learning to provide a more reliable label. We test the proposed method in the corridor environment of a typical building. Experiments results show that our method can achieve less than a half meter localization accuracy, and meanwhile enjoys the run-time efficiency.
Biomechanical Investigation of Red Cell Sedimentation Using Blood Shear Stress and Blood Flow Image in a Capillary Chip

Blood image intensity has been used to detect erythrocyte sedimentation rate (ESR). However, it does not give information on the biophysical properties of blood samples under continuous ESR. In this study, to quantify mechanical variations of blood under continuous ESR, blood shear stress and blood image intensity were obtained by analyzing blood flows in the capillary channel. A blood sample is loaded into a driving syringe to demonstrate the proposed method. The blood flow rate is set in a periodic on–off pattern. A blood sample is then supplied into a capillary chip, and microscopic blood images are captured at specific intervals. Blood shear stress is quantified from the interface of the bloodstream in the coflowing channel. τ0 is defined as the maximum shear stress obtained at the first period. Simultaneously, ESRτ is then obtained by analyzing temporal variations of blood shear stress for every on period. AII is evaluated by analyzing the temporal variation of blood image intensity for every off period. According to the experimental results, a shorter period of T = 4 min and no air cavity contributes to the high sensitivity of the two indices (ESRτ and AII). The τ0 exhibits substantial differences with respect to hematocrits (i.e., 30–50%) as well as diluents. The ESRτ and AII showed a reciprocal relationship with each other. Three suggested properties represented substantial differences for suspended blood samples (i.e., hardened red blood cells, different concentrations of dextran solution, and fibrinogen). In conclusion, the present method can detect variations in blood samples under continuous ESR effectively.
Fully-Binarized Distance Computation based On-device Few-Shot Learning for XR applications

Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. A critical requirement for emerging AI applications is personalization and adaptability without requiring retraining. Few-shot learning using embedding-based computations present an attractive method for the same. However, quantization-based optimizations to map such computations are yet to be explored. In this work, we present a fully binarized distance computing (BinDC) framework to perform distance computations for few-shot learning using only accumulation and logic operations (XOR/XNOR). The proposed method leads to marginal loss in accuracy of ≈ 4% (for 4-bits). This leads to savings in memory (≈ 8 ), energy (≈ 2.5-3×), power (≈ 2×) and latency (≈ 1.1-1.5×) compared to a floating-point cosine distance computation when using CPU-based computations performed on an embedded platform. We further demonstrate realization utilizing RRAM (resistive random access memory) based IMC (in-memory computing) to further improve EDP (energy delay product) (≈ 1000×) in comparison to the embedded CPU-based realization.
RAMP: Retrieval-Augmented MOS Prediction via Confidence-based Dynamic Weighting

Automatic Mean Opinion Score (MOS) prediction is crucial to evaluate the perceptual quality of the synthetic speech. While recent approaches using pre-trained self-supervised learning (SSL) models have shown promising results, they only partly address the data scarcity issue for the feature extractor. This leaves the data scarcity issue for the decoder unresolved and leading to suboptimal performance. To address this challenge, we propose a retrieval-augmented MOS prediction method, dubbed {\bf RAMP}, to enhance the decoder's ability against the data scarcity issue. A fusing network is also proposed to dynamically adjust the retrieval scope for each instance and the fusion weights based on the predictive confidence. Experimental results show that our proposed method outperforms the existing methods in multiple scenarios.
Do Vocal Breath Sounds Encode Gender Cues for Automatic Gender Classification?

The acoustic features of continuous speech, such as pitch (F0) and formant frequencies (F1, F2) have been utilized for gender classification. However, non-speech signals including vocal breath sounds have not been explored due to the absence of gender-specific acoustic features. This study investigates if vocal breath sounds carry gender information and if they can be used for automatic gender classification. The study examines the use of data-driven and knowledge-based features from breath sounds, classifier complexity, and the importance of breath signal segment location and duration. Results from experiments on 54 minutes of male and 52 minutes of female breath sounds demonstrate that classifiers with low-complexity and knowledge-based features (MFCC statistics) perform similarly to high-complexity classifiers with data-driven features. Breath segments of around 3 seconds are found to be the most suitable choice regardless of location, eliminating the need for breath cycle boundary marking.
Losing My Religion: A Preliminary Study of Changes in Belief Pattern After Sexual Assault:

The purpose of the present study was to examine the impact of sexual trauma on changes in religious perception, subjective health, and mental health among sexual assault victims. A total of 111 Jewish women participated in the study, including 51 sexual assault victims and 60 comparisons matched on age, religion, and marital status. The study was of a retrospective, cross-sectional design. Approximately half of the women (47.5%) who suffered sexual assault changed their religious perception and belief in the direction of secularization. In addition, these women exhibited elevated psychiatric symptoms and lower well-being relative to the comparison group. The results of the preliminary study revealed changes in belief patterns due to psychological trauma. Mental health professionals should take these aspects into consideration when treating women who suffered sexual trauma.
A robust performance enhancement of primary H∞ controller based on auto-selection of adjustable fractional weights: Application on a permanent magnet synchronous motor

This paper proposes a new robustification strategy of a primary H∞ controller based on a systematic selection of adequate fractional weights. Its robust performance (RP) margin is well enhanced with respect to unstructured multiplicative uncertainties presented in linear feedback systems. The proposed robustification strategy provides a robustified H∞ controller when following proposed hierarchical control is well respected. First, the primary H ∞ controller is synthesized from solving a weighted-mixed sensitivity H∞ problem using initial integer weights. Thus, an initial RP margin is obtained. Second, an automatic selection of adjustable fractional weights is performed by the particle swarm optimization algorithm, in which some proposed tuning rules are accordingly well satisfied. Third, frequency response data of these weights are computed and then fitted by corresponding approximated integer weights using a frequency identification technique. Finally, these weights reformulate a new weighted-mixed sensitivity problem. The optimal solution to this problem updates the previous initial RP margin. These last three steps are repeated as the updated RP margin is diminished. Otherwise, the proposed hierarchical control is achieved by selecting the best adjustable fractional weights, providing, therefore, the best approximated integer weights and leading, therefore, to the robustified H∞ controller. In order to confirm the effectiveness of our proposed hierarchical control, primary and robustified H∞ controllers are applied on a permanent magnet synchronous motor where its actual behavior is modeled by an unstructured multiplicative uncertain model. The results obtained are compared in frequency domains using the singular value plots of their sensitivity functions. Otherwise, the same results are compared in time domains using the Powersim® software.
Late pleistocene changes in the Peru current based on planktonic foraminifera

approved: Alan C. Mix Planktonic foraminiferal faunal assemblages were used to estimate sea-surface temperatures (SST) in the southeast Pacific Ocean during the Last Glacial Maximum (LGM) and off the coast of Peru over the last 150,000 years. Temperatures along the eastern boundary of South America were 6-8° C cooler during the LGM than at present and have varied by as much as 10° C over the past 150,000 years. A likely source of ice-age cooling along the eastern boundary is an increase in the strength and a corresponding decrease in the temperature of water advected northward in the Peru Current. Evidence for this mechanism of cooling comes from incursion of subpolar foraminiferal species northward into the eastern Pacific during the LGM. We infer from this change in the fauna and decreased temperatures that stronger northward advection of eastern boundary current (EBC) waters was influential in driving cooling of the Eastern Tropical Pacific (EEP) during the LGM. The 150,000-year SST records from the Nazca Rise further reveal that changes in the strength of the Peru Current have influenced temperatures in the Eastern Equatorial Pacific on glacial-interglacial timescales. The effects of changes in the eastern boundary current on EEP temperatures are evaluated using a simple heat budget model in which equatorial SSTs are predicted from Peru Current temperatures. Variations in EBC temperatures account for about one third of the variability in equatorial SSTs. The remaining variance near the equator is probably associated with local wind-driven upwelling and is linked to the orbital cycles. We infer from this model that temperature changes in the Eastern Equatorial Pacific reflect both a direct response of equatorial upwelling to insolation and changes in eastern boundary current advection. Redacted for privacy Late Pleistocene Changes in the Peru Current Based on Planktonic Foraniinifera
Impact of Residual Noise and Artifacts in Speech Enhancement Errors on Intelligibility of Human and Machine

Single-channel (1-ch) speech enhancement (SE) has been widely studied, and high accuracy has been achieved recently. However, enhanced speech still includes some errors that affect human hearing quality and SE applications, e.g., automatic speech recognition (ASR). Previously, [Iwamoto et al., “How bad are artifacts? : Analyzing the impact of speech enhancement errors on ASR” in Interspeech 2022, pp. 5418–5422] decomposed the errors in an enhanced signal into residual noise and artifact components, and analyzed their impacts on ASR performance. They showed that the artifacts have a greater impact than the residual noise on ASR. Although the impact on human intelligibility has not been investigated yet, it is essential to get the knowledge to develop SE techniques suitable for both humans and machines. This paper, therefore, investigates the effects of such error factors on human listening. Our subjective test results show that the artifacts have a large impact on human intelligibility, and that residual noise has a lesser impact. This tendency is similar in machine ASR.
Multimodal Turn-Taking Model Using Visual Cues for End-of-Utterance Prediction in Spoken Dialogue Systems

In this study, we propose a multimodal model for predicting the end-of-utterance probability in spoken dialogue systems, high-lighting the unique role of visual cues in addition to acoustic and linguistic information. Although the effectiveness of visual cues, such as gaze, mouth, and head movements, has been suggested, few studies have fully incorporated them into turn-taking models, and the relative importance of these visual cues has also been underresearched. To address these issues, we first conducted an ablation study on visual features, showing the larger contribution of eye movements than mouth and head movements. Additionally, an end-to-end visual feature extraction model utilizing 3D-CNN is employed to comprehensively capture these visual cues. By combining visual features with acoustic and verbal information, AUC score for end-of-utterance prediction improved from 0.896 to 0.920, demonstrating the effectiveness of incorporating these visual cues in turn-taking models.
Microbiota and Resistome Analysis of Colostrum and Milk from Dairy Cows Treated with and without Dry Cow Therapies

This study investigated the longitudinal impact of methods for the drying off of cows with and without dry cow therapy (DCT) on the microbiota and resistome profile in colostrum and milk samples from cows. Three groups of healthy dairy cows (n = 24) with different antibiotic treatments during DCT were studied. Colostrum and milk samples from Month 0 (M0), 2 (M2), 4 (M4) and 6 (M6) were analysed using whole-genome shotgun-sequencing. The microbial diversity from antibiotic-treated groups was different and higher than that of the non-antibiotic group. This difference was more evident in milk compared to colostrum, with increasing diversity seen only in antibiotic-treated groups. The microbiome of antibiotic-treated groups clustered separately from the non-antibiotic group at M2-, M4- and M6 milk samples, showing the effect of antibiotic treatment on between-group (beta) diversity. The non-antibiotic group did not show a high relative abundance of mastitis-causing pathogens during early lactation and was more associated with genera such as Psychrobacter, Serratia, Gordonibacter and Brevibacterium. A high relative abundance of antibiotic resistance genes (ARGs) was observed in the milk of antibiotic-treated groups with the Cephaguard group showing a significantly high abundance of genes conferring resistance to cephalosporin, aminoglycoside and penam classes. The data support the use of non-antibiotic alternatives for drying off in cows.
Why is There No History of Fascist International Thought?

Notwithstanding recent efforts, historians of international thought have yet to adequately address the highly heterogeneous and often paradoxical ideas espoused by international thinkers of a fascist persuasion. Instead, fascist international thought has commonly been ignored or otherwise reduced to an antiquated Darwinian realism. This article aims to present a case for how and why this fragmentary situation should be corrected. Specifically, it advocates for a closer interdisciplinary engagement between the history of international thought and the field of fascist studies. It thus implores the former to consider salient thematic and methodological developments within the latter and adapt them accordingly. The consequent research agenda that emerges feasibly offers novel insight into (I) unexplored avenues in the history of international thought and the disciplinary history of International Relations, alongside presenting both (II) theoretical and (III) normative implications for the discipline as such. Pourquoi n’y a-t-il pas d’Histoire de la pensée fasciste internationale?
CFVC: Conditional Filtering for Controllable Voice Conversion

This paper describes a many-to-many voice conversion model that ﬁlters the speaker vector to control high-level attributes such as speaking rate while preserving voice timbre. In order to control only the speaking rate, it is essential to decompose the speaker vector into a speaking rate vector and others. The challenge is to train such disentangled representations with no/few annotation data. Motivated by this difﬁculty, we pro-pose an approach combining the conditional ﬁltering method with data augmentation. The experimental results showed that our method disentangled complex attributes without annotation and separately controlled speaking rate and voice timbre. Audio samples can be accessed on our web page 1 .
Predicting and Mitigating the Effect of Skewness on Credibility Assessment of Social Media Content Using Machine Learning: A Twitter Case Study

—Many strategies have been put forward to assess the credibility of online social media content, however, none of them focuses on the issue of accuracy paradox which mostly occurs in highly skewed datasets, a case that usually arises in real-life situations. The purpose of this paper is to explore the use of various machine learning models including Gaussian Naïve Bayes, Latent Dirichlet Allocation (LDA), Linear Regression, Logistic Regression, and Support Vector Machine (SVM) for identifying the credibility of tweets. This includes proposing a new algorithm where the generative properties of Gaussian naïve Bayes are integrated with the discriminative properties of logistic regression and the author evaluates its performance in terms of accuracy and prediction power of determining tweet credibility. The Machine Learning Models used in this study, implemented on the Twitter datasets extracted from various real-world events are compared based on their accuracy and predictive power, in determining the credibility of tweets, to identify various accuracy paradox cases. The proposed algorithm is then used for the credibility inference of tweets and the reduction in the number of accuracy paradox cases is monitored. An extensive experimental study is performed to evaluate the performance of the proposed model on Twitter datasets with varied degrees of skewness. Our proposed model achieved accuracy and predictive power of 97% and 94% for a balanced dataset and 99% and 93% for an imbalanced dataset with 99% skewness.
Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms

Robust audio anti-spoofing has been increasingly challenging due to the recent advancements on deepfake techniques. While spectrograms have demonstrated their capability for anti-spoofing, complementary information presented in multi-order spectral patterns have not been well explored, which limits their effectiveness for varying spoofing attacks. Therefore, we propose a novel deep learning method with a spectral fusion-reconstruction strategy, namely S2pecNet, to utilise multi-order spectral patterns for robust audio anti-spoofing representations. Specifically, spectral patterns up to second-order are fused in a coarse-to-fine manner and two branches are designed for the fine-level fusion from the spectral and temporal contexts. A reconstruction from the fused representation to the input spectrograms further reduces the potential fused information loss. Our method achieved the state-of-the-art performance with an EER of 0.77% on a widely used dataset: ASVspoof2019 LA Challenge.