Miracle-dz/UniFAR-FTUs huggingface dataset:
CHARACTERIZATION AND MELT SPINNING OF POLY (LACTIC ACID)/POLY (ETHYLENE GLYCOL) BLENDS

In this study, melt blended compositions of pure PLA with additions of polyethylene glycol (PEG) up to 30 wt% were prepared. Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), and thermogravimetric analysis (TGA) were used to investigate the properties of PLA/PEG blends, such as structural, thermal, and morphological properties. The results showed that further increments of PEG cause the -OH group of PLA/PEG blends to show a broad peak, indicating that there is hydrogen bonding interaction between PEG and PLA chains. DSC result revealed that the addition of PEG decreases the glass transition temperature from 57 °C to 46 °C and crystallization temperature from 107 °C to 87 °C. Such trends suggest enhanced chain mobility of PLA chains. TGA thermograms showed that further additions of PEG into PLA resulted in a consistent shift to lower temperature and decrease in thermal stability. Optical microscopy (OM) and scanning electron microscopy (SEM) observations of the melt spun PLA/PEG microfibers revealed that the diameter of the microfibers averaged between 15 to 80 microns. ABSTRAK: Kajian ini menganalisa komposisi adunan lebur PLA asli bersama tambahan polietilena glikol (PEG) sebanyak 30%. Penjelmaan Fourier spektroskopi inframerah (FTIR), kalorimeter pengimbasan pembezaan (DSC) dan analisis termogravimetri (TGA) telah digunakan bagi mengkaji sifat-sifat adunan PLA/PEG, seperti struktur, terma dan sifat-sifat morfologi. Keputusan menunjukkan penambahan PEG seterusnya menyebabkan kumpulan -OH campuran PLA/PEG memberikan puncak yang lebar, ini menunjukkan ada interaksi ikatan hidrogen antara rantaian PEG dan PLA. Keputusan DSC menunjukkan penambahan PEG mengurangkan perubahan gelas dari 57 °C kepada 46 °C dan suhu kristalisasi dari 107 °C kepada 87 °C. Trend ini mencadangkan peningkatan pergerakan rangkaian pada rantaian PLA. Termogram TGA menunjukkan dengan penambahan berterusan PEG ke dalam PLA menghasilkan penurunan konsisten pada suhu dan pengurangan kestabilan haba. Pemerhatian mikroskop optik (OM) dan mikroskopi elektron penskanan (SEM) mikrofiber spun lebur PLA/PEG menunjukkan purata diameter mikrofiber ini antara 15 ke 80 mikron.
Yidishe tates forming Jewish families Experiences of intermarried Finnish Jewish men

Jewish communities often do not endorse the idea of intermarriage, and Orthodox Judaism opposes the idea of marrying out. Intermarriage is often perceived as a threat that may jeopardise Jewish continuity as children of such a relationship may not identify as Jews. When a Jewish woman marries out, her children will in any case become Jewish by halakhah – the Jewish law – by which Judaism is inherited from mother to child – and thus usually faces less difficulties over acceptance in Jewish communities. Even though the Torah speaks of patrilineal descent, in post­ biblical times, the policy was reversed in favour of the matrilineal principle, and children of Jewish men and non­Jewish women must therefore go through the conversion process if they wish to join a Jewish congregation according to most Jewish denominational requirements. The aim of this article is to analyse what happens when Jewish men, who belong to Finland’s Orthodox communities, marry out. Do they ensure Jewish continuity, and raise their children Jewish, and how do they act as Yidishe tates – Jewish fathers? If yes, how do they do so, and what problems do they face? These questions are answered through an analysis of thirteen semi­structured in­depth interviews conducted with male members of the Jewish Community of Helsinki and Turku in 2019–20. Yidishe tates forming Jewish families Experiences of intermarried Finnish Jewish men DOI: https://doi.org/10.30752/nj.97558
Attitudes and habits of young adults in using the services of fast food restaurants

200 Abstract: The purpose of the paper was to investigate the attitudes and perceptions of young people about the fast food restaurants in Serbia, with the special emphasis to the elements of corporate social responsibility (brand, nutritional values, ethical values and the quality of food), and to show the extent to which these products are represented in their daily diet. Data were collected from February to June 2019 between students of the University of Belgrade and Novi Sad, and between young people at high schools in Belgrade and Novi Sad (Serbia). A total sample consisted of 1145 young consumers. It was evident that the values and preferences of the target group of the leading fast food restaurants have changed, in the already developed market as well as in the developing ones, such as Serbia. The paper presents empirical results of using the services of fast food restaurants in Serbia by the younger population, as well as their perception of corporate social responsibility, with the special emphasis to the restaurants of McDonald‘s and KFC.
Randomized Algorithms 2020-1 Lecture 10

Hash tables is very well studied subject in computer science and one of the more useful practices. Knuth’s “The Art of Computer Programming” Volume 3 devotes a lot of space to the various possibilities and the origin of the idea is attributed to a 1956 paper by Arnold Dumey [5]. A major issue is how to resolve collisions and popular suggestions are chaining and Open addressing (or closed hashing). For the latter we need to specify a probing scheme and one of the more popular ones is linear probing which takes advantage of the locality properties of computer memory (in the various levels of hierarchy).
COMPARATIVE STUDY BETWEEN ANISE SEEDS AND MINT LEAVES (CHEMICAL COMPOSITION, PHENOLIC COMPOUNDS AND FLAVONOIDS)

The objective of research was to study the chemical composition , phenolic compounds and flavonoids of powder from anise seeds and mint leaves. Anise seeds contains: total carbohydrates 51.4 %, crude protein 18.1 %, total lipids 13.7 % and total ash 14.9 % in dry sample. While mint leaves contains contains total carbohydrates 55.7 %, crude protein 19.8 %, total lipids 4.9 %, and total ash 18.6 % in dry sample. Total phenolic in anise seeds were 216.6-237.8 mg/100g in aqueous and ethanolic extracts, respectively and total flavonoids were 148.1-156.4 mg/100g in aqueous and ethanolic extracts, respectively comparing with 234.3-278.9 mg/100g in aqueous and ethanolic extracts, respectively and 157.5-186.3 mg/100g in aqueous and ethanolic extracts, respectively of total phenolic compounds and flavonoids in mint leaves. HPLC results showed that anise seeds were found to contain 19 of phenolic compounds, amoung them catechein, cinnamic, ellagic, gallic, chlorogenic, ferulic and catechol were the major active in anise seeds, while mint leaves contained rosmaric, cinnamic, gallic, ferulic, and savianolic as the major of phenolic compounds. In conclusion the obtained results found that, anise seeds and mint leaves extracts were rich in secondary metabolites (phenolic compounds and flavonoids) with high nutritional and health values.
Possible Association of Polymorphisms in Ubiquitin Specific Peptidase 46 Gene With Post-traumatic Stress Disorder

Introduction: Dynamic proteolysis, through the ubiquitin-proteasome system, has an important role in DNA transcription and cell cycle, and is considered to modulate cell stress response and synaptic plasticity. We investigated whether genetic variants in the ubiquitin carboxyl-terminal hydrolase 46 (USP46) would be associated with post-traumatic stress disorder (PTSD) in people with exposure to combat trauma using a case-control candidate gene association design. Methods: Korean male veterans exposed to the Vietnam War were grouped into those with (n = 128) and without (n = 128) PTSD. Seven tagging SNPs of USP46 were selected, and single-marker and haplotype-based association analyses were performed. All analyses were adjusted for sociodemographic factors and levels of combat exposure severity and alcohol problem. Results: One single-marker (rs2244291) showed nominal evidence of association with PTSD status and with the “re-experiencing” cluster, although the association was not significant after Bonferroni correction. No significant association with the other SNPs or the haplotypes was detected. Conclusion: The present finding suggests preliminarily that genetic vulnerability regarding the ubiquitin-proteasome system may be related to fear memory processes and the development of PTSD symptoms after trauma exposure. Further studies with a larger sample size will be needed to examine the role of the ubiquitin-proteasome system including USP46 in PTSD.
Multi-Sample Online Learning for Spiking Neural Networks Based on Generalized Expectation Maximization

Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activations. Probabilistic SNN models are typically trained to maximize the likelihood of the desired outputs by using unbiased estimates of the log-likelihood gradients. While prior work used single-sample estimators obtained from a single run of the network, this paper proposes to leverage multiple compartments that sample independent spiking signals while sharing synaptic weights. The key idea is to use these signals to obtain more accurate statistical estimates of the log-likelihood training criterion, as well as of its gradient. The approach is based on generalized expectation-maximization (GEM), which optimizes a tighter approximation of the log-likelihood using importance sampling. The derived online learning algorithm implements a three-factor rule with global per-compartment learning signals. Experimental results on a classification task on the neuromorphic MNIST-DVS data set demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of compartments used for training and inference.
Evaluating Deep Learning in SystemML using Layer-wise Adaptive Rate Scaling(LARS) Optimizer

Increasing the batch size of a deep learning model is a challenging task. Although it might help in utilizing full available system memory during training phase of a model, it results in significant loss of test accuracy most often. LARS solved this issue by introducing an adaptive learning rate for each layer of a deep learning model. However, there are doubts on how popular distributed machine learning systems such as SystemML or MLlib will perform with this optimizer. In this work, we apply LARS optimizer to a deep learning model implemented using SystemML.We perform experiments with various batch sizes and compare the performance of LARS optimizer with \textit{Stochastic Gradient Descent}. Our experimental results show that LARS optimizer performs significantly better than Stochastic Gradient Descent for large batch sizes even with the distributed machine learning framework, SystemML.
Instance and Panoptic Segmentation Using Conditional Convolutions

We propose a simple yet effective framework for instance and panoptic segmentation, termed CondInst (conditional convolutions for instance and panoptic segmentation). In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance. In contrast, we propose to attend to the instances with dynamic conditional convolutions. Instead of using instance-wise ROIs as inputs to the instance mask head of fixed weights, we design dynamic instance-aware mask heads, conditioned on the instances to be predicted. CondInst enjoys three advantages: 1) Instance and panoptic segmentation are unified into a fully convolutional network, eliminating the need for ROI cropping and feature alignment. 2) The elimination of the ROI cropping also significantly improves the output instance mask resolution. 3) Due to the much improved capacity of dynamically-generated conditional convolutions, the mask head can be very compact (e.g., 3 conv. layers, each having only 8 channels), leading to significantly faster inference time per instance and making the overall inference time less relevant to the number of instances. We demonstrate a simpler method that can achieve improved accuracy and inference speed on both instance and panoptic segmentation tasks. On the COCO dataset, we outperform a few state-of-the-art methods. We hope that CondInst can be a strong baseline for instance and panoptic segmentation. Code is available at: https://git.io/AdelaiDet.
Graph Joint Attention Networks

Graph attention networks (GATs) have been recognized as powerful tools for learning in graph structured data. However, how to enable the attention mechanisms in GATs to smoothly consider both structural and feature information is still very challenging. In this paper, we propose Graph Joint Attention Networks (JATs) to address the aforementioned challenge. Different from previous attention-based graph neural networks (GNNs), JATs adopt novel joint attention mechanisms which can automatically determine the relative signiﬁcance between node features and structural coefﬁcients learned from graph topology, when computing the attention scores. Therefore, representations concerning more structural properties can be inferred by JATs. Besides, we theoretically analyze the expressive power of JATs and further propose an improved strategy for the joint attention mechanisms that enables JATs to reach the upper bound of expressive power which every message-passing GNN can ultimately achieve, i.e., 1-WL test. JATs can thereby be seen as most powerful message-passing GNNs. The proposed neural architecture has been extensively tested on widely used benchmarking datasets, and has been compared with state-of-the-art GNNs for various downstream predictive tasks. Experimental results show that JATs achieve state-of-the-art performance on all the testing datasets.
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression

Adversarial training is actively studied for learning robust models against adversarial examples. A recent study finds that adversarially trained models degenerate generalization performance on adversarial examples when their weight loss landscape, which is loss changes with respect to weights, is sharp. Unfortunately, it has been experimentally shown that adversarial training sharpens the weight loss landscape, but this phenomenon has not been theoretically clarified. Therefore, we theoretically analyze this phenomenon in this paper. As a first step, this paper proves that adversarial training with the L2 norm constraints sharpens the weight loss landscape in the linear logistic regression model. Our analysis reveals that the sharpness of the weight loss landscape is caused by the noise aligned in the direction of increasing the loss, which is used in adversarial training. We theoretically and experimentally confirm that the weight loss landscape becomes sharper as the magnitude of the noise of adversarial training increases in the linear logistic regression model. Moreover, we experimentally confirm the same phenomena in ResNet18 with softmax as a more general case.
On Coupling Matter Fields to Gauge Fields With Restrictions on the Loop Expansion

It has been shown that by using a Lagrange multiplier (LM) field to restrict field configurations that contribute to the path integral to those that satisfy the classical equations of motion, Green’s functions receive no contributions beyond one-loop order. We examine the consequences of this in greater detail for a scalar model, for Yang-Mills (YM) theory, and for the Einstein-Hilbert (EH) action. For the scalar model, it appears that renormalization of the couplings needed to eliminate divergences arising at one-loop order leads to results that do not respect unitarity when the LM field is present. However, this inconsistency is not present for YM theory; it becomes possible to compute all renormalization group (RG) functions exactly. For the EH action, restricting radiative corrections to one-loop order means that divergences can be removed by renormalizing the LM field, so that the model becomes consistent with renormalizability and unitarity. It is shown how these conclusions can be used to find a model in which scalar fields couple to a gauge field (either a metric or YM field) in a way consistent with renormalizability and unitarity, with the scalar field propagating in a background gauge field at all orders in the loop expansion.
Active Slices for Sliced Stein Discrepancy

Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated promising successes in goodness-of-fit tests and model learning in high dimensions. Despite their theoretical elegance, their empirical performance depends crucially on the search of optimal slicing directions to discriminate between two distributions. Unfortunately, previous gradient-based optimisation approaches for this task return sub-optimal results: they are computationally expensive, sensitive to initialization, and they lack theoretical guarantees for convergence. We address these issues in two steps. First, we provide theoretical results stating that the requirement of using optimal slicing directions in the kernelized version of SSD can be relaxed, validating the resulting discrepancy with finite random slicing directions. Second, given that good slicing directions are crucial for practical performance, we propose a fast algorithm for finding such slicing directions based on ideas of active sub-space construction and spectral decomposition. Experiments on goodness-of-fit tests and model learning show that our approach achieves both improved performance and faster convergence. Especially, we demonstrate a 14-80x speed-up in goodness-of-fit tests when comparing with gradient-based alternatives.
GIBBON: General-purpose Information-Based Bayesian OptimisatioN

This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the information gain -- an information-theoretic quantity central to solving a range of BO problems, including noisy, multi-fidelity and batch optimisations across both continuous and highly-structured discrete spaces. Previously, these problems have been tackled separately within information-theoretic BO, each requiring a different sophisticated approximation scheme, except for batch BO, for which no computationally-lightweight information-theoretic approach has previously been proposed. GIBBON (General-purpose Information-Based Bayesian OptimisatioN) provides a single principled framework suitable for all the above, out-performing existing approaches whilst incurring substantially lower computational overheads. In addition, GIBBON does not require the problem's search space to be Euclidean and so is the first high-performance yet computationally light-weight acquisition function that supports batch BO over general highly structured input spaces like molecular search and gene design. Moreover, our principled derivation of GIBBON yields a natural interpretation of a popular batch BO heuristic based on determinantal point processes. Finally, we analyse GIBBON across a suite of synthetic benchmark tasks, a molecular search loop, and as part of a challenging batch multi-fidelity framework for problems with controllable experimental noise.
The Critical Mean-field Chayes-Machta Dynamics

The random-cluster model is a unifying framework for studying random graphs, spin systems and electrical networks that plays a fundamental role in designing efficient Markov Chain Monte Carlo (MCMC) sampling algorithms for the classical ferromagnetic Ising and Potts models. In this paper, we study a natural non-local Markov chain known as the Chayes–Machta (CM) dynamics for the mean-field case of the random-cluster model, where the underlying graph is the complete graph on n vertices. The random-cluster model is parametrised by an edge probability p and a cluster weight q. Our focus is on the critical regime: $p = p_c(q)$ and $q \in (1,2)$ , where $p_c(q)$ is the threshold corresponding to the order–disorder phase transition of the model. We show that the mixing time of the CM dynamics is $O({\log}\ n \cdot \log \log n)$ in this parameter regime, which reveals that the dynamics does not undergo an exponential slowdown at criticality, a surprising fact that had been predicted (but not proved) by statistical physicists. This also provides a nearly optimal bound (up to the $\log\log n$ factor) for the mixing time of the mean-field CM dynamics in the only regime of parameters where no non-trivial bound was previously known. Our proof consists of a multi-phased coupling argument that combines several key ingredients, including a new local limit theorem, a precise bound on the maximum of symmetric random walks with varying step sizes and tailored estimates for critical random graphs. In addition, we derive an improved comparison inequality between the mixing time of the CM dynamics and that of the local Glauber dynamics on general graphs; this results in better mixing time bounds for the local dynamics in the mean-field setting.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for interpreting predictions from GNNs have primarily focused on generating subgraphs that are especially relevant for a particular prediction. However, such methods are not counterfactual (CF) in nature: given a prediction, we want to understand how the prediction can be changed in order to achieve an alternative outcome. In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes. Using only edge deletions, we find that our method, CF-GNNExplainer, can generate CF explanations for the majority of instances across three widely used datasets for GNN explanations, while removing less than 3 edges on average, with at least 94\% accuracy. This indicates that CF-GNNExplainer primarily removes edges that are crucial for the original predictions, resulting in minimal CF explanations.
Decoding of (Interleaved) Generalized Goppa Codes

Generalized Goppa codes are defined by a code locator set $L$ of polynomials and a Goppa polynomial G(x). When the degree of all code locator polynomials in $L$ is one, generalized Goppa codes are classical Goppa codes. In this work, binary generalized Goppa codes are investigated. First, a parity-check matrix for these codes with code locators of any degree is derived. A careful selection of the code locators leads to a lower bound on the minimum Hamming distance of generalized Goppa codes which improves upon previously known bounds. A quadratic-time decoding algorithm is presented which can decode errors up to half of the minimum distance. Interleaved generalized Goppa codes are introduced and a joint decoding algorithm is presented which can decode errors beyond half the minimum distance with high probability. Finally, some code parameters and how they apply to the Classic McEliece post-quantum cryptosystem are shown.