Miracle-dz/UniFAR-FTUs huggingface dataset:
Intolerance of Uncertainty, Negative Reinforcement Alcohol Use Motives, and Hazardous Drinking in College Students with Clinically Elevated Worry

Abstract Background: Hazardous drinking has remained high for college students in recent years, and individuals who endorse drinking to cope with emotional distress or to conform socially report higher levels of alcohol use. Intolerance of uncertainty, a core process underlying generalized anxiety disorder, has been linked to negative reinforcement drinking motives; however, no research, to date, has examined the role of intolerance of uncertainty in terms of alcohol use motives and hazardous drinking among individuals with generalized anxiety disorder. Objective: The current study was designed to examine the relationships between intolerance of uncertainty, coping and conformity alcohol use motives, and hazardous drinking in an analogue generalized anxiety disorder sample. Methods: Participants were 323 college students (Mage = 19.25, SD = 2.23, Range = 18–40) who endorsed past-year alcohol use and clinically elevated levels of worry. Self-report measures were completed online for course credit. Results: Partially consistent with our hypotheses, uncertainty paralysis predicted greater levels of coping motives, but not conformity motives. Desire for predictability did not predict either drinking motive. Mediation analyses revealed that there was a significant indirect effect of uncertainty paralysis on more hazardous drinking through greater coping motives. Conclusion: Overall, these findings highlight the potential utility of targeting behavioral inhibition due to uncertainty to reduce unhealthy coping via alcohol use and subsequent hazardous alcohol use.
A new semi-blind watermarking algorithm using ordered Hadamard transform

An efficient and fast watermarking algorithm based on ordered Hadamard transform (HT) is proposed. Since it uses a few information of watermark image in secret key, it is categorised as a semi-blind method. In our algorithm, both host and watermark images are decomposed into non-overlapping blocks. Next, they are mapped into transform domain as Hadamard coefficients. Then, several high frequency AC components of host image are substituted with watermark coefficients without using any strength factor. Not employing this factor guarantees the embedded image to be retrieved with primary quality. Moreover, using HT offers several advantages of fastness, higher image fidelity, greater reliability of watermark detection and higher data hiding capacity. The experimental results show that our method keeps the quality of host image and is robust to common attacks. Compared to frequency domain and hybrid methods, it has advantages of shorter processing time, acceptable robustness and ease of hardware implementation.
Oscillator strengths and transition probabilities for singly ionized terbium

Electric dipole transition probabilities and oscillator strengths for singly ionized terbium (Tb II) have been calculated with the weakest bound electron potential model (WBEPM) theory using experimental energy levels and theoretical expectation values of orbital radii corresponding to those energy levels under the assumption of the Jj coupling scheme. The transition probabilities and the oscillator strengths calculated have been compared with available data in the literature. A good agreement has been obtained. In this work, the WBEPM theory has been applied to heavy atoms, such as Tb II, for the first time.
Elektronische Nervenzellen

Das menschliche Gehirn funktioniert grundsätzlich anders als ein digitaler Computer, weshalb es bestimmte Aufgaben viel besser als dieser lösen kann. Hierfür wäre eine Computerarchitektur attraktiv, die künstlich die Eigenschaften biologischer Nervensysteme nachahmt. Vor allem die Verbindungen zwischen den Nervenzellen sind interessant: Diese Synapsen spielen bei Lernprozessen eine entscheidende Rolle. Bislang fehlten jedoch elektronische Bauelemente, die sich als künstliche Synapsen eignen. Memristoren, die zum Beispiel auf Tunnelkontakten basieren, könnten dies ändern.
The Health Impact of Long-Term Kratom (Mitragyna Speciosa) Use in Southern Thailand

Abstract Background Mitragyna speciosa or Kratom has been used in Thailand traditionally for its medicinal value. Despite case reports of kratom consumption causing adverse effects, research on its long-term health impact is limited. This study examines the long-term health impact of kratom use among people in Southern Thailand. Methods Three community-based surveys were conducted from 2011 to 2015. In the first and second surveys (2011 and 2012) a total of 1,118 male respondents comprising 355 regular kratom users, 171 occasional kratom users, 66 ex-users, and 592 non-users aged 25 or above, were recruited from 40 villages. All respondents were followed up in this study. However, not all respondents were successfully followed up throughout the entire set of studies. Results Common health complaints were no more common among kratom users than ex- and non-users, but more regular than occasional users claimed kratom to be addictive. Those with high kratom dependence scores were more likely to experience intense withdrawal symptoms, which developed 1-12 h after the last kratom intake. Over half (57.9%) of regular users had experienced intoxication effects compared to only 29.3% of occasional users. Kratom users were less likely to have a history of chronic diseases such as diabetes, hypertension, dyslipidemia than ex- and non-users. Conclusion Regular long-term chewing of fresh kratom leaves was not related to an increase in common health complaints, but may pose a drug dependence risk. Severe kratom dependents were more likely to suffer from intense withdrawal symptoms. Medical records revealed no death due to traditional kratom use, but the high prevalence of tobacco or/and hand rolled cigarette smoking among kratom users should be of concern.
Characterizing infinite dimensional manifolds topologically

© Association des collaborateurs de Nicolas Bourbaki, 1980, tous droits réservés. L’accès aux archives du séminaire Bourbaki (http://www.bourbaki. ens.fr/) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Representations, duals and quantum doubles of monoidal categories

[For the entire collection see Zbl 0742.00067. ]\par The Tanaka-Krein type equivalence between Hopf algebras and functored monoidal categories provides the heuristic strategy of this paper. The author introduces the notion of a double cross product of monoidal categories as a generalization of double cross product of Hopf algebras, and explains some of the motivation from physics (the representation theory for double quantum groups). \par The Hopf algebra constructions are formulated in terms of monoidal categories $\underline C$ and functors $\underline C\to\underline{\text{Vec}}$ (finite-dimensional vector spaces) and generalized by replacing $\underline{\text{Vec}}$ by another monoidal category $\underline V$. It is interesting to remark that the monoidal category $\underline C$ (functored over a category $\underline V$) has a ``Hopf algebra like structure'', $\underline V$ having the role of the ground field. If $\underline V$ is a quasitensor category, a coadjo!
Singular Value Decomposition based Steganography Technique for JPEG2000 Compressed Images

In this paper, a steganography technique for JPEG2000 compressed images using singular value decomposition in wavelet transform domain is proposed. In this technique, DWT is applied on the cover image to get wavelet coefficients and SVD is applied on these wavelet coefficients to get the singular values. Then secret data is embedded into these singular values using scaling factor. Different compression rates are considered for JPEG2000 images after embedding the secret images. GA is used to optimize the value of SF. Maximum capacity of the proposed technique is 25% of cover image size and maximum PSNR values between cover and its stego image is more than the PSNR of existing techniques. Embedding capacity of proposed technique is also higher than the embedding capacity of existing techniques. Also, PSNR between secret image and extracted image is high and hence the visual quality of the extracted secret image is good enough to the human visual system. Steganalysis tests are performed on the stego images to show imperceptibility of proposed technique.
Characterization of Breath Sensor at Different Frequencies in Outdoor Condition

This paper investigates a breath sensor device that is designed to detect the moisture in human bulk matrix in outdoor conditions. The human bulk matrix is rich in moisture and carbon dioxide apart from the 872 volatile organic compounds emanating from the human breath. Human breathing effort is a respiration process that involves inhalation and exhalation modes. The human bulk matrix is a product of the latter. Most research in human breath analysis is concentrating on both human bulk matrix and breathing pattern. The aim of this study is to characterize the fabricated breath sensor at different input wave frequencies in the outdoor environment. In this study, an outdoor experiment was carried out using the breath sensor device that is connected to the input waveform from the frequency generator and the output reading is captured using the oscilloscope. A single exhaled breath originated from the human subject is required to activate the breath sensor. This method is easier and simpler, and the output wave result is generated by an oscilloscope in real-time. The result indicates that the investigated breath sensor is suitable for clinical and healthcare monitoring. Early studies indicate that the breath sensor can diagnose diseases related to breathing problems such as sleep apnoea, asthma, and strokes.
Calculation of Top Event Probability of Fault Tree using BDD

ABSTRACT As the number of gates and basic events in fault trees increases, it becomes difficult to calculate the exact probability of the top event. In order to overcome this difficulty the BDD methodology can be used to calculate the exact top event probability for small and medium size fault trees in short time. Fault trees are converted to BDD by using CUDD library functions and a failure path search algorithm is proposed to calculate the exact top event probability. The backward search algorithm is more efficient than the forward one in finding failure paths and in the calculation of the top event probability. This backward search algorithm can reduce searching time in the identification of disjoint failure paths from BDD and can be considered as an effective tool to find the cut sets and the minimal cut sets for the given fault trees. 키워드 : 이진결정도, 단절집합, 고장경로, 사고수목, 최소단절집합 Key word : Binary Decision Diagram, Cut Set, Failure Path, Fault Tree, Minimal Cut Set Received 19 January 2016,
CoMeFa: Deploying Compute-in-Memory on FPGAs for Deep Learning Acceleration

Block random access memories (BRAMs) are the storage houses of FPGAs, providing extensive on-chip memory bandwidth to the compute units implemented using logic blocks and digital signal processing slices. We propose modifying BRAMs to convert them to CoMeFa (Compute-in-Memory Blocks for FPGAs) random access memories (RAMs). These RAMs provide highly parallel compute-in-memory by combining computation and storage capabilities in one block. CoMeFa RAMs utilize the true dual-port nature of FPGA BRAMs and contain multiple configurable single-bit bit-serial processing elements. CoMeFa RAMs can be used to compute with any precision, which is extremely important for applications like deep learning (DL). Adding CoMeFa RAMs to FPGAs significantly increases their compute density while also reducing data movement. We explore and propose two architectures of these RAMs: CoMeFa-D (optimized for delay) and CoMeFa-A (optimized for area). Compared to existing proposals, CoMeFa RAMs do not require changing the underlying static RAM technology like simultaneously activating multiple wordlines on the same port, and are practical to implement. CoMeFa RAMs are especially suitable for parallel and compute-intensive applications like DL, but these versatile blocks find applications in diverse applications like signal processing and databases, among others. By augmenting an Intel Arria 10–like FPGA with CoMeFa-D (CoMeFa-A) RAMs at the cost of 3.8% (1.2%) area, and with algorithmic improvements and efficient mapping, we observe a geomean speedup of 2.55× (1.85×) across microbenchmarks from various applications and a geomean speedup of up to 2.5× across multiple deep neural networks. Replacing all or some BRAMs with CoMeFa RAMs in FPGAs can make them better accelerators of DL workloads.
Hybrid PDES Simulation of HPC Networks Using Zombie Packets

High-fidelity network simulations provide insights into new realms for high-performance computing (HPC) architectures, although at a high cost. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should be only used when appropriate. Thus the need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. We present a surrogate model for HPC networks in which packets bypass the network, and the network state itself is suspended when switching to the surrogate. To bypass the network, every packet is scheduled to arrive at a predicted time in the future estimated from historical data; to suspend the network, all in-flight packets are delivered to their destinations, but they are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. We obtained a 3 × speedup for a simulation where 70% of virtual time was spent in surrogate mode. When considering the surrogate portion only, the speedup jumps to nearly 20 × on a uniform random network traffic example. The accuracy of the overall simulation increased when the network state was suspended instead of ignored, which demonstrates the need for modeling the network state when transitioning from surrogate back to high-fidelity mode.
Proposal to Improve The Classification of School Violence

Nowadays, violence in movies and in society is on the rise, which has a significant impact on children, particularly adolescents. The prevalence of school violence is increasing and it is becoming a concern for schools, families, and society as a whole. However, because the school violence detection system has not yet been developed, our lab created VSiSGU data based on the collection of camera data from within the school as well as data from social networks. There are also many techniques for processing continuous image sequence data from cameras in order to detect school violence. As a result, we propose a method for improving performance by selecting frames at the l, l+k, l+2k,. , l+nk positions in the videos to train. After that, we use the VGGNet algorithm combined with RNN to develop a training model on the above data. The evaluation results show that our proposed method is more efficient in terms of time and still ensures higher or equivalent accuracy than the traditional sampling method.
Exploring Machine Learning Models with Spatial-Temporal Information for Interconnect Network Traffic Forecasting

Interconnect networks are an essential component of high-performance computing (HPC) systems. To study large-scale networking systems, parallel discrete event simulation (PDES) has been widely used to simulate real-world HPC behaviors. However, PDES simulation requirements and computational complexity are increasing rapidly, making it challenging to achieve accurate results. Therefore, researchers have been exploring a surrogate-ready PDES framework that utilizes machine learning-based surrogate models to accelerate PDES. In this paper, we present our vision and initial step to leverage machine learning models to utilize spatial-temporal information to forecast interconnect network traffic. The preliminary results show that it is promising to explore machine learning models for interconnect network traffic forecasting.
Strong isotope effect in ultracold collision of N{sub 2}{sup +} ({nu}=1, j=0) with He: A case study of virtual-state scattering

A comparative study of the vibrational deactivation of {sup 14}N{sub 2}{sup +} ({nu}=1, j=0) in collision with {sup 3}He and {sup 4}He atoms is investigated. Close-coupling calculations are carried out in the 10{sup -6}-2000-cm{sup -1} kinetic energy range. At very low kinetic energy, it is found that the cross sections for the collision involving {sup 3}He is strongly enhanced by the presence of a virtual state associated with the van der Waals well. The change of mass from {sup 3}He to {sup 4}He also erases a strong shape resonance at 5x10{sup -3} cm{sup -1} supported by the van der Waals well. As a result, the law of variation of the associated vibrational quenching rate coefficient is found to be very different for these two systems. This behavior is further analyzed by drawing the real part of the scattering length as a function of the variable mass of a fictitious He atom. Two zero energy resonances are identified for a mass of, respectively, about 3.18 and 3.85 atomic mass units.
Measuring the impacts of quantity and trustworthiness of information on COVID‐19 vaccination intent

The COVID‐19 crisis provided an opportunity for information professionals to rethink the role of information in individuals' decision making such as vaccine uptake. Unlike previous studies, which often considered information as a single factor among others, this study examined the impact of the quantity and trustworthiness of information on people's adoption of information for vaccination decisions based on the information adoption model. We analyzed COVID‐19 Preventive Behavior Survey data collected by the Massachusetts Institute of Technology from Facebook users (N = 82,213) in 15 countries between October 2020 and March 2021. The results of logistic regression analyses indicate that reasonable quantity and trustworthiness of information were positively related to COVID‐19 vaccination intent. But excessive and less than the desired amount of information was more likely to have negative impacts on vaccination intent. The degrees of trust in the mediums and in the sources were associated with the level of vaccine acceptance. But the effects of trustworthiness accorded to information sources showed variations across sources and mediums. Implications for information professionals and suggestions for policies are discussed.
Predicting binding affinity changes from long-distance mutations using MD simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions. Author Summary Protein-protein interactions are vital to almost all biological processes, and therefore the ability to accurately and efficiently predict how mutations alter protein binding has far-reaching applications in protein analysis and design. Current approaches to predict such mutational free energy changes are limited to mutations directly at the interaction interface. Much research has underlined the prevalence of allosteric protein regulation in biological processes, indicating the importance of understanding and predicting the effects of protein perturbations which act over long distances. In this work we develop a novel method based on molecular dynamics simulations, the Rosetta macromolecular modeling suite, and an analytical framework from alchemical free energy calculations which can predict the effects of long-distance mutations with levels of accuracy rivaling state of the art interface-specific methods. We hope that our method will serve as a novel framework for high throughput mutational analysis and therefore benefit future protein design efforts.
The road to restoration: Identifying and conserving the adaptive legacy of American chestnut

The American chestnut (Castanea dentata) is a functionally extinct tree species that was decimated by an invasive fungal pathogen in the early 20th century. Disease resistant chestnuts have been developed through hybridization and genetic modification, but these populations may lack the adaptive genomic diversity necessary to restore the species across its climatically diverse historical range. An understanding of the genomic architecture of local adaptation in wild American chestnut and identification of seed zones for germplasm conservation are necessary in order to deploy locally adapted, disease-resistant American chestnut populations. Here, we characterize the genomic basis of climate adaptation in remnant wild American chestnut, define seed zones based on climate envelopes and adaptive diversity, and make sampling recommendations for germplasm conservation. Whole genome re-sequencing of 384 trees coupled with genotype-environment association methods suggest the species range can be most parsimoniously subdivided into three seed zones characterized by relatively homogeneous allele frequencies relative to rangewide adaptive diversity. Using these data, we developed a method to estimate the number of samples required from each seed zone to recapitulate standing adaptive diversity in each seed zone, and found that on average, 21-29 trees will need to be conserved to capture 95% of the wild adaptive diversity. Taken together, these results will inform the development of an ex situ germplasm conservation and breeding plan to develop locally adapted blight-resistant American chestnut populations, and provide a blueprint for developing restoration plans for other imperiled tree species.