Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware

A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with more synaptic connections, enhancing the overall network capabilities. The proposed neuron design is based on superconductor electronics fabric, incorporating multiple superconducting loops, each with two Josephson Junctions. This arrangement enables each input data branch to have positive and negative inductive coupling, supporting excitatory and inhibitory synaptic data. Compatibility with synaptic devices and thresholding operation is achieved using a single flux quantum (SFQ) pulse-based logic style. The neuron design, along with ternary synaptic connections, forms the foundation for a superconductor-based SNN inference. To demonstrate the capabilities of our design, we train the SNN using snnTorch, augmenting the PyTorch framework. After pruning, the demonstrated SNN inference achieves an impressive 96.1% accuracy on MNIST images. Notably, the network exhibits a remarkable throughput of 8.92 GHz while consuming only 1.5 nJ per inference, including the energy consumption associated with cooling to 4K. These results underscore the potential of superconductor electronics in developing high-performance and ultra-energy-efficient neural network accelerator architectures.
J urnal M anajemen (E disi E lektronik )

Researchers examined a company engaged in interior design providing various facilities from artistry to furniture supplies. This study aims to propose a marketing program to evaluate the marketing carried out by companies through social media. This study used Observation, in-depth interviews, secondary company data, documentation, and descriptive qualitative research methodologies collection. This research resulted in an analysis of the proposal of the Ruang Impian program, a program to do marketing through social media. This program proposal is assisted by the theory of the Technology Acceptance Model with the concept of Utilitarian Information to help companies develop social media. It is hoped that the Technology Acceptance Model with the idea of Utilitarian Confirmation can help the company's marketing develop and become a force for DENTUUM.
A Literature Review on High-functioning Autistic Employees

This literature review aims to contribute to the field of autism and employment. It will analyse bibliographic data on peer-reviewed research on the benefits and challenges of a neurodiverse workforce. Similarly, it will examine the experiences of high-functioning autistic people in the workplace. Despite reports in popular media on the benefits of a neurodiverse workplace, including better innovation and company performance – there is very little academic research to support these claims. Of the limited existing literature, there are very few articles in business and management journals. This review gathers empirical evidence to test such claims. The research is based on high-functioning autistic people (verbal, with average or above IQ). Autism will be referred to as Autism Spectrum Disorder (ASD) from here on, which includes Asperger syndrome under the criteria of the DSM-5. Data from peer-reviewed research articles on high-functioning autism, employment and adults in the Social Sciences Citation Index were analysed to gauge the current trends in publication and citation. This database was chosen to limit the medical literature, which is extensive and an entirely different field. The data indicates that research on the experiences autistic employees, and their employers, up to recent years has been an understudied field. Based on the analysis used for this study, research and interest in this field began to emerge in publications in 2005. This review identified high-functioning autism as an increasingly important factor to consider in designing hiring and recruitment processes. It is also a significant consideration in conflict-resolution, communication and the training and support areas of management. Considering the steep increase in autism diagnoses over the past 30 years, coupled with the supports made available to autistic people up to third-level education; a more neurodivergent, educated talent pool is emerging which will change the way managers approach and manage diversity in the workplace.
WIP: An Adaptive High Frequency Removal Attack to Bypass Pulse Fingerprinting in New-Gen LiDARs

—LiDAR stands as a critical sensor in the realm of autonomous vehicles (AVs). Considering its safety and security criticality, recent studies have actively researched its security and warned of various safety implications against LiDAR spooﬁng attacks, which can cause critical safety implications on AVs by injecting ghost objects or removing legitimate objects from their detection. To defend against LiDAR spooﬁng attacks, pulse ﬁngerprinting has been expected as one of the most promising countermeasures against LiDAR spooﬁng attacks, and recent research demonstrates its high defense capability, especially against object removal attacks. In this WIP paper, we report the progress in conducting further security analysis on pulse ﬁngerprinting against LiDAR spooﬁng attacks. We design a novel adaptive attack strategy, the Adaptive High-Frequency Removal (A-HFR) attack, which can be effective against broader types of LiDARs than the existing HFR attacks. We evaluate the A-HFR attack on three commercial LiDAR with pulse ﬁngerprinting and ﬁnd that the A-HFR attack can successfully remove over 96% of the point cloud within a 20 ◦ horizontal and a 16 ◦ vertical angle. Our ﬁnding indicates that current pulse ﬁngerprinting techniques might not be sufﬁciently robust to thwart spooﬁng attacks. We also discuss potential strategies to enhance the defensive efﬁcacy of pulse ﬁngerprinting against such attacks. This ﬁnding implies that the current pulse ﬁngerprinting may not be an ultimate countermeasure against LiDAR spooﬁng attacks. We ﬁnally discuss our future plans.
Endogenous Persistence with Recursive Inattentiveness

The DSGE model with endogenous and time-varying sticky information in Drager (2010) is extended by allowing agents’ recursive choice between forecasts under rational or sticky information to affect the model solution. Dynamic equilibrium paths generate highly persistent series for output, inflation and the nominal interest rate. Agents choose predictors in a near-rational manner and we find that the share of agents with rational expectations reacts to the overall variability of aggregate variables. The model can generate hump-shaped responses of inflation and output to a monetary policy shock if the degree of inattentiveness is sufficiently high. Finally, feedback from agents’ degree of inattentiveness to the model solution affects the determinacy region of the model. The Taylor principle is then only a necessary condition for determinacy, and monetary policy should target the output gap as well in order to ensure a unique and stable solution.
Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows

We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processing. In such systems, coscheduling of GPU memory management and task placement represents a promising opportunity. We propose Compass, a novel framework that unifies these functions to reduce job latency while using resources efficiently, placing tasks where data dependencies will be satisfied, collocating tasks from the same job (when this will not overload the host or its GPU), and efficiently managing GPU memory. Comparison with other state of the art schedulers shows a significant reduction in completion times while requiring the same amount or even fewer resources. In one case, just half the servers were needed for processing the same workload.
Mind Captain: A Positive Technology App for Youth Well-being

The COVID-19 emergency has notoriously adversely affected youth's mental health, leading to a dramatic increase not only in clinically relevant disorders but also in generalized sub-syndromic affective maladjustment and emotional dysregulation. As youth mental health apps hold unique potential for support of this vulnerable population, we have been developing a novel digital app for youth well-being called “Mind Captain” , aimed to increase emotional literacy as a prerequisite for emotion management, to offer strategies to deal with difficult emotions and train positive strengths. Innovative features are: 1) the involvement of youth workers who will receive a dedicated training aimed at including the app in their youthwork; 2) a psychological framework composed of well-established constructs from Socio-Emotional Learning, the third-wave Cognitive-Behavioural approach of Acceptance and Commitment Training, Positive Psychology and Compassion Focused approach 3) a metaphorical storytelling, based on the user being the captain of a ship and voyaging on a flexible route of personal growth, starting from emotional self-awareness through emotional self-regulation to positive empowerment; each voyage destination has a precise psychological function. A support website will offer extra psychoeducational and research materials. The human-centered design approach included bench-marking analysis and systematic review of m-health youth apps, youth need analysis from literature and through dedicated national and international focus groups, the continuous project team interaction for a gradual refinement to achieve a minimum viable product to be tested for usability, engagement
Mineral Extraction from Geothermal Reservoirs: A Case Study from Western Anatolia

Geothermal brine is a promising source for sustainable and environmentally friendly mineral extraction, containing a variety of valuable minerals, such as silica, lithium, strontium, and rare earth elements. However, the low mineral concentrations in the brine p resent a significant challenge, requiring large volumes of brine to be processed. This study investigates the potential of mineral extraction from geothermal reservoirs, using a case study of reserve estimation in a geothermal field to demonstrate the application of Monte Carlo simulation, a stochastic approach used to handle uncertain parameters with critical effects on reserve estimation. A review and evaluation of mineral extraction methods is then presented, highlighting their challenges, advantages, and techno-economic perspectives. The study found that mineral extraction from geothermal reservoirs is economically viable, although more research and development is needed to overcome the challenges associated with this process.
Transformers for Scattering Amplitudes

We pursue the use of Transformers to extend state-of-the-art results in theoretical particle physics. Specifically, we use Transformers to predict the integer coefficients of large mathematical expressions that represent scattering amplitudes in planar N = 4 Yang-Mills theory, which is a quantum field theory closely related to the theory that describes Higgs boson production at the Large Hadron Collider. We first formulate the physics problem in a language-based representation that is amenable to Transformer architectures and standard training objectives. Then we show that the model can achieve high accuracy ( > 98%) on two tasks.
Organizational Commitment of Secondary Schools Teachers in the Federal Territory of Kuala Lumpur

The quality of teaching and teachers’ organizational commitment determines the success in educational institutions. This study aims to examine the factors influencing organizational commitment of teachers among secondary schools in Federal Territory of Kuala Lumpur. This is a non-experimental quantitative research using survey technique through the administration of a set of questionnaire. A total of 357 surveys were randomly selected as samples. The data was analyzed by following descriptive and inferential statistics with SPSS. The results exhibited that teachers’ organizational commitment was perceived as a moderate level, and there was a positive interrelationship among the dimensions of organizational commitment. In addition, the age of the teacher was the only statistically significant predictor of teacher organizational commitment. As implication from this study, the researcher recommends the policy makers for taking initiatives to enhance the level of commitment of teachers.
Understanding Route Origin Validation (ROV) Deployment in the Real World and Why MANRS Action 1 Is Not Followed

—BGP hijacking is one of the most important threats to routing security. To improve the reliability and availability of inter-domain routing, a lot of work has been done to defend against BGP hijacking, and Route Origin Validation (ROV) has become the best current practice. However, although the Mutually Agreed Norms for Routing Security (MANRS) has been encouraging network operators to at least validate announcements of their customers, recent research indicates that a large number of networks still do not fully deploy ROV or propagate illegitimate announcements of their customers. To understand ROV deployment in the real world and why network operators are not following the action proposed by MANRS, we make a long-term measurement for ROV deployment and further find that many non-compliant networks may deploy ROV only at part of customer interfaces, or at provider or peer interfaces. Then, we present the first notification experiment to investigate the impact of notifications on ROV remediation. However, our analysis indicates that none of the notification treatments has a significant effect. After that, we conduct a survey among network operators and find that economical and technical problems are the two major classes of reasons for non-compliance. Seeking a realistic ROV deployment strategy, we perform large-scale simulations, and, to our surprise, find that not following MANRS Action 1 can lead to better defence of prefix hijacking. Finally, with all our findings, we provide practical recommendations and outline future directions to help promote ROV deployment.
Determinants of Physical Performance and Physiological Responses During an Official Cross-Country Marathon of Mountain-Biking (Xcm-Mtb)

Introduction : The variables determinants of physical performance in cross-country marathon of mountain biking (XCM-MTB) are still unknown. Objective : We aimed to verify the training control variables and the physiological responses in an official XCM-MTB race. Material and methods : 13 athletes (11 men and 2 women; 33.3 ± 12.7 years of age) participated in this study. It was conducted during an official XCM-MTB in Brazil (route of 70 km). The heart rate (HR), altimetry, velocity, temperature, pacing, and power were obtained every 10 km travelled by the STRAVA application. Multiple linear regression analysis was performed to verify whether the variables could predict physical performance. Results : The athletes maintained constant HR elevation in the corresponding zone 80% HR max . They also presented a variation in the pacing (F = 35.82; p < 0.001; d = 0.66) and power (F = 7.20; p < 0.001; d = 0.18) showing higher values in the last 10 km. Only pacing can be considered a predictor of the physical performance (β = 0.958; t = 7.30; p < 0.001), specifically the one at 20 km (F = 10.23; p = 0.004; R2 = 0.82). Conclusion : The study concluded that the analyzed variables are reliable for the performance control in an official XCM-MTB race. HR and power are variables that can be used to prescribe and control training, as they change according to the requirements of the race. Power can also be used as a performance predictor as it is directly influenced by terrain.
SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional neural networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN, named the Shallow to Deep Feature Fusion Network (SDF2Net), is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the airborne synthetic aperture radar (AIRSAR) datasets of Flevoland and San Francisco, as well as the ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in overallaccuracy, with a 1.3% and 0.8% enhancement for the AIRSAR datasets and a 0.5% improvement for the ESAR dataset. Analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising overall accuracy of 96.01% even with only a 1% sampling ratio.
Estimation of complex carryover effects in crossover designs with repeated measures

It has been argued that the models used to analyze data from crossover designs are not appropriate when simple carryover effects are assumed. In this paper, the estimability conditions of the carryover effects are found, and a theoretical result that supports them, additionally, two simulation examples are developed in a non-linear dose-response for a repeated measures crossover trial in two designs: the traditional AB/BA design and a Williams square. Both show that a semiparametric model can detect complex carryover effects and that this estimation improves the precision of treatment effect estimators. We concluded that when there are at least five replicates in each observation period per individual, semiparametric statistical models provide a good estimator of the treatment effect and reduce bias with respect to models that assume either, the absence of carryover or simplex carryover effects. In addition, an application of the methodology is shown and the richness in analysis that is gained by being able to estimate complex carryover effects is evident.
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching

Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have several limitations and often fail to provide meaningful local insights on the paired geometries. In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we introduce a new operator, called Coupled Laplacian, that allows to easily generate aligned eigenspaces for multiple rigidly-registered geometries. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes. We initially evaluate the performance of the proposed technique in a point-wise manner, specifically focusing on the task of object anomaly localization using the MVTec 3D-AD dataset. Additionally, we define a new medical task, called automatic Bone Side Estimation (BSE), which we address through a global similarity score derived from coupled eigenspaces. In order to test it, we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique, based on Coupled Laplacian, outperforms other methods by reaching an impressive accuracy on both tasks. The code to reproduce our experiments is publicly available at https://github.com/matteo-bastico/CoupledLaplacian and in the Supplementary Code.
The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks

The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investigate the role that current LLMs can play in improving callgraph analysis and type inference for Python programs. Using the PyCG, HeaderGen, and TypeEvalPy micro-benchmarks, we evaluate 26 LLMs, including OpenAI's GPT series and open-source models such as LLaMA. Our study reveals that LLMs show promising results in type inference, demonstrating higher accuracy than traditional methods, yet they exhibit limitations in callgraph analysis. This contrast emphasizes the need for specialized fine-tuning of LLMs to better suit specific static analysis tasks. Our findings provide a foundation for further research towards integrating LLMs for static analysis tasks.
Improve Robustness of Eye Disease Detection by including Learnable Probabilistic Discrete Latent Variables into Machine Learning Models

Ocular diseases, ranging from diabetic retinopathy to glaucoma, present a significant public health challenge due to their prevalence and potential for causing vision impairment. Early and accurate diagnosis is crucial for effective treatment and management.In recent years, deep learning models have emerged as powerful tools for analysing medical images, including ocular imaging . However, challenges persist in model interpretability and uncertainty estimation, which are critical for clinical decision-making. This study introduces a novel application of GFlowOut, leveraging the probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks, for the classification and analysis of ocular diseases using eye fundus images. We develop a robust and generalizable method that utilizes GFlowOut integrated with ResNet18 and ViT models as backbone in identifying various ocular conditions. This study employs a unique set of dropout masks - none, random, bottomup, and topdown - to enhance model performance in analyzing ocular images. Our results demonstrate that the bottomup GFlowOut mask significantly improves accuracy, outperforming the traditional dropout approach.
LEO Satellite Orbit Prediction with Physics Informed Machine Learning

In recent space missions, the more complicated the missions become, the more important autonomous spacecraft controllers are. In this study, we focus on a precise autonomous orbit prediction of LEO (Low-Earth Orbit) satellite. The Physics Informed Machine learning (PIML) enables us to predict the satellite orbit with compatible accuracy to the numerical simulation but more efficiently. The proposed physics-informed machine learning algorithm is based on modeling the orbital trajectory as a Partial Differential Equation (PDE) and using deep Neural Operator (NO) to model the system’s dynamic. We analyze the prediction accuracy of the proposed model and compare it with a pure data-driven model. We also provide the evaluation of the computation and memory requirements of the proposed approach and compare it with a numerical orbit simulator.