Miracle-dz/UniFAR-FTUs huggingface dataset:
POST-RESTRUCTURING GOVERNANCE: AN EXAMINATION OF INTERLOCKING DIRECTORATES

This study presents an empirical examination of firm restructuring and interlocking directorates (i.e., board interlocks) in the post-asset restructuring period. Portfolio restructuring is an important area of study as it represents a shift in an organization’s domain and involves critical decisions that impact firm boundaries and the allocation of resources. Drawing upon agency and institutional theories, we suggest firms experiencing poor performance will restructure and redesign their governance structures, namely the number of board interlocks, in the postrestructuring period so as to enhance organizational legitimacy. Additionally, in response to institutional pressures for improved performance, organizations may choose to co-opt the source of these pressures as a means of averting threats to its stability or existence. The results reveal a positive relationship between divestiture size and the number of board interlocks in the postrestructuring period. The findings address a significant gap in the extant literature by addressing the need for further research examining firms’ propensity to implement corporate governance changes following a restructuring.
Convolutional Neural Network Based Classification of WeChat Mini-Apps

In recent years, a novel mobile computing paradigm has been evolving rapidly, with a host app allowing users to install and run mini-apps inside the app itself. However, the current classification mechanism of mini-apps is blurry and coarse-grained, making users unable to clearly understand mini-app functions, which can result in a series of privacy issues. In this study, an automatic convolutional neural network (CNN)-based classification approach is proposed for Wechatmini-apps. The proposed method integrates the static and dynamic features of WeChat mini-apps to achieve precise classification. Our approach was evaluated in a real-world testbed and the results showed that it can effectively classify Wechatmini-apps into proper categories, helping users better understand the functions of WeChat mini-apps while reducing user privacy violations.
Reliable and Streaming Truth Discovery in Blockchain-based Crowdsourcing

Truth discovery is an effective and compelling approach to addressing data conflicts among different workers and offers more trustworthy truths to task requesters in crowdsourcing. Prior research either focused on studying more accurate truth discovery algorithms or aimed to protect data privacy from the centralized and honest-but-curious crowdsourcing platforms. They all overlooked the stronger threats from the malicious crowdsourcing platform (e.g., may return incorrectly estimated truths) and many critical issues inherited from centralization. This paper proposes a blockchain-based decentralized truth discovery scheme for crowdsourcing, with computation integrity guarantees against malicious participants and support for efficient processing of generic streaming data. We adopt the idea of hybrid storage and computations to ease the expensive on-chain cost. Workers are grouped for off-chain partial truth estimation and smart contracts are leveraged for on-chain final truth aggregation. To prevent any improper computations from malicious entities, we record the hashes of data and worker weights on-chain occasionally. Through theoretical analysis and extensive experiments over real-world and synthetic datasets implemented in Ethereum, we demonstrate that our scheme 1) achieves our reliability goals with certain privacy assurance; 2) exhibits a higher truth estimation accuracy than existing approaches and a lower gas consumption than the baseline.
OFDM-Based Synchronous PNC Communications Using Higher Order QAM Modulations

Physical-layer Network Coding (PNC) has great potential to improve the throughput and latency in wireless networks. However, there are two main challenges in PNC systems that do not exist in the conventional Point-to-Point $(\mathrm{P}2\mathrm{P})$ communication systems: 1) time and frequency asynchrony of the paired PNC users; and 2) ambiguity of the PNC mapping at the relay node. To address these challenges, in this paper, we apply precoding for power control and phase synchronization of the paired PNC users, while we use modulo $-\sqrt{M}$ addition for the PNC mapping ambiguity removal in higher-order M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of the Orthogonal Frequency Division Multiplexing (OFDM)-PNC systems with cyclic prefix extension under Rayleigh faded Tapped Delay Line (TDL)-C and Rician faded TDL-D channel models, proposed by the Third Generation Partnership Project $(3\text{GPP})$, as well as the Additive White Gaussian Noise (AWGN) channel model. The results reveal that our proposed technique can achieve a significant Signal-to-Noise Ratio (SNR) improvement of 12 $\text{dB}$ over its asynchronous OFDM-PNC counterpart (without precoding) for Binary Phase Shift Modulation (BPSK) under a TDL-C faded channel model. Moreover, without channel coding, our proposed PNC technique requires an SNR of around $13\text{dB}$ to deliver a two-way 16-QAM communication at a Bit Error Rate of 10−3 under a Rayleigh faded TDL-C channel.
An Ensemble Link Prediction Framework With AUC-Guided Leaderboard Probing For Volunteer Collaboration Prediction Challenge

This paper presents the first-place solution to the Volunteer Collaboration Prediction Challenge, addressing two primary challenges: the time-evolving nature of the network and the multi-step prediction task. To effectively capture the timeevolving characteristics of the collaboration network, we design an ensemble link prediction framework that leverages diverse topological features derived from a sequence of network snapshots. Additionally, in order to gather valuable insights for different prediction time steps, we propose an AUC-guided leaderboard probing strategy based on a unique characteristic of the testing set. The ensemble framework demonstrates exceptional performance in this competition, showcasing its effectiveness in practical volunteer collaboration prediction scenarios. Furthermore, the AUC-guided leaderboard probing strategy provides empirical evidence for potential vulnerabilities in the evaluation settings of Kaggle challenges.
Flophet: A Novel Prophet-Based Model for Traffic Flow Prediction in Vehicular Ad Hoc Networks

The rapid economic growth along with the population concentration in urban areas have led to caused urban traffic problem. It is one of the most serious problems in big cities that people have to deal in daily life. In recent years, researchers show wide interest in overcoming traffic issues where new models and frameworks have been rapidly developed for efficient traffic management in suitable vehicular environments; the emergence of Vehicular Ad Hoc Networks (VANETs). Nevertheless, traffic flow prediction is a major challenge in VANETs that has taken much attention. Subsequently, performing accurate and real-time traffic flow prediction plays an important role in reducing traffic congestion, saving traveler time, improving traffic safety, detect accidents rapidly, and reduce infrastructure damage. In this paper, we propose an efficient traffic prediction model called prophet traffic flow predictor (Flophet) for vehicular ad hoc networks. In this model, two major enhancement on the traditional neural prophet model were done. First, we propose an efficient algorithm for predicting the traffic flow trend and, then, we implement a new future regressor component called network mobility. Through simulations in real VANET data, we show the relevance of Flophet compared to other existing models.
Cerebrospinal Fluid Proteomic Changes after Nusinersen in Patients with Spinal Muscular Atrophy

Disease-modifying treatments have transformed the natural history of spinal muscular atrophy (SMA), but the cellular pathways altered by SMN restoration remain undefined and biomarkers cannot yet precisely predict treatment response. We performed an exploratory cerebrospinal fluid (CSF) proteomic study in a diverse sample of SMA patients treated with nusinersen to elucidate therapeutic pathways and identify predictors of motor improvement. Proteomic analyses were performed on CSF samples collected before treatment (T0) and at 6 months (T6) using an Olink panel to quantify 1113 peptides. A supervised machine learning approach was used to identify proteins that discriminated patients who improved functionally from those who did not after 2 years of treatment. A total of 49 SMA patients were included (10 type 1, 18 type 2, and 21 type 3), ranging in age from 3 months to 65 years. Most proteins showed a decrease in CSF concentration at T6. The machine learning algorithm identified ARSB, ENTPD2, NEFL, and IFI30 as the proteins most predictive of improvement. The machine learning model was able to predict motor improvement at 2 years with 79.6% accuracy. The results highlight the potential application of CSF biomarkers to predict motor improvement following SMA treatment. Validation in larger datasets is needed.
Alloplasmic lines of beets with new cytoplasm from the wild genus Beta patula

The purpose of the study was to obtain new breeding genotypes containing a nuclear genome of a sugar beet sterility maintainer and sterile cytoplasm of wild beet ( Beta patula L.). The donors of a new sterile cytoplasm of B. patula plants were selected by the indirect cytological traits of cytoplasmic male sterility (CMS), especially pollen sterility, abnormal development of the anthers, mosaic patterns of anthers and flowers. The anthers were examined for morphological traits of pollen and microspore degeneration in CMS. During reciprocal crosses, (i) the expression of recessive alleles of the nuclear genes of the CMS sterility maintainers was analysed, (ii) the peculiarities of hybridisation of the experimental genotypes with sterile B. patula cytoplasm were studied, and (iii) the selection of linked genes for hypocotyl colour Rr and one-or two-year growth cycle Bb was carried out. The obtained backcross offspring of BC 3 S B. patula had 95% fertility, 100% CMS-0 type sterility, and sugar content between 19.5% and 21.5%. Recombination in the locus of linked genes of the one-and two-year growth cycles and hypocotyl colour RB rb in hybrids F 1 , B. patula and backcross offspring was detected. At the same time, the one-and two-year growth cycle in seed plants was restored only in the 4th cycle of backcross. Recombinant genotypes with a red hypocotyl and two-year growth cycle BC 3 S B. patula had multi-seed fruits, single or branched stems of seed plants and were chimerical by sterility (CMS-1 and CMS-0 types). The obtained alloplasmic introduced lines containing the cytoplasm of B. patula have economically valuable properties as parent components for the development of new triploid hybrids. The experimental triploid hybrids had root sugar content from 19.5% to 20.5% and dry matter content from 25% to 29%. Alloplasmic lines with the new CMS of B. patula are recommended for sugar beet breeding.
THz Near-Field Codebook Design and Fast Beam Training with Grating Lobes

Beam training is regarded as an efficient beamforming strategy for Terahertz (THz) communications, where the line-of-sight propagation component is dominant in THz signal propagation. Nevertheless, the near-field effect boosts the codebook size for the beam training since each near-field beam merely concentrates the radiation in a small spatial area, rather than along a direction regardless of distance. In this paper, we propose to utilize grating lobes to accelerate the beam training process. Specifically, an explicit model is proposed for characterizing the beam patterns of the main lobe and grating lobes. Based on this model, we propose a codebook design and a fast beam training scheme for THz near-field communications, where multi-beams are generated simultaneously with only one RF chain. Numerical results show the effectiveness and superiority of using grating lobes compared to the benchmarks, with less beam training overhead while achieving almost the same rate performance as that by the perfect CSI case.
Exploring the Interplay of Muscular Endurance, Functional Balance, and Limits of Stability: A Comparative Study in Individuals with Lumbar Spondylosis Using a Computerized Stabilometric Force Platform

Lumbar spondylosis, characterized by degenerative changes in the lumbar spine, often leads to pain, reduced spinal stability, and musculoskeletal dysfunction. Understanding the impact of lumbar spondylosis on musculoskeletal function, particularly lumbar extensor endurance, functional balance, and limits of stability, is crucial for improving the management and well-being of affected individuals. This study aimed to assess lumbar extensor endurance, functional balance, and limits of stability in individuals with lumbar spondylosis compared to age-matched healthy individuals and explore the correlations among these parameters within the lumbar spondylosis group. The lumbar spondylosis group consisted of 60 individuals initially screened by an orthopedician and referred to physical therapy. Age-matched healthy controls (n = 60) were recruited. Inclusion criteria encompassed adults aged 45–70 years for both groups. Lumbar extensor endurance was assessed using the Sorensen test, functional balance with the Berg Balance Scale, and limits of stability using a computerized stabilometric force platform. Lumbar extensor endurance was significantly lower in individuals with lumbar spondylosis compared to healthy controls (23.06 s vs. 52.45 s, p < 0.001). Functional balance, as assessed by the Berg Balance Scale, demonstrated a significant decrement in the lumbar spondylosis group (48.36 vs. 53.34, p < 0.001). Additionally, limits of stability variables, under both eyes-open and eyes-closed conditions, exhibited marked impairments in the lumbar spondylosis group (p < 0.001 for all variables). Within the lumbar spondylosis group, lumbar extensor endurance exhibited significant positive correlations with functional balance (0.46, p < 0.001) and negative correlations with limits of stability variables (r ranging from −0.38 to −0.49, p < 0.01 for all variables). This study underscores the significance of addressing lumbar extensor endurance, functional balance, and stability impairments in the comprehensive management of lumbar spondylosis.
Channel Charting: Model-Based Approaches

We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment $p$ by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon's method (SM), and autoencoder (AE) from [1]. We show that ISQ, LR, and MM outperform all three in performance. ISQ and LR have similar performance with ISQ having less complexity than LR. The performance of MM is better than ISQ and LR but it is more complex. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM algorithm but is less complex due to the avoidance of the eigenvector and eigenvalue analysis and a potential register transfer logic (RTL) implementation.
Accelerating Network Coding with Programmable Switch ASICs

Random Linear Network Coding holds great potential for enhancing the performance of mega-constellation networks. However, its implementation brings several challenges such as complexity in matrix operations, bandwidth limitations, and increased latency. Although some CPU- and GPU-based solutions have achieved sufficient coding throughput, the gateway stations of satellites require processing rates greater than 10 Gbps while maintaining sub-millisecond delays, which is a challenge with current solutions. In this study, we present and assess efficient RLNC encoding strategies for programmable hardware pipelines, such as the widely adopted Tofino chip with multiple programmable packet parsers and match-action stages. Our approach differs from existing RLNC implementations by offloading the demanding matrix operations from the CPU to the programmable network switch hardware pipeline. Additionally, we optimize logical table ID and other resource utilization by scheduling matrix multiplications across multiple stages. We performed a preliminary evaluation of our design on the Tofino switch and observed a significant improvement in RLNC encoding latency, with a reduction to sub-millisecond levels. Moreover, our design outperforms state-of-the-art solution in terms of throughput when the generation size is greater than 100.
The Weakening of the Stratospheric Polar Vortex and the Subsequent Surface Impacts as Consequences to Arctic Sea-ice Loss

This study investigates the stratospheric response to Arctic sea-ice loss and subsequent near-surface impacts by analyzing 200-member coupled experiments using the Whole Atmosphere Community Climate Model version 6 (WACCM6) with preindustrial, present-day, and future sea-ice conditions specified following the protocol of Polar Amplification Model Intercomparison Project. The stratospheric polar vortex weakens significantly in response to the prescribed sea-ice loss, with a larger response to greater ice loss (i.e., future minus preindustrial) than to smaller ice loss (i.e., future minus present-day). Following the weakening of the stratospheric circulation in early boreal winter, the coupled stratosphere-troposphere response to ice loss strengthens in late winter and early spring, projecting onto a negative North Atlantic Oscillation-like pattern in the lower troposphere. To investigate whether the stratospheric response to sea-ice loss and subsequent surface impacts depend on the background oceanic state, ensemble members are initialized by a combination of varying phases of Atlantic Multidecadal Variability (AMV) and Interdecadal Pacific Variability (IPV). Different AMV and IPV states combined, indeed, can modulate the stratosphere-troposphere responses to sea-ice loss, particularly in the North Atlantic sector. Similar experiments with another climate model show that, although strong sea-ice forcing also leads to tighter stratosphere-troposphere coupling than weak sea-ice forcing, the timing of the response differs from that in WACCM6. Our findings suggest that Arctic sea-ice loss can affect the stratospheric circulation and subsequent tropospheric variability on seasonal timescales, but modulation by the background oceanic state and model dependence need to be taken into account.
A Comparative Study of Reinforcement Learning and Analytical Methods for Optimal Control

Numerous reinforcement learning (RL) algorithms have been introduced to resolve challenging tasks like game playing, natural language processing, and control. Particularly, RL can be used to find a good policy for control systems for which the optimal control sequence is difficult to find by analytical methods. This paper compares RL and analytical methods for optimal control in an inverted pendulum environment. Dynamic programming (DP) and model predictive control (MPC) are considered for the analytical methods. The control results of RL, DP, and MPC are qualitatively and quantitatively compared in terms of total reward, state response, and control sequence to investigate the relationships between them. Because they have similar problem formulations, the relationships can be explained by RL parameters: discounting factor and exploration rate. This comparative study is expected to provide insights to those studying RL algorithms and optimal control theories.
Waveform-based simulated annealing of crosshole transmission data: A semi-global method for estimating seismic anisotropy

Michael V. Afanasiev,1,2 R. Gerhard Pratt,1 Rie Kamei1,3 and Glenn McDowell4 1Department of Earth Sciences, University of Western Ontario, London, ON, Canada N6A5B7 2Institute of Geophysics, ETH Zürich, Zürich 8053, Switzerland. E-mail: michael.afanasiev@erdw.ethz. ch 3School of Earth and Environment, University of Western Australia, Crawley, WA 6009, Australia 4Vale Technology Development (Canada) Ltd., Sudbury, Ontario, Canada
A Third ‘Crosswords Based’ Approach to 2-D Phase Retrieval Problems

In our recent papers, the phase retrieval problem is dealt with by splitting the 2-D problem into a series of 1-D problems solved through the Spectral Factorization method and a subsequent crosswords-like selection scheme, which also allows for successful recovery by using a single measurement surface (plus minimal a-priori information of the source). In this communication we provide a further and hopefully final advance of the corresponding procedure, thus removing the computational complexity associated to our previous crossing schemes.
Vision Transformer Model for Predicting the Severity of Diabetic Retinopathy in Fundus Photography-Based Retina Images

Diabetic Retinopathy (DR) is a result of prolonged diabetes with poor blood sugar management. It causes vision problems and blindness due to the deformation of the human retina. Recently, DR has become a crucial medical problem that affects the health and life of people. Diagnosis of DR can be done manually by ophthalmologists, but this is cumbersome and time consuming especially in the current overloaded physician’s environment. The early detection and prevention of DR, a severe complication of diabetes that can lead to blindness, require an automatic, accurate, and personalized machine learning-based method. Various deep learning algorithms, particularly convolutional neural networks (CNNs), have been investigated for detecting different stages of DR. Recently, transformers have proved their capabilities in natural language processing. Vision transformers (ViTs) are extensions of these models to capture long-range dependencies in images, which achieved better results than CNN models. However, ViT always needs huge datasets to learn properly, and this condition reduced its applicability in DR domain. Recently, a new real-world and large fundus image dataset called fine-grained annotated diabetic retinopathy (FGADR) has been released which supported the application of ViT in DR diagnosis domain. The literature has not explored FGADR to optimize ViT models. In this paper, we propose a novel ViT based deep learning pipeline for detecting the severity stages of DR based on fundus photography-based retina images. The model has been built using FGADR dataset. The model has been optimized using a new optimizer called AdamW to detect the global context of images. Because FGADR is an imbalanced dataset, we combine several techniques for handling this issue including the usage of F1-score as the optimization metric, data augmentation, class weights, label smoothing, and focal loss. Extensive experiments have been conducted to explore the role of ViT with different data balancing techniques to detect DR. In addition, the proposed model has been compared with the state-of-the-art CNN algorithms such as ResNet50, Incep-tionV3, and VGG19. The adopted model was able to capture the crucial features of retinal images to understand DR severity better. It achieved superior results compared to other CNN and baseline ViT models (i.e., 0.825, 0.825, 0.826, 0.964, 0.825, 0.825, and 0.956 for F1-score, accuracy, balanced accuracy, AUC, precision, recall, specificity, respectively). The results of the proposed ViT model were quite encouraging to be applied in real medical environment for assisting physicians to make accurate, personalized, and timely decisions.