Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Informational Masking due to Masker Intelligibility in Same-talker Speech-in-speech Perception

Intelligibility of the competing speech plays a significant role in causing informational masking (IM) to the target speech during speech-in-speech perception, especially in same-talker conditions where the target and the masker share a large number of similarities in acoustics. Few studies have quantitatively measured IM as a function of intelligibility of competing speech. Evidence shows that voiced segments are robust cues for speech intelligibility. In this study, the contribution of masker intelligibility to IM was studied by adjusting the voice-to-noise ratio (VNR) on voiced segments of the competing speech, while maintaining energetic masking (EM) at different target-to-masker ratios. Although model estimations suggested that the intelligibility due to EM converged when VNR < 0 dB, listener performance showed that more release from IM was received with a further decrease in VNR. It was projected that masker intelligibility could lead to target intelligibility decreased by 50%.
Improved Deep Reinforcement Learning Algorithms and Applications in Quantitative Trading in Extreme Stock Markets

: Aiming at the issue that existing deep reinforcement learning algorithms cannot achieve satisfactory returns in the quantitative trading process, particularly during extreme stock market conditions such as sharp declines, we propose an improved TD algorithm based on immediate reward 𝑅 (cid:3047) , which we name as RTD. We further extend RTD to multi-step conditions (MRTD) and apply it to enhance algorithms such as DQN and DDPG. We then utilize these two improved algorithms in the stock quantitative trading process. Experimental results demonstrate that our proposed algorithms can respond to market changes more efficiently, resulting in improved accuracy of investment strategies and higher investment return rates, even during extreme market conditions. For instance, when the market declines by more than 10%, and the two specified stocks decline by nearly 5.09% and 13.30%, we can still obtain return rates of 2.78% and 4.19% respectively, which confirms the effectiveness of our proposed algorithms.
Memory-augmented conformer for improved end-to-end long-form ASR

Conformers have recently been proposed as a promising modelling approach for automatic speech recognition (ASR), outperforming recurrent neural network-based approaches and transformers. Nevertheless, in general, the performance of these end-to-end models, especially attention-based models, is particularly degraded in the case of long utterances. To address this limitation, we propose adding a fully-differentiable memory-augmented neural network between the encoder and decoder of a conformer. This external memory can enrich the generalization for longer utterances since it allows the system to store and retrieve more information recurrently. Notably, we explore the neural Turing machine (NTM) that results in our proposed Conformer-NTM model architecture for ASR. Experimental results using Librispeech train-clean-100 and train-960 sets show that the proposed system outperforms the baseline conformer without memory for long utterances.
Who Benefits From The Export-Import Bank Aid?

We study the effectiveness of government aid to exporters by exploring an exogenous shock that affected the ability of the Export-Import Bank of the United States (EXIM) to provide aid to U.S. exporters through loan guarantees to importers. We focus on Boeing, the largest individual recipient of aid. We find that Boeing sales declined only modestly – despite Boeing’s significant reliance on EXIM for export credit. Moreover, we find that this decline is driven by financially constrained airlines or by airlines operating in countries with underdeveloped financial systems. We show that airlines in developed countries were easily able to substitute EXIM guaranteed loans for private credit and thus could still purchase Boeing aircraft despite the EXIM shock. Our results are consistent with the view that government-sponsored export credit is mostly relevant for importers in countries with underdeveloped financial systems, which represent a relatively small share of total EXIM aid.
Inclusion of a Body Wave Seismic Moment Tensor Estimator in the CTBTO/IDC Processing Suite

Abstract : The experimental SELO system of the CTBTO's Preparatory Commission (LeBras et al., 2006) now includes a moment tensor (MT) and moment magnitude estimate. The SELO system rapidly detects and locates events. The effort to include further processing of large natural events, and get a better size estimate for large natural events than the mb magnitude was stated in the summer of 2005. The basic scientific software that performs the MT inversion was acquired from Y. Yagi from Tsukuba University, Japan. It performs a time-domain, long-period body-waves MT inversion soon after an event is automatically detected and located, within about 20 minutes after its occurrence. We wrote signal processing scripts to prepare the broad-band International Monitoring System (IMS) vertical seismic data for MT inversion, including deconvolution of the complete instrument responses. We included the scientific software into the Commission's environment and have now achieved its seamless integration through the web page displaying the results of the SELO bulletin. The addition of the moment tensor inversion module allows us to estimate the size and focal mechanism of the detected events, which is of interest for better characterization of the event in a limited time frame. This is important since the only available measure of the size of the earthquake for SELO events was the mb scale, which saturates for larger events. We have been able to interactively run a number of inversions and we present case examples and statistics from selected events since December 2006.
Toward environmentally sustainable supply chains: How contract management can help companies along their transformation journey

With the European Union having set ambitious environmental policy targets, company executives face mounting pressure to reach their sustainability goals. While expanding the contract-management process's utilization may hold promise for accelerating sustainable development throughout the contract lifecycle (from the pre-award through the post-award phase), the literature has afforded it little attention. Building on contingency theory and a qualitative multiple-case-study method, the paper examines how the contract-management process could proactively enhance the necessary shift to environmentally sustainable supply chains. Further, the authors explore whether contract management as a contingency variable could exert a positive influence on the sustainability practices followed in supply-chain relationships and, thereby, on sustainability performance. The preliminary findings indicate that, while expanded utilization of contract management remains modest, it already shows potential as a future success factor for companies on their journey of transformation in pursuit of sustainable supply-chain relationships. As a contingency factor, contract management might be able to make an impact by embedding sustainability in the contract process from the supplier-selection phase onward while also contributing to the final contract's solid inclusion of sustainability-related duties for the parties, whether in the main contract or via appendices. Accordingly, the authors suggest multiple, parallel avenues for future development.
A Correlational Study Between Executive Functions and Conditional Discriminations in Children with Autism

Abstract Neuropsychology is a science that allows tracing the profile of cognitive impairments and preserved skills to design appropriate treatments and educational practices aiming at a better quality of life for the individual. This is basic correlational research, which objective was to verify if the results found in the executive functions in children with autism spectrum disorder (ASD) are predictive of or have some correlation with the performance in conditional discrimination through choice tasks according to the identity matching-to-sample (MTS) model. Correlations revealed significant associations between neuropsychological tests and MTS tasks. Future research may further explore MTS tasks for the assessment and intervention of individuals with ASD.
Hybrid SOMP-MUSIC-Based Channel Estimation Scheme for Terahertz Massive MIMO-OFDM Systems

The channel estimation procedure is quite challenging in Terahertz (THz)-band communication systems due to the beam-split effect. This occurrence emerges in ultra-broadband THz signals that have large fractional bandwidths. In such a scenario, the components of the THz path at various subcarriers split into diverse spatial directions with the consequence of array gain loss. This results in a beam split. Most of the previous channel estimation techniques documented in the literature neglect the effect of the beam split which is not realistic in the real-time scenario. In this paper, a compressive sensing technique that considers the beam split effect is proposed for channel estimation in THz Massive Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system. The proposed estimator is based on the hybrid of simultaneous orthogonal matching pursuit (SOMP) and MUltiple SIgnal Classification (MUSIC) algorithms and is named the hybrid (H) SOMP-MUSIC channel estimator. It employs a time-delay network to mitigate the effect of beam split. By using the inherent sparsity in the THz channel, the proposed HSOMP-MUSIC estimator can estimate the channel with few pilots than the traditional OMP and SOMP techniques. In comparison with another method that incorporated the beam split effect into the estimation procedure, and two other methods that neglect the effect, the proposed HSOMP-MUSIC-based estimator exhibits better achievable performance.
Co-integrated Non-Volatile Charge Trap Memory with III-V/Si Photonics

We review results on non-volatile charge-trap memory (CTM) integrated with III-V/Si photonics. The III-V/Si CTM cell facilitates non-volatile functionality for a variety of devices such as Mach-Zehnder Interferometers (MZIs), asymmetric MZI lattice filters, and ring resonators.
Prescriptive Maintenance of Freight Vehicles using Deep Reinforcement Learning

Supply chain disruptions caused by breakdown of freight vehicles lead to delayed deliveries which cost companies millions of dollars and loss of customer goodwill. Breakdowns can be reduced through predictive maintenance, which has become a mature field with solutions offered by various vendors. In this paper, we propose a prescriptive maintenance approach that leverages deep reinforcement learning (DRL) to directly make maintenance decisions for a fleet of freight vehicles such as trucks. Proximal Policy Optimization (PPO) is a state-of-the-art reinforcement learning (RL) algorithm based on policy gradient and uses function approximators like deep neural networks (DNNs) to store the policy and value functions. In order to introduce long timescale memory that can lead to superior policies in complex problems, we integrate the PPO with a Long Short Term Memory (LSTM) network. The resulting PPO-LSTM scheme requires careful handling of sequences of observations. We investigated the performance of the PPO-DNN and PPO-LSTM schemes in making prescriptive maintenance decisions for a fleet of trucks transporting goods between factories. From sensor readings indicating the condition of the truck transmission system which deteriorate under use, good maintenance decisions are made that enable a large number of trucks to remain active. The performance of these schemes were evaluated in realistic simulations of different traffic conditions using the SUMO simulator working in conjunction with realistic truck transmission system and factory production simulators. Our results show that the proposed schemes outperformed baseline schemes and achieved a significant increase in production throughput under different traffic conditions and maintenance and repair durations.
A New Resource Management Technique in 3D Wireless Networks

This paper proposes a new access link (AL) and backhaul link (BL) resource management technique for use in 3-dimensional (3D) aerial networks. Increased traffic resulting from the emergence of various applications for unmanned aerial vehicles (UAVs) has resulted in a scarcity of resources. The non-orthogonal shared resource allocation (NSRA) framework proposed here solves the problem of resource scarcity by allocating resources to be shared non-orthogonally between the AL and the BL. In addition, we propose a new selective AL-BL resource sharing algorithm assisted by successive interference cancellation (SIC). We also define the resource sharing allowable zone of the 3D aerial network based on air-to-air channel characteristics. Numerical results show that the proposed algorithm enhances the data rate of the 3D network compared with orthogonal resource allocation.
Online Tensor Method for Moving Objective Detection with FMCW Radar

Frequency modulated continuous wave (FMCW) radar can precisely detect moving objects utilizing the Doppler information. However, only exploiting the Doppler information in one frame can usually lead to object false detection when static background has large radar cross section or the moving objective occludes some static background. In this paper, we investigate the moving objective detection problem with FMCW radar through utilizing the Doppler information in multiple frames to increase objective detection accuracy. To solve this problem, an online tensor robust principal component analysis (RPCA) algorithm is proposed with low hardware and computation complexity. The proposed algorithm can maintain the intrinsic tensor data structure. Experimental results show that the proposed algorithm can accurately detect the static background and moving object even for the case of occlusion or static object with large RCS.
End-to-end Neuromorphic Lip Reading

Human speech perception is intrinsically a multi-modal task since speech production requires the speaker to move the lips, producing visual cues in addition to auditory information. Lip reading consists in visually interpreting the movements of the lips to understand speech, without the use of sound. It is an important task since it can either complement an audio-based speech recognition system or replace it when sound is not available. We introduce in this paper a neuromorphic model for lip reading, that uses events produced by an event-based sensor capturing lips motion as input, and that classifies short event sequences in word categories based on a SNN architecture. Experimental results show that the proposed model successfully leverages various advantages of neuromorphic approaches such as energy efficiency and low latency, which are central features in real-time embedded scenarios. To the best of our knowledge, it is the first proposal of an end-to-end neuromorphic lip reading model.
Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach

The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.
Individual Postprandial Glycemic Responses to Meal Types by Different Carbohydrate Levels and Their Associations with Glycemic Variability Using Continuous Glucose Monitoring

This study aimed to investigate individual postprandial glycemic responses (PPGRs) to meal types with varying carbohydrate levels and examine their associations with 14-day glycemic variability using continuous glucose monitoring (CGM) in young adults. In a two-week intervention study with 34 participants connected to CGM, four meal types and glucose 75 g were tested. PPGRs were recorded for up to 2 h with a 15 min interval after meals. Data-driven cluster analysis was used to group individual PPGRs for each meal type, and correlation analysis was performed of 14-day glycemic variability and control with related factors. Participants had a mean age of 22.5 years, with 22.8% being male. Four meal types were chosen according to carbohydrate levels. The mean glucose excursion for all meal types, except the fruit bowl, exhibited a similar curve with attenuation. Individuals classified as high responders for each meal type exhibited sustained peak glucose levels for a longer duration compared to low responders, especially in meals with carbohydrate contents above 50%. A meal with 45% carbohydrate content showed no correlation with either 14-day glycemic variability or control. Understanding the glycemic response to carbohydrate-rich meals and adopting a meal-based approach when planning diets are crucial to improving glycemic variability and control.
Comparative study of the management and organization of irrigation projects

This paper is primarily concerned with development of an analytical framework for the monitoring and evaluation of irrigation project management in developing countries. It is based on an extensive review of the English language literature on the subject, and four field studies in South and East Asia. The central part of the report consists of a series of detailed arguments to explain and justify the particular evaluation framework prepared. A principal focus is the performance of the activity of water distribution since it is peculiar to irrigated agriculture. Special attention is given to identifying and recommending those organizational structures and management procedures which appear most likely to serve the interests of the rural poor, particularly small farmers and those at the tail-end of irrigation systems. The study concludes that there are immense opportunities for improvements in the performance of irrigation projects through management reform and better water distribution. Appendix A summarizes guidelines for analysis in a checklist format. Recommendations for further evaluation and follow-up action are given.
The Potential Utilizing ChatGPT for Education and Teaching Students: Understanding, Prospects, Challenges, and Utilization"

This research aims to analyze the development of the ChatGPT topic in the context of its utilization and the challenges in supporting 21st-century education and learning programs that require the development of learning media as well as mapping the risks associated with the presence of AI (Artificial Intelligence) phenomenon, specifically ChatGPT. This requires the development of learning media as well as the mapping of risks associated with the presence of AI phenomenon, specifically ChatGPT. The method used in this study is a literature review related to ChatGPT, gathered from the Google database using the Google Advanced search strategy. The keywords "ChatGPT," "education," and "learning" were used to access various publications, particularly national and international scholarly articles that discuss relevant topics. The research findings generally indicate that ChatGPT is currently popular and dynamic, and it is predicted to continue growing in the future. However, discussions specifically related to ChatGPT in the field of education and learning are still the subject of debate, with both supporters and dissenters presenting rational arguments. Several examples demonstrate the significant positive benefits of ChatGPT's presence. To avoid confusion, it is crucial to establish policies formulated by authorized entities and implement them nationally with various supporting regulations.
Composite Effective Degree Markov Chain for Epidemic Dynamics on Higher-Order Networks

Epidemiological models based on traditional networks have made important contributions to the analysis and control of malware, disease, and rumor propagation. However, higher-order networks are becoming a more effective means for modeling epidemic spread and characterizing the topology of group interactions. In this article, we propose a composite effective degree Markov chain approach (CEDMA) to describe the discrete-time epidemic dynamics on higher-order networks. In this approach, nodes are classified according to the number of neighbors and hyperedges in different states to characterize the topology of higher-order networks. By comparing with the microscopic Markov chain approach, CEDMA can better match the numerical simulations based on Monte Carlo and accurately capture discontinuous phase transitions and bistability phenomena caused by higher-order interactions. In particular, the theoretical solution to CEDMA can well predict the critical point at continuous phase transition and corroborate the existence of the discontinuous phase transition in the susceptible–infectious–susceptible (SIS) process. Moreover, CEDMA can be further extended to depict the susceptible–infectious–recovered (SIR) process on higher-order networks.
The experiences and needs of parents of children with asthma in coping with the child’s disease

About 14% of children have asthma, and despite effective treatments, the outcome is not always as expected, with hospitalisation rates remaining high. Parents of children with asthma experience stress as they doubt their ability to cope with the difficult circumstances. Parents complain that they do not have sufficient information on the reduction of factors associated with disease exacerbation. They need knowledge of how to effectively participate in coping with the disease. The aim of the research is to describe the experiences and needs of parents of children with asthma in coping with the child’s disease. The design of the research is qualitative and descriptive. The data were collected through semi-structured interviews and analysed by an inductive content analysis method. The interviews were conducted in the autumn of 2021. The subjects were found with the help of the Tallinn Children’s Hospital staff. Eight subjects participated in the research. The results of the study showed that the parents’ experiences in coping with the child’s disease consisted of health-related experiences, life management and material challenges, experiences with relationships, and various support systems. The needs of parents in dealing with the child’s disease included the need for various services, the need for information, the need to support the child, and health needs of both the parents and the child. Parents experienced socio-economic difficulties and incomplete information when coping with the child’s disease. For this reason, families should receive support from various specialists and organisations, and the role of nurses is to be a connecting link between families and support services.