Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems

Several studies have attempted to solve traveling salesman problems (TSPs) using various deep learning techniques. Among them, Transformer-based models show state-of-the-art performance even for large-scale Traveling Salesman Problems (TSPs). However, they are based on fully-connected attention models and suffer from large computational complexity and GPU memory usage. Our work is the first CNN-Transformer model based on a CNN embedding layer and partial self-attention for TSP. Our CNN-Transformer model is able to better learn spatial features from input data using a CNN embedding layer compared with the standard Transformer-based models. It also removes considerable redundancy in fully-connected attention models using the proposed partial self-attention. Experimental results show that the proposed CNN embedding layer and partial self-attention are very effective in improving performance and computational complexity. The proposed model exhibits the best performance in real-world datasets and outperforms other existing state-of-the-art (SOTA) Transformer-based models in various aspects. Our code is publicly available at https://github.com/cm8908/CNN_Transformer3.
Dynamic Sparse Training with Structured Sparsity

Dynamic Sparse Training (DST) methods achieve state-of-the-art results in sparse neural network training, matching the generalization of dense models while enabling sparse training and inference. Although the resulting models are highly sparse and theoretically less computationally expensive, achieving speedups with unstructured sparsity on real-world hardware is challenging. In this work, we propose a sparse-to-sparse DST method, Structured RigL (SRigL), to learn a variant of fine-grained structured N:M sparsity by imposing a constant fan-in constraint. Using our empirical analysis of existing DST methods at high sparsity, we additionally employ a neuron ablation method which enables SRigL to achieve state-of-the-art sparse-to-sparse structured DST performance on a variety of Neural Network (NN) architectures. Using a 90% sparse linear layer, we demonstrate a real-world acceleration of 3.4x/2.5x on CPU for online inference and 1.7x/13.0x on GPU for inference with a batch size of 256 when compared to equivalent dense/unstructured (CSR) sparse layers, respectively.
Macroeconomic factors and Stock exchange return: A Statistical Analysis

The purpose of this research is to examine the relationship between the Dhaka Stock exchange index return and macroeconomic variables such as exchange rate, inflation, money supply etc. The long-term relationship between macroeconomic variables and stock market returns has been analyzed by using the Johnson Cointegration test, Augmented Dicky Fuller (ADF) and Phillip Perron (PP) tests. The results revealed the existence of cointegrating relationship between stock prices and the macroeconomic variables in the Dhaka stock exchange. The consumer price index, money supply, and exchange rates proved to be strongly associated with stock returns, while market capitalization was found to be negatively associated with stock returns. The findings suggest that in the long run, the Dhaka stock exchange is reactive to macroeconomic indicators.
Contact Edit: Artist Tools for Intuitive Modeling of Hand-Object Interactions

Posing high-contact interactions is challenging and time-consuming, with hand-object interactions being especially difficult due to the large number of degrees of freedom (DOF) of the hand and the fact that humans are experts at judging hand poses. This paper addresses this challenge by elevating contact areas to first-class primitives. We provide end-to-end art-directable (EAD) tools to model interactions based on contact areas, directly manipulate contact areas, and compute corresponding poses automatically. To make these operations intuitive and fast, we present a novel axis-based contact model that supports real-time approximately isometry-preserving operations on triangulated surfaces, permits movement between surfaces, and is both robust and scalable to large areas. We show that use of our contact model facilitates high quality posing even for unconstrained, high-DOF custom rigs intended for traditional keyframe-based animation pipelines. We additionally evaluate our approach with comparisons to prior art, ablation studies, user studies, qualitative assessments, and extensions to full-body interaction.
An Adaptive Algorithm for Learning with Unknown Distribution Drift

We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. Instead, the algorithm adapts to the sample data. Without explicitly estimating the drift, the algorithm learns a family of functions with almost the same error as a learning algorithm that knows the magnitude of the drift in advance. Furthermore, since our algorithm adapts to the data, it can guarantee a better learning error than an algorithm that relies on loose bounds on the drift. We demonstrate the application of our technique in two fundamental learning scenarios: binary classification and linear regression.
Representation Learning via Manifold Flattening and Reconstruction

This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our such-generated neural networks, called Flattening Networks (FlatNet), are theoretically interpretable, computationally feasible at scale, and generalize well to test data, a balance not typically found in manifold-based learning methods. We present empirical results and comparisons to other models on synthetic high-dimensional manifold data and 2D image data. Our code is publicly available.
NorQuAD: Norwegian Question Answering Dataset

In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.
Modified RSA Public Key Cryptosystem Using Short Range Natural Number Algorithm

In asymmetric key cryptography, also called Public Key cryptography, two different keys (which forms a key pair) are used. One key is used for encryption & only the other corresponding key must be used for decryption. No other key can decrypt the message – not even the original (i.e. the first) key used for encryption. The beauty of this scheme is that every communicating party needs just a key pair for communicating with any number of other communicating parties. Once some one obtains a key pair, he /she can communicate with any one else. The Short Range Natural Number (SRNN) algorithm is similar to RSA algorithm with some modification. This modification increases the security of the cryptosystem. In this algorithm we have an extremely large number that has two prime factors ( similar to RSA ). In addition of this we have used two natural numbers in pair of keys (public , private). These natural numbers increase the security of the cryptosystem. So its name is “Modified RSA Public Key Cryptosystem using Short Range Natural Number Algorithm”. KeywordsCryptography, RSA, Key, Symmetric Key and Asymmetric Key Cryptography. ___________________________________________________________________________________________________
Mining and visualizing large-scale course reviews of LMOOCs learners through structural topic model

Understanding Language Massive Online Open Courses (LMOOCs) learners’ subjective evaluation is essential for language teachers to improve their instructional design, examine the teaching and learning effects, and promote course quality. The present research uses word frequency and co-occurrence analysis, comparative keyword analysis, and structural topic modeling to analyze 69,232 reviews from one Massive Online Open Courses (MOOCs) platform in China. Learners hold a strongly positive overall perception of LMOOCs. Four negative topics appear more commonly in negative reviews as compared to positive ones. Additionally, variations in negative reviews across course types are examined, indicating that learners’ main concerns about high-level LMOOCs include teaching/learning problems, learner expectation, and learner attitude, whereas learners of low-level courses are more critical in the topic of scholarship ability. Our study contributes to the LMOOCs study by providing a better understanding of learners’ perceptions using rigorous statistical techniques.
Motor decoding from the posterior parietal cortex using deep neural networks

Objective. Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs. Approach. Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded nine reaching endpoints in 3D space or five grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding. Main results. DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness. Significance. Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.
Predicting change in neural activity during social exclusion in late childhood: The role of past peer experiences

ABSTRACT A painful experience affecting many children is social exclusion. The current study is a follow-up study, investigating change in neural activity during social exclusion as a function of peer preference. Peer preference was defined as the degree to which children are preferred by their peers and measured using peer nominations in class during four consecutive years for 34 boys. Neural activity was assessed twice with a one-year interval, using functional MRI during Cyberball (MageT1 = 10.3 years, MageT2 = 11.4 years). Results showed that change in neural activity during social exclusion differed as a function of peer preference for the a-priori defined region-of-interest of the subgenual anterior cingulate cortex (subACC), such that relatively lower history of peer preference was associated with an increase in activity from Time1 to Time2. Exploratory whole brain results showed a positive association between peer preference and neural activity at Time2 in the left and right orbitofrontal gyrus (OFG). These results may suggest that boys with lower peer preference become increasingly sensitive to social exclusion over time, associated with increased activity in the subACC. Moreover, lower peer preference and associated lower activity within the OFG may suggest decreased emotion regulation as a response to social exclusion.
Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: New anatomical evidences in the field of personal identification through a retrospective observational study

Orbital and nasal parameters among modern humans show considerable variation, which affects facial shape, and these characteristics vary according to race, region, and period in evolution. The aim of the study was to ascertain whether there are sex differences in the orbital and/or nasal indexes and/or the single measurements used to calculate these in a Kosovar population. The following parameters were taken into consideration: orbital height (OH), orbital width (OW), nasal height (NH), and nasal width (NW). The ratios between orbital index/nasal index (RONI) were calculated. All measurements were obtained from a population sample comprising 408 individuals. The accuracy in sex prediction was 52.86% (CI95% = 45.05%–60.67%) for NW and 64.96% for NH (CI95% = 57.50%– 72.42%). The difference between male and female indexes was statistically significant (P < 0.05). The anthropometric study revealed that only NW and NH are configured as predictors of sexual dimorphism. It could be useful to increase the number of samples to test the discriminant function in other population groups.
Optimal pinging frequencies in the search for an immobile beacon

ABSTRACT We consider a search for an immobile object that can only be detected if the searcher is within a given range of the object during one of a finite number of instantaneous detection opportunities; i.e., “pings. ” More specifically, motivated by naval searches for battery-powered flight data recorders of missing aircraft, we consider the trade-off between the frequency of pings for an underwater locator beacon and the duration of the search. First, assuming that the search speed is known, we formulate a mathematical model to determine the pinging period that maximizes the probability that the searcher detects the beacon before it stops pinging. Next, we consider generalizations to discrete search speed distributions under a uniform beacon location distribution. Lastly, we present a case study based on the search for Malaysia Airlines Flight 370 that suggests that the industry-standard beacon pinging period—roughly 1 second between pings—is too short.
Voluntary suspension of medically-assisted reproduction during the COVID-19 pandemic in Japan: a nationwide internet survey

Abstract We explored whether coronavirus disease 2019 (COVID-19)-related distress was associated with voluntary suspension of medically-assisted reproduction (MAR) treatment from April to May 2020 in Japan. Data for 1,096 candidate respondents were collected from a Japanese nationwide cross-sectional internet survey distributed from August 25 to September 30, 2020. Multiple logistic regression was performed to clarify the association between voluntary suspension of MAR treatment and the Fear of COVID-19 Scale (FVC-19S) score. High FCV-19S score was inversely associated with voluntary suspension of MAR treatment compared to low FCV-19S score among women (OR = 0.28, 95% CI = 0.10–0.84). Age-stratified analyses revealed that low FVC-19S score was significantly associated with voluntary suspension of MAR treatment among women aged < 35 years (OR = 3.86, 95% CI = 1.35–11.0). In contrast, the association between FVC-19S score and voluntary suspension of MAR treatment was reversed and not significant among women aged ≥ 35 years (OR = 0.67, 95% CI = 0.24–1.84). COVID-19-related distress was significantly associated with voluntary suspension of MAR treatment among women aged < 35 years, and this association was reversed but not significant among women aged ≥ 35 years.
Prognostic significance of early acute kidney injury in COVID-19 patients requiring mechanical ventilation: a single-center retrospective analysis

Abstract Acute kidney injury (AKI) is associated with impaired outcomes in critically ill COVID-19 patients. However, the prognostic significance of early AKI is poorly described. We aimed to determine whether AKI on admission to the intensive care unit (ICU) and its development within the first 48 h predict the need for renal replacement therapy (RRT) and increased mortality. An analysis of 372 patients with COVID-19 pneumonia requiring mechanical ventilation without advanced chronic kidney disease from 2020 to 2021 was performed. The AKI stages on ICU admission and Day 2 were determined using adapted KDIGO criteria. The early development of renal function was assessed by the change in AKI score and the Day-2/Day-0 creatinine ratio. Data were compared between three consecutive COVID-19 waves and with data before the pandemic. Both ICU and 90-day mortality (79% and 93% vs. 35% and 44%) and the need for RRT increased markedly with advanced AKI stage on ICU admission. Similarly, an early increase in AKI stage and creatinine implied highly increased mortality. RRT was associated with very high ICU and 90-day mortality (72% and 85%), even surpassing that of patients on ECMO. No difference was found between consecutive COVID-19 waves, except for a lower mortality in the patients on RRT in the last omicron wave. Mortality and need for RRT were comparable in the COVID-19 and pre-COVID-19 patients, except that RRT did not increase ICU mortality in the pre-COVID-19 era. In conclusion, we confirmed the prognostic significance of both AKI on ICU admission and its early development in patients with severe COVID-19 pneumonia.
Relationship Between Hypertriglyceridemic Waist Phenotype and Disposition Index in Chinese Men With Normal Glucose Tolerance

The relationship between hypertriglyceridemic waist (HTGW) phenotype, insulin sensitivity, and pancreatic β-cell function remains still unclear in subjects with normal glucose tolerance (NGT). The objective is to detect whether the disposition index (DI) could be used as a predictive indicator of insulin sensitivity and pancreatic β-cell function in men with HTGW phenotype and NGT. A total of 180 men without diabetes were recruited in this study and underwent an oral glucose tolerance test (OGTT) to calculate DI based on the OGTT. Subjects were put into Group A (normal waist circumference [WC] and triglyceride [TG] concentrations), B (enlarged WC or elevated TG concentrations), and C (HTGW phenotype, both enlarged WC and elevated TG concentrations) (n = 60 for each group) according to WC and TG concentrations. The OGTT plasma glucose concentrations at 0.5 and 1 hr for patients in Groups B and C were higher than those in Group A (both p < .05). Group C patients had significantly lower 1/[fasting insulin] values and DI than those in Group A (p < .05), and the 1/[fasting insulin] values in Group C were significantly lower than those in Group B (p < .05). DI correlated positively with high-density lipoprotein cholesterol (p < .05), which was independently associated with WC (p = .002) and TG (p = .009). The HTGW phenotype is associated with decreased DI among men with NGT, indicating decreased DI is a strong predictor of future impaired glucose tolerance, which can provide guidance and reference for screening patients with potential impaired glucose tolerance in Chinese community population.
Single-Position Prone Lateral Lumbar Interbody Fusion Increases Operative Efficiency and Maintains Safety in Revision Lumbar Spinal Fusion

Study Design. Multi-centre retrospective cohort study. Objective. To evaluate the feasibility and safety of the single-position prone lateral lumbar interbody fusion (LLIF) technique for revision lumbar fusion surgery. Background Context. Prone LLIF (P-LLIF) is a novel technique allowing for placement of a lateral interbody in the prone position and allowing posterior decompression and revision of posterior instrumentation without patient repositioning. This study examines perioperative outcomes and complications of single position P-LLIF against traditional Lateral LLIF (L-LLIF) technique with patient repositioning. Method. A multi-centre retrospective cohort study involving patients undergoing 1 to 4 level LLIF surgery was performed at 4 institutions in the US and Australia. Patients were included if their surgery was performed via either: P-LLIF with revision posterior fusion; or L-LLIF with repositioning to prone. Demographics, perioperative outcomes, complications, and radiological outcomes were compared using independent samples t-tests and chi-squared analyses as appropriate with significance set at P<0.05. Results. 101 patients undergoing revision LLIF surgery were included, of which 43 had P-LLIF and 58 had L-LLIF. Age, BMI and CCI were similar between groups. The number of posterior levels fused (2.21 P-LLIF vs. 2.66 L-LLIF, P=0.469) and number of LLIF levels (1.35 vs. 1.39, P=0.668) was similar between groups.Operative time was significantly less in the P-LLIF group (151 vs. 206 min, P=0.004). EBL was similar between groups (150mL P-LLIF vs. 182mL L-LLIF, P=0.31) and there was a trend toward reduced length of stay in the P-LLIF group (2.7 vs. 3.3d, P=0.09). No significant difference was demonstrated in complications between groups. Radiographic analysis demonstrated no significant differences in preoperative or postoperative sagittal alignment measurements. Conclusion. P-LLIF significantly improves operative efficiency when compared to L-LLIF for revision lumbar fusion. No increase in complications was demonstrated by P-LLIF or trade-offs in sagittal alignment restoration. Level of Evidence. Level 4