Miracle-dz/UniFAR-FTUs huggingface dataset:
ConvKT: Conversation-Level Knowledge Transfer for Context Aware End-to-End Spoken Language Understanding

Dialog history enhances downstream classification performance in both speech and text based dialog systems. However, there still exists a gap in dialog history integration in a fully end-to-end (E2E) spoken dialog system (SDS) versus a textual dialog system. Text-based dialog systems use large language models (LLMs) to encode long-range dependencies by attending to the entire conversation as a contiguous token sequence. This is not possible in an E2E SDS, as speech sequences can be intractably long. We propose a convolution subsampling approach to make the speech sequence of a conversation tractable and use a conformer to attend to the speech-based conversation in a fine-grained manner. This model is further enhanced via a conversation-level knowledge transfer from a LLM us-ing a token-level alignment strategy. Finetuning the E2E model pretrained this way gives significant gains, of up to 8%, over strong non-contextual baselines in the E2E dialog act classification task on two datasets.
Activation of antigen-presenting cells by DNA delivery vectors

Gene-based modulation of immune functions is a promising means of eliciting protective immunity and induction of tolerance. Novel viral and non-viral DNA delivery systems are being investigated to achieve efficient gene transfer into mammalian cells. Antigen-presenting cells (APCs), in particular dendritic cells, are crucial targets in this context due to their capacity to initiate and direct effector functions. The increasing relevance of APCs as targets of DNA vectors calls for an assessment of vector-driven activation of these cells. For viral vectors, a putative pathway of APC activation would be Toll-like receptor signalling for certain RNA genome viruses. On the other hand, non-viral vectors appear to mature APCs by interaction of polymeric particulates or bioactive lipids with cellular mechanisms. The rational design of DNA-based therapies is possible only when the intrinsic effects of the vector and immune modulation originating from the DNA are delineated. This paper will summarise recent reports of adjuvant properties of viral and non-viral delivery systems.
A Unified Approach to Facial Affect Analysis: the MAE-Face Visual Representation

Facial affect analysis is essential for understanding human expressions and behaviors, encompassing action unit (AU) detection, expression (EXPR) recognition, and valence-arousal (VA) estimation. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing a high-quality and large-scale Affwild2 dataset for identifying widely used emotion representations. In this paper, we employ MAE-Face as a unified approach to develop robust visual representations for facial affect analysis. We propose multiple techniques to improve its fine-tuning performance on various downstream tasks, incorporating a two-pass pre-training process and a two-pass fine-tuning process. Our approach exhibits strong results on numerous datasets, highlighting its versatility. Moreover, the proposed model acts as a fundamental component for our final framework in the ABAW5 competition. Our submission achieves outstanding outcomes, ranking first place in the AU and EXPR tracks and second place in the VA track.
Lazy Demand-driven Partial Redundancy Elimination

: Partial redundancy elimination (PRE) is a code optimization algorithm that simultaneously performs common sub-expression elimination and loop-invariant code motion. Traditional PREs analyze the entire program and eliminate redundancies. By contrast, demand-driven PRE (DDPRRE) is proposed as an algorithm to analyze only a part of the program to determine whether each expression is redundant by query propagation. The previous DDPRE reduces the analysis time because it limits the analysis range; however, it is known that redundancy may not be eliminated when the nodes in the loop are revisited. We propose a novel DDPRE, named lazy demand-driven PRE (LDPRE), which eliminates redundancy by delaying the decision of whether the analyzing expression is redundant or not when a node in a loop is revisited and redundancy cannot be analyzed. LDPRE uses a semi-lattice as the answer space during the query propagation. The semi-lattice includes not only true / false implying that the queried expression is redundant or not, but also (cid:62) for undecidability. While maintaining the analytical e ﬃ ciency characteristic of demand-driven analysis, our algorithm eliminates redundancy that previous DDPRE could not eliminate by determining the answer using semi-lattice.
Ecological networks : Linking structure to dynamics in food webs

M. Pascual and J. A. Dunne: Preface PART I: INTRODUCTION 1: M. Pascual and J. A. Dunne: From Small to Large Ecological Networks in a Dynamic World PART II: STRUCTURE OF COMPLEX ECOLOGICAL NETWORKS 2: J. A. Dunne: The Network Structure of Food Webs 3: C. C. Cartozo, G. Garlaschelli, and G. Caldarelli: Graph Theory and Food Webs 4: A. Dobson, K. Lafferty, and A. Kuris: Parasites and Food Webs 5: J. Bascompte and P. Jordano: The Structure of Plant-Animal Mutualistic Networks PART III: INTEGRATING ECOLOGICAL STRUCTURE AND DYNAMICS 6: N. D. Martinez, R. J. Williams, and J. A. Dunne: Diversity, Complexity, and Persistance in Large Model Ecosystems 7: Exploring Network Space with Genetic Algorithms Modularity, Resilience, and Reactivity 8: J. F. Gillooly, A. P. Allen, and J. H. Brown: Food-Web Structure and Dynamics: Reconciling Alternative Ecological Currencies PART IV: ECOLOGICAL NETWORKS AS EVOLVING, ADAPTIVE SYSTEMS 9: A. J. McKane and B. Drossel: Models of Food-Web Evolution 10: S. D. Peacor, R. L. Riolo, and M. Pascual: Phenotypic Plasticity and Species Coexistence: Modeling Food Webs as Complex Adaptive Systems 11: C. O. Wilke and S. S. Chow: Exploring the Evolution of Ecosystems with Digital Organisms 12: N. D. Martinez: Network Evolution: Exploring the Change and Adaptation of Complex Ecological Systems over Deep Time PART V: STABILITY AND ROBUSTNESS OF ECOLOGICAL NETWORKS 13: R. V. Sole and J. M. Montoya: Ecological Network Meltdown from Habitat Loss and Fragmentatino 14: J. Memmott, David Alonso, E. L. Berlow, A. Dobson, J. A. Dunne, R. V. Sole, and J. Weitz: Biodiversity Loss and Ecological Network Structure 15: M. Pascual, J. A. Dunne, and S. A. Levin: Challenges for the Future: Integrating Ecological Structure and Dynamics
Beatboxing Kick Drum Kinematics

Study of the vocal movements of beatboxing can benefit speech science in a number of ways; but while there are established models of speech motor control that make deterministic predictions about vocal kinematics, little is known about beatboxing motor control. A region of interest method was applied to real-time MRI videos of beatboxing Kick Drums to measure the time to peak velocity—a measurement that is used to assess how well models of speech action predict actual movement trajectories. The average time to peak velocity for Kick Drums is about half of the way (58%) through the total movement duration, similar to the times to peak velocity reported for speech actions. However, while the times to peak velocity for Kick Drums tend to be just above 50%, the times to peak velocity reported for speech sounds are usually a bit below 50%. Further study is needed to assess whether this difference reflects a more extreme constriction goal or a qualitatively different movement pattern.
Evaluation of High-Performance Radio Propagation Simulation Method in Path Loss Estimation

To establish a simulation technology for estimating and predicting radio propagation characteristics with short calculation time and high accuracy, we have proposed a novel color images method (CIM) based on processing colors images of walls of buildings which are seen from a transmission and reception points. However, the estimation performance of the proposed CIM has not been sufficiently clarified. In this paper, we compare the CIM with Wireless InSite (WI) employing a conventional ray tracing method (RTM) in terms of percentage of estimable reception points, estimation accuracy, and calculation time. Comparison results show that for the same percentage of estimable reception points, the average value and root mean squared error (RMSE) of path loss estimation errors of the CIM are -0.8 dB and 8.3 dB, respectively, and much smaller than those of the WI with 15.9 dB and 21.7 dB. Moreover, the calculation time of the CIM is 0.0089 times shorter than that of the WI in case of changing conditions of the transmission point.
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions

There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems. However, there is a dearth of analyses of what is actually learnt and the relative importance of training the different components of the front-end. In this paper, we investigate this question on keyword spotting, speech-based emotion recognition and language identification tasks and find that the filters for spectral decomposition and the low pass filter used to estimate spectral energy variations exhibit no learning and the per-channel energy normalisation (PCEN) is the key component that is learnt. Following this, we explore the potential of adapting only the PCEN layer with a small amount of noisy data to enable it to learn appropriate dynamic range compression that better suits the noise conditions. This in turn enables a system trained on clean speech to work more accurately on noisy test data as demonstrated by the experimental results reported in this paper.
An Acoustic Analysis of Fricative Variation in Three Accents of English

This study reports on an analysis of accent and gender differences in the realisation of the fricative /s/ within three accents of English: London, Cambridge, Belfast. There were 30 speakers in the study. Using multilevel modelling, significant differences between accents in the dynamic acoustics of the alveolar fricative /s/ are evident. Significant gender differences in the fricative energy measure trajectories are also found, within each accent. We further discuss the implications of these differences to our understanding of the role of gender and accent in the realisation of spectra movements in English fricatives, highlighting the necessity of a dynamic approach to sociophonetic acoustic variation
Recent Advances in Additive Manufacturing of Soft Magnetic Materials: A Review

Additive manufacturing (AM) is an attractive set of processes that are being employed lately to process specific materials used in the fabrication of electrical machine components. This is because AM allows for the preservation or enhancement of their magnetic properties, which may be degraded or limited when manufactured using other traditional processes. Soft magnetic materials (SMMs), such as Fe–Si, Fe–Ni, Fe–Co, and soft magnetic composites (SMCs), are suitable materials for electrical machine additive manufacturing components due to their magnetic, thermal, mechanical, and electrical properties. In addition to these, it has been observed in the literature that other alloys, such as soft ferrites, are difficult to process due to their low magnetization and brittleness. However, thanks to additive manufacturing, it is possible to leverage their high electrical resistivity to make them alternative candidates for applications in electrical machine components. It is important to highlight the significant progress in the field of materials science, which has enabled the development of novel materials such as high-entropy alloys (HEAs). These alloys, due to their complex chemical composition, can exhibit soft magnetic properties. The aim of the present work is to provide a critical review of the state-of-the-art SMMs manufactured through different AM technologies. This review covers the influence of these technologies on microstructural changes, mechanical strengths, post-processing, and magnetic parameters such as saturation magnetization (MS), coercivity (HC), remanence (Br), relative permeability (Mr), electrical resistivity (r), and thermal conductivity (k).
Performance Evaluation of MIMO Radars Under a Generalized Model of Array Imperfections

This article evaluates the localization accuracy of colocated multiple-input–multiple-output (MIMO) radars under receiver impairments using a generalized errors model. Specifically, the receiver's gain and phase imperfections are modeled using Rician and von Mises distributions while the antenna location errors are modeled by a Gaussian distribution. The joint estimation of the direction of arrival (DOA) and Doppler frequency is compared for several estimation methods including multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT), and maximum likelihood estimation. In addition to array model defects, the impact of transmitted signals correlation on the considered localization algorithms is investigated. Moreover, the Fisher information matrix (FIM) and Cramer–Rao lower bound (CRLB) are derived to evaluate the asymptotic performance and quantify the impact of the receiver imperfections and signals correlation on the aforementioned estimation methods. Results show that while all methods are sensitive to receiver's impairments, the ESPRIT method is the most vulnerable and may fail completely under some operating conditions. A perfect match with corresponding simulation results demonstrates the validity of the derived expressions.
Information Magnitude Based Dynamic Sub-sampling for Speech-to-text

Attention-based models have achieved new state-of-the-art in many tasks while the computational cost of these models increases drastically compared with previous methods. For most acoustic tasks, excessively long speech sequences exacerbate this problem and do not benefit a lot from the attention mechanism. We propose the information magnitude (IM) based dynamic stride convolution (IM-DSC) method. This method first calculates the IM according to the importance of each frame, then dynamically squeezes the redundant frames. We carry on experiments on speech translation and automatic speech recognition tasks. Our results show that we achieve 0.5 BLEU and 0.4 BLEU improvements on the MuST-C En-De and En-Fr datasets with a 22% compression ratio. For the ASR task, we gain a 0.2 WER reduction with a 21% compression ratio on the Lib-rispeech dataset.
Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning

Identifying high-quality (i.e., relatively clear) measurements of surface conditions is a near-universal first step in working with optical satellite imagery. Many cloud masking algorithms have been developed to characterize the likelihood that reflectance measurements for individual pixels were influenced by clouds, cloud shadows, and other atmospheric effects. However, due to the continuous density of the atmospheric volume, we argue that quantification of occlusion and corruption effects is better treated as a regression problem rather than a discretized classification problem as done in prior work. We propose a space-time context network trained using a bootstrapping procedure that leverages millions of automatically-mined video sequences informed by a weakly supervised measure of atmospheric similarity. We find that our approach outperforms existing machine learning and physical basis cloud and cloud shadow detection algorithms, producing state-of-the-art results for Sentinel-2 imagery on two different out-of-distribution reference datasets. The resulting product offers a flexible quality assessment (QA) solution that enables both standard cloud and cloud shadow masking via thresholding and more complex image grading for compositing or downstream models. By way of generality, minimal supervision, and scale of our training data, our approach has the potential to significantly improve the utility and usability of optical remote sensing imagery.
Xanthones with Potential Anti-Inflammatory and Anti-HIV Effects from the Stems and Leaves of Cratoxylum cochinchinense

Four new xanthones, cratocochinones A–D (1–4), together with eight known analogues (5–12), were isolated from the stems and leaves of Cratoxylum cochinchinense. The chemical structures of cratocochinones A–D (1–4) were elucidated by comprehensive spectroscopic analyses and the known compounds were identified by comparisons with the spectral data reported in the literature. All isolated compounds 1–12 were evaluated for their anti-inflammatory activities and anti-HIV-1 activities. Compounds 1–12 showed remarkable inhibitory activities on nitric oxide (NO) production induced by lipopolysaccharide in mouse macrophage RAW 264.7 cells in vitro, with IC50 values in the range of 0.86 ± 0.05 to 18.36 ± 0.21 µM. Meanwhile, compounds 1–12 exhibited significant anti-HIV-1 activities with EC50 which ranged from 0.22 to 11.23 µM. These findings indicate that the discoveries of these xanthones, isolated from the stems and leaves of C. cochinchinense, showing significant anti-inflammatory and anti-HIV-1 effects could be of great importance to the research and development of new natural anti-inflammatory and anti-HIV agents.
Burden of Chikungunya Virus Infection during an Outbreak in Myanmar

Chikungunya virus (CHIKV) infection is a re-emerging arboviral disease with no approved vaccine, although numerous options are in development. Before vaccine implementation, disease burden, affected age group, and hospitalization rate information should be documented. In 2019, a sizeable outbreak of the East Central South African genotype of CHIKV occurred in Myanmar, and during this period, a cross-sectional study was conducted in two regions, Mandalay and Yangon, to examine the molecular and seropositivity rate of the CHIKV infection. The participants (1124) included dengue-suspected pediatric patients, blood donors, and healthy volunteers, who were assessed using molecular assays (quantitative real-time RT-PCR), serological tests (anti-CHIKV IgM capture and IgG indirect enzyme-linked immunosorbent assays), and neutralization tests. The tests confirmed the following positivity rates: 11.3% (127/1124) for the molecular assay, 12.4% (139/1124) for the anti-CHIKV IgM Ab, 44.5% (500/1124) for the anti-CHIKV IgG Ab, and 46.3% (520/1124) for the CHIKV neutralizing Ab. The highest rate for the molecular test occurred with the dengue-suspected pediatric patients. The seroprevalence rate through natural infection was higher in the healthy volunteers and blood donors than that in the pediatric patients. The results of this study will help stakeholders determine the criteria for choosing appropriate recipients when a CHIKV vaccine is introduced in Myanmar.
Mechanisms and Clinical Research Progress of Rituximab in the Treatment of Adult Minimal Change Disease

Introduction: Minimal change disease (MCD) is a common subtype of primary nephrotic syndrome in adults. The pathogenesis of MCD is still not well understood, but some studies suggest that MCD is a T cell-mediated disease related to podocyte dysfunction. Previous research has also indicated the crucial role of B cells in the pathogenesis of MCD. Rituximab (RTX) is a recombinant chimeric mouse/human antibody targeting CD20 antigen. In recent years, RTX has been increasingly used in adult MCD patients. Methodology: We searched the PubMed database using the keywords “Minimal change disease”, “Nephrotic syndrome”, and “Rituximab” and obtained a total of 140 articles. We will now provide a literature review based on these 140 articles, according to our research topic. Discussion: This article provides an overview of the mechanisms and clinical research progress of RTX in the treatment of adult MCD. We have also discussed the current treatment methods for MCD, exploring the potential of using RTX as a first-line therapy for refractory adult MCD. Conclusion: MCD is a common pathological type of nephrotic syndrome, and the exact mechanisms are still not fully understood. Although RTX as a treatment of adult MCD has shown promising clinical results in patients with refractory adult MCD, the safety and efficacy of RTX still lack high-quality clinical evidence. Further research is needed to explore the pathogenesis of MCD and the RTX treatment for MCD.
Soil water nitrate concentrations in giant cane and forest riparian buffer zones

Soil water nitrate concentrations in giant cane and forest riparian buffer zones along Cypress Creek in southern Illinois were compared to determine if the riparian zones were sources or sinks for nitrogen in the rooting zone. Suction lysimeters were used to collect soil water samples from the lower rooting zone in each of the two vegetation types. The cane riparian zone had a significantly lower mean soil water nitrate-N concentration (0.45 mg L-1) than the forest riparian buffer zone (4.29 mg L-1). This was attributed to higher litter C/N ratios in the cane, which likely resulted in greater microbial immobilization of nitrogen within the rooting zone.
The Performance of Machine Learning for Chronic Kidney Disease Diagnosis

This paper aims to review the performance of different machine learning (ML) models and develop models for the automated diagnosis of chronic kidney disease. To detect chronic kidney disease with better precision, selecting the right and better-performing ML model is significant as it improves the precision and accuracy of the chronic kidney disease diagnosis. The study uses the Joana Briggs Institute (JBI) scoping review methodology, which involves different steps such as searching relevant literature, conducting the review, and reporting the review result. In the search, the year of publication and the indexing of journals where the studies are published is used as inclusion and exclusion criteria. The review result shows that the current chronic kidney disease detection has focused on the development of ensemble-based and deep-learning methods. The deep learning method can achieve a higher accuracy of 99.75%.
Robust Data Exchange Across Wind Turbines

Wind farms play an important role in the smart grid initiative. Most wind turbines are deployed in open areas and their wireless communication layer needs to be robust. In this paper, the concept of information-theoretic secrecy is introduced to establish some benchmarks of robust communication. Compatible with the common topology of wind turbine deployment, the secrecy analysis is conducted in the framework of cooperative communication with a generic relay configuration. The analysis takes several issues into account, such as fading, line-of-sight, and multi-scattering. We derive several benchmark metrics for this system. These metrics are explicitly expressed in the closed-form. The availability of these solutions helps directly gain the deep insights, while reducing the computational load of tedious simulations.