Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust cell-free mmWave/sub-THz access using minimal coordination and coarse synchronization

This study investigates simpler alternatives to coherent joint transmission for supporting robust connectivity against signal blockage in mmWave/sub-THz access networks. By taking an information-theoretic viewpoint, we demonstrate analytically that with a careful design, full macrodiversity gains and significant SNR gains can be achieved through canonical receivers and minimal coordination and synchronization requirements at the infrastructure side. Our proposed scheme extends non-coherent joint transmission by employing a special form of diversity to counteract artificially induced deep fades that would otherwise make this technique often compare unfavorably against standard transmitter selection schemes. Additionally, the inclusion of an Alamouti-like space-time coding layer is shown to recover a significant fraction of the optimal performance. Our conclusions are based on an insightful multi-point intermittent block fading channel model that enables rigorous ergodic and outage rate analysis, while also considering timing offsets due to imperfect delay compensation. Although simplified, our approach captures the essential features of modern mmWave/sub-THz communications, thereby providing practical design guidelines for realistic systems.
Clinical significance of a proposed developmental trauma disorder diagnosis: results of an international survey of clinicians.

OBJECTIVE Maltreatment, family violence, and disruption in primary caregiver attachment in childhood may constitute a developmental form of trauma that places children at risk for multiple psychiatric and medical diagnoses that often are refractory to well-established evidence-based mental health treatments. No integrative diagnosis exists to guide assessment and treatment for these children and adolescents. This study therefore assessed clinicians' ratings of the clinical utility of a proposed developmental trauma disorder diagnostic framework. METHOD An Internet survey was conducted with an international convenience sample of 472 self-selected medical, mental health, counseling, child welfare, and education professionals. Respondents made quantitative ratings of the clinical significance of developmental trauma disorder, developmental trauma exposure, and symptom items and also posttraumatic stress disorder (PTSD) and other Axis I internalizing and externalizing disorder symptom items for 4 clinical vignettes. Ratings of the discriminability of each developmental trauma disorder item from PTSD, other anxiety disorders, affective disorders, and externalizing behavior disorders, and of each developmental trauma disorder item's amenability to existing evidence-based treatments for those disorders, also were obtained. RESULTS Respondents viewed developmental trauma disorder criteria as (1) comparable in clinical utility to criteria for PTSD and other psychiatric disorders; (2) discriminable from and not fully accounted for by other disorders; and (3) refractory to existing evidence-based psychotherapeutic treatments. CONCLUSIONS The exposure and symptom criteria proposed for a developmental trauma disorder diagnosis warrant clinical dissemination and scientific field testing to determine their actual clinical utility in treating traumatized children with complex psychiatric presentations.
From Fake to Hyperpartisan News Detection Using Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.
Cohomology and deformation of compatible Hom-Leibniz algebras

In this paper, we consider compatible Hom-Leibniz algebra where the Hom map twists the operations in the compatible system. We consider a suitably graded Lie algebra whose Maurer-Cartan elements characterize the structure of compatible Hom-Leibniz algebras. Using this, we study cohomology, infinitesimal deformations, the Nijenhuis operator, and their relation for compatible Hom-Leibniz algebras. Finally we see the cohomology of compatible Hom-Leibniz algebra with coefficients in an arbitrary representation.
Balanced Classification: A Unified Framework for Long-Tailed Object Detection

Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. In this article, we contend that the learning bias originates from two factors: 1) the unequal competition arising from the imbalanced distribution of foreground categories, and 2) the lack of sample diversity in tail categories. To tackle these issues, we introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution and dynamic intensification of sample diversities in a synchronized manner. Specifically, a novel foreground classification balance loss (FCBL) is developed to ameliorate the domination of head categories and shift attention to difficult-to-differentiate categories by introducing pairwise class-aware margins and auto-adjusted weight terms, respectively. This loss prevents the over-suppression of tail categories in the context of unequal competition. Moreover, we propose a dynamic feature hallucination module (FHM), which enhances the representation of tail categories in the feature space by synthesizing hallucinated samples to introduce additional data variances. In this divide-and-conquer approach, BACL sets a new state-of-the-art on the challenging LVIS benchmark with a decoupled training pipeline, surpassing vanilla Faster R-CNN with ResNet-50-FPN by 5.8% AP and 16.1% AP for overall and tail categories. Extensive experiments demonstrate that BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings

The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive information under privacy attacks. This paper studies the delicate equilibrium between data privacy and task utility goals of VFL under differential privacy (DP). To address the generality issue of prior arts, this paper advocates a flexible and generic approach that decouples the two goals and addresses them successively. Specifically, we initially derive a rigorous privacy guarantee by applying norm clipping on shared feature embeddings, which is applicable across various datasets and models. Subsequently, we demonstrate that task utility can be optimized via adaptive adjustments on the scale and distribution of feature embeddings in an accuracy-appreciative way, without compromising established DP mechanisms. We concretize our observation into the proposed VFL-AFE framework, which exhibits effectiveness against privacy attacks and the capacity to retain favorable task utility, as substantiated by extensive experiments.
Development Of Automated Cardiac Arrhythmia Detection Methods Using Single Channel ECG Signal

Arrhythmia, an abnormal cardiac rhythm, is one of the most common types of cardiac disease. Automatic detection and classification of arrhythmia can be significant in reducing deaths due to cardiac diseases. This work proposes a multi-class arrhythmia detection algorithm using single channel electrocardiogram (ECG) signal. In this work, heart rate variability (HRV) along with morphological features and wavelet coefficient features are utilized for detection of 9 classes of arrhythmia. Statistical, entropy and energy-based features are extracted and applied to machine learning based random forest classifiers. Data used in both works is taken from 4 broad databases (CPSC and CPSC extra, PTB-XL, G12EC and Chapman-Shaoxing and Ningbo Database) made available by Physionet. With HRV and time domain morphological features, an average accuracy of 85.11%, sensitivity of 85.11%, precision of 85.07% and F1 score of 85.00% is obtained whereas with HRV and wavelet coefficient features, the performance obtained is 90.91% accuracy, 90.91% sensitivity, 90.96% precision and 90.87% F1 score. The detailed analysis of simulation results affirms that the presented scheme effectively detects broad categories of arrhythmia from single-channel ECG records. In the last part of the work, the proposed classification schemes are implemented on hardware using Raspberry Pi for real time ECG signal classification.
Using Factorial Survey Experiments to Measure Attitudes, Social Norms, and Fairness Concerns in Developing Countries

Survey-based experimental methods are increasingly used in the social sciences to study, among others, attitudes, norms, and fairness judgments. One of these methods is the factorial survey experiment (FSE or vignette experiment) in which respondents are confronted with various descriptions of situations that differ in a discrete number of attributes (or factors), and they are asked to evaluate those situations according to criteria such as agreement, approval, and fairness. Due to the systematic experimental variation of the presented situations, an FSE can separate effects of single situational attributes, allowing the causal influence of relevant situational attributes to be determined. This is the key advantage over simple survey items. While most studies using FSEs are carried out in developed countries in which respondents are familiar with surveys, we add further evidence that this method can also unfold its power in a developing context. Building on previous applications of FSEs in Africa, we demonstrate the usefulness of this method in four novel studies on social norms regarding the physical punishment of children and the social approval of technology adoption in Benin as well as judgments of just earnings in Rwanda. We also test for the first time the applicability of multiple vignettes per respondents in a Global South/remote area context. The results of these studies are theoretically meaningful and the overwhelming majority of respondents discriminate between vignettes. This supports the validity of FSEs. However, conducting survey experiments in developing countries is different from similar experimental research in developed countries and, therefore, we also discuss some of these differences and corresponding challenges. Last but not least, our article shows, provided a few precautions are heeded, that FSEs could be used as a vehicle to innovate social science research in a Global South/remote area context.
Benchmarking density functional theory on the prediction of antiferromagnetic transition temperatures

This study investigates the predictive capabilities of common DFT methods (GGA, GGA+$U$, and GGA+$U$+$V$) for determining the transition temperature of antiferromagnetic insulators. We utilize a dataset of 29 compounds and derive Heisenberg exchanges based on DFT total energies of different magnetic configurations. To obtain exchange parameters within a supercell, we have devised an innovative method that utilizes null space analysis to identify and address the limitations imposed by the supercell on these exchange parameters. With obtained exchanges, we construct Heisenberg Hamiltonian to compute Transition temperatures using classical Monte Carlo simulations. To refine the calculations, we apply linear response theory to compute on-site ($U$) and intersite ($V$) corrections through a self-consistent process. Our findings reveal that GGA significantly overestimates the transition temperature (by ~113%), while GGA+$U$ underestimates it (by ~53%). To improve GGA+$U$ results, we propose adjusting the DFT results with the $(S+1)/S$ coefficient to compensate for quantum effects in Monte Carlo simulation, resulting in a reduced error of 44%. Additionally, we discover a high Pearson correlation coefficient of approximately 0.92 between the transition temperatures calculated using the GGA+$U$ method and the experimentally determined transition temperatures. Furthermore, we explore the impact of geometry optimization on a subset of samples. Using consistent structures with GGA+U and GGA+U+V theories reduced the error.
Perceptions of the Fourth Industrial Revolution and Artificial Intelligence Impact on Society

The Fourth Industrial Revolution, particularly Artificial Intelligence (AI), has had a profound impact on society, raising concerns about its implications and ethical considerations. The emergence of text generative AI tools like ChatGPT has further intensified concerns regarding ethics, security, privacy, and copyright. This study aims to examine the perceptions of individuals in different information flow categorizations toward AI. The results reveal key themes in participant-supplied definitions of AI and the fourth industrial revolution, emphasizing the replication of human intelligence, machine learning, automation, and the integration of digital technologies. Participants expressed concerns about job replacement, privacy invasion, and inaccurate information provided by AI. However, they also recognized the benefits of AI, such as solving complex problems and increasing convenience. Views on government involvement in shaping the fourth industrial revolution varied, with some advocating for strict regulations and others favoring support and development. The anticipated changes brought by the fourth industrial revolution include automation, potential job impacts, increased social disconnect, and reliance on technology. Understanding these perceptions is crucial for effectively managing the challenges and opportunities associated with AI in the evolving digital landscape.
Perception versus Reality: Canadian Consumer Views of Local and Organic

During the past decade, Canadian consumers have developed a keen interest in local and organic foods. In response, the Canadian government established standards to regulate their labeling. However, many retail and media outlets offer varying definitions that fit their needs. Consumers utilize this often conflicting information to formulate their understanding of local and organic. The aim of this study was to investigate consumer understanding and perception of local and organic food, especially in regard to production characteristics. The results indicate that local is predominantly defined as decreased miles to transport, whereas organic is defined as food produced without the use of synthetic pesticides. However, a fairly large percentage of consumers perceive inaccurate definitions as being characteristics of local and organic. Furthermore, consumers with accurate definitions of local and organic share a similar consumer profile, while consumers with misguided perceptions do not. We also see that characteristics such as ethnic heritage, personal characteristics, geographic region, and length of stay in Canada not only influence consumer understanding and perception, but also the geographic boundaries associated with local.
THE INFLUENCE OF GOOD CORPORATE GOVERNANCE PRINCIPLES, INTERNAL CONTROL SYSTEMS, AND ORGANIZATIONAL COMMITMENT ON UPT PERFORMANCE WITH HUMAN RESOURCES COMPETENCE AS A MODERATING VARIABLE IN IMPLEMENTING UNITS TECHNICAL DIRECTOR GENERAL OF RESOURCES AND

UPT performance is work performance achieved by UPT in realizing the activity or program targets that have been set at the beginning effectively and efficiently. This study aims to determine the effect of good corporate governance, internal control systems, organizational commitment on UPT performance with human resource competence as a moderating variable in Technical Implementation Units of the Directorate General of Resources and Equipment of Post and Information Technology throughout Indonesia. The population in this study were 35 UPT Directorate General of Resources and Equipment of Post and Information Technology throughout Indonesia. A sample of 140 respondents who are activity managers in each UPT. Sources of data used are primary data and data collection techniques through questionnaires. Data were analyzed using Partial Least Square (PLS). The results of the study show that good corporate governance, internal control systems, organizational commitment, and human resource competence have a significant positive effect on UPT performance. Human resource competence is only able to moderate the effect of organizational commitment on UPT performance, but is unable to moderate the effect of good corporate governance and internal control systems on UPT performance.
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation perform well in regions where normals change slowly, they tend to fail where normals vary rapidly. To address this issue, we propose a novel approach called MSECNet, which improves estimation in normal varying regions by treating normal variation modeling as an edge detection problem. MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream. The MSEC stream achieves robust edge detection through multi-scale feature fusion and adaptive edge detection. The detected edges are then combined with the output of the backbone network using the edge conditioning module to produce edge-aware representations. Extensive experiments show that MSECNet outperforms existing methods on both synthetic (PCPNet) and real-world (SceneNN) datasets while running significantly faster. We also conduct various analyses to investigate the contribution of each component in the MSEC stream. Finally, we demonstrate the effectiveness of our approach in surface reconstruction.
Decoupling control parameter method to study the coupling characteristics of subsystems in the feed system

When developing high-speed and high-precision CNC machine tools, subsystem coupling effects must be considered while designing the feed system to maximize its dynamic performance. Currently, the influence of changes in control parameters on the matching characteristics of each subsystem was not yet considered when studying the coupling relationship between subsystems. Therefore, it is difficult to define the law of action between subsystems under the interference of control parameters. Hence, a new method was proposed in this paper aiming to isolate the disturbance of control parameters and to highlight the actions between electromechanical subsystems. This was achieved by optimizing the servo control parameters for each configuration of electromechanical parameters, ensuring that the feed system performance is optimal for the observed configuration. This approach eliminated the influence of the control subsystem on the electromechanical coupling relationship. The approach effectiveness was verified via the integrated model of the feed system. As such, this paper provides a reliable method to further study the coupling mechanism of subsystems, revealing the mechanism behind the dynamic design of feed systems.
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.
Quantifying the spread of communicable diseases with immigration of infectious individuals (preprint)

We construct a set of new epidemiological thresholds to address the general problem of spreading and containment of a disease with influx of infected individuals when the classic $\mathcal R_0$ is no longer meaningful. We provide analytical properties of these indices and illustrate their usefulness in a compartmental model of COVID-19 with data taken from Chile showing a good predictive potential when contrasted with the recorded disease behaviour. This approach and the associated analytical and numerical results allow us to quantify the severity of an immigration of infectious individuals into a community, and identification of the key parameters that are capable of changing or reversing the spread of an infectious disease in specific models.
Macroscopic effects in generation of attosecond XUV pulses via high-order frequency mixing in gases and plasma

We study the generation of attosecond XUV pulses via high-order frequency mixing (HFM) of two intense generating fields, and compare this process with the more common high-order harmonic generation (HHG) process. We calculate the macroscopic XUV signal by numerically integrating the 1D propagation equation coupled with the 3D time-dependent Schr\"odinger equation. We analytically find the length scales which limit the quadratic growth of the HFM macroscopic signal with propagation length. Compared to HHG these length scales are much longer for a group of HFM components, with orders defined by the frequencies of the generating fields. This results in a higher HFM macroscopic signal despite the microscopic response being lower than for HHG. In our numerical simulations, the intensity of the HFM signal is several times higher than that for HHG in a gas, and it is up to three orders of magnitude higher for generation in plasma; it is also higher for longer generating pulses. The HFM provides very narrow XUV lines ($\delta \omega / \omega = 4.6 \times 10^{-4}$) with well-defined frequencies, thus allowing for a simple extension of optical frequency standards to the XUV range. Finally, we show that the group of HFM components effectively generated due to macroscopic effects provides a train of attosecond pulses such that the carrier-envelope phase of an individual attosecond pulse can be easily controlled by tuning the phase of one of the generating fields.
A Thermodynamically Universal Turing Machine

Expanding upon the widely recognized notion of mathematical universality in Turing machines, a concept of thermodynamic universality in Turing machines is introduced. Under the physical Church-Turing thesis, the existence of a thermodynamically universal Turing machine (TUTM) is demonstrated. A TUTM not only has the capability to simulate the input-output behavior of any given Turing machine but also replicate the heat production of that machine up to an additive constant. The finding shows that the hypothesis that the physical world is simulated by Turing machines may not be completely absurd.