Miracle-dz/UniFAR-FTUs huggingface dataset:
iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps

MOTIVATION One of the main challenges in applying graph convolutional neural networks on gene-interaction data is the lack of understanding of the vector space to which they belong, and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces. The challenge becomes more prevalent when dealing with various types of heterogeneous data. We introduce a systematic, generalized method, called iSOM-GSN, used to transform "multi-omic" data with higher dimensions onto a two-dimensional grid. Afterwards, we apply a convolutional neural network to predict disease states of various types. Based on the idea of Kohonen's self-organizing map, we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network. RESULTS We have tested the model to predict breast and prostate cancer using gene expression, DNA methylation, and copy number alteration. Prediction accuracies in the 94-98% range were obtained for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 14 input genes for both cases. The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for representation learning, visualization, dimensionality reduction, and interpretation of multi-omic data. AVAILABILITY The source code and sample data are available via a Github project at https://github.com/NaziaFatima/iSOM_GSN. SUPPLEMENTARY INFORMATION Supplementary figures and data availability are in the Supplementary Material file.
Opening the Black Box of Financing Decision-Making

Financing decisions are an important topic for all firms. Especially in family firms this topic is of high interest due to the interplay of financial and non-financial goals. Recent studies so far have resulted in inconsistent findings, as the process of how these decisions are made is not well understood. This thesis aims to provide an understanding for how the financing decision-making process unfolds in family firms. Our approach is guided by strategy-as-practice and the concepts of strategic actors and arenas in order to uncover where the process unfolds and who is involved. Based on a case study approach in the German context, the study suggests that the financing process develops through the alternation between formal and informal arenas. While in formal arenas conformity is desired, in the informal arenas friction is tolerated and practitioners can discuss openly their opinions and ideas. Our findings add to the theoretical understanding of micro-activities in family businesses by outlining external arenas and actors as an overlooked component. Furthermore, the study contributes to current research about financing in the context of family firms by highlighting the role of norms and how they influence usable practices. In addition, this thesis is of interest to practitioners who are involved in financing decision-making processes in family firms as it provides them with better understanding how to approach the process.
VERIFIABLY ROBUST NEURAL NETWORKS

Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training. In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass. CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in `∞ robustness. Notably, we achieve 7.02% verified test error on MNIST at = 0.3, and 66.94% on CIFAR-10 with = 8/255.
The Effect of Administrative Obstacles on Job Performance: A Case of Universities in South-South, Nigeria

This study investigates the effect of administrative obstacles on job performance; a case of universities in south-south, Nigeria. Two hypotheses were formulated and tested by using multiple regression technique to justify the truth or otherwise of the hypotheses. The tested null hypotheses were rejected and the alternate hypotheses retained. The inference was drawn that, the administrative obstacles of male and female heads of department correlates with their job performance, and that a significant relationship exist between physical facilities and job performance of heads of department. The impact in the variation is a clear testimony of the fact that administrative obstacles and job performance does not differ significantly from one another. It was recommended from the findings that administrative obstacles seminars and workshop should be organized for incumbent heads of department, so as to expose those administrative obstacles that are inherent in the work environment. Adequate infrastructural facilities should be provided in our universities by the management to ease the job performance of heads of department; and urgent steps such as provision of physical facilities, organization of workshops and seminars should be taken to address the problem of administrative obstacles in the university system.
Nonlinear partial differential equations model related to ethanol production

The study presents a mathematical model of an ethanol production system via fermentation. This model was extended from the established model to examine mass transfer and the inhibition effects on microbial such as yeasts or bacteria, sugar as its substrate and ethanol for the product. In this study, two types of laboratory-scale fermentation are considered, i.e. shaker fermentation and shaker-free fermentation. This led to studying the coupled diffusionreaction and coupled diffusion-reaction-advection models from a previous mathematical model which describes mass transfer. A better understanding of those model able to predict the behaviour of mass transfer effect on fermentation scenarios. The effect of the diffusion coefficient and the advection coefficient are investigated to simulate the dynamical behaviour of the system. Since the model is nonlinear partial differential equations (PDEs), Gear’s algorithm, a numerical method was employed to solve the system while the Nelder–Mead method is utilised to estimate the value of the parameters. The result shows that the diffusion does not have a huge impact on the whole ethanol production system but is contrary to advection. In order to affect the ethanol production system, only tiny advection value is needed, however, a big diffusion value is necessary to achieve the same effect.
Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods

The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning. The Hessian is often used to understand these geometric properties. We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian. Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian. We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16. To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature. This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.
Effects of electrical stimulation and age at slaughter on carcass and meat quality of two Sudanese Baggara beef types

This study aimed to evaluate the influences of electrical stimulation (ES), age at slaughter, and breed type on muscle pH, the decline in carcass temperature, and meat quality attributes of Sudanese indigenous Baggara cattle. Eighty Baggara bulls, representative of Nyalawi (n = 40) and Mesairi (n = 40) breed types, were selected at their typical marketing age of about 4.5 years. Electrical stimulation was applied for 30 seconds at 20 minutes post mortem to 20 randomly selected carcasses from each breed type and compared with 20 carcasses from each type that were not electrically stimulated (NES). Samples of the Longissimus dorsi muscle were collected for meat analyses. Breed type showed no significant influence on meat quality characteristics, while ES and age at slaughter did. Electrical stimulation accelerated the carcass pH decline significantly up to 24 hours post mortem. Meat from electrically stimulated carcasses and younger animals resulted in higher L* values, lower a* values, higher hue values, and better tenderness. Older Mesairi animals had darker meat than their younger counterparts. Electrical stimulation reduced water-holding capacity (WHC), although it had no influence on cooking loss (CL). Meat from older cattle showed better WHC compared with meat from younger animals. The ES treatment decreased the variations in meat tenderness between the younger and older bulls. It is concluded that the use of ES and younger bulls produced more tender meat with better colour. Therefore, these practices should be adopted in Sudan to ensure better beef quality management. Keywords : Longissimus dorsi muscle, meat characteristics, Mesairi, Nyalawi, Sudan
Treatment Mulberry Trees with Some Insecticides and Its Effect on the Productivity of Silkworm, Bombyx mori L.

During the spring season of 2019, the effect of two insecticides (Cymbush®&Tracer®) on larvae of Bombyx mori L. were studied at Plant Protec. Dept. Fac. of Agric. , Fayoum Univ. Insecticides were sprayed on mulberry trees by concentration 1ml/l . of Cymbush® and 0.1ml/l. of Tracer® . Mulberry leaves were harvested on to three times (after 3,6,9,12 days). Results show that, the 5th instar larval mortality percentages increased , where data ranged between 18.30% in Cymbush® and 10.21% in Tracer® compared to 9.93% in control. While cocooning percentages, cocoon shell ratio and silk productivity were decreased when comparing to control, where data ranged between 74.00% in Cymbush®, 89.08% in Tracer® , 12.00% in Cymbush®,19.07% in Tracer® and 1.021cg/day in Cymbush®, 1.800cg/day in Tracer® , respectively compared to 93.00%, 19.77% and 1.910cg/day in control, respectively.
Using Explainabilty to Detect Adversarial Attacks

Deep learning models are often sensitive to adversarial attacks, where carefully-designed input samples can cause the system to produce incorrect decisions. Here we focus on the problem of detecting attacks, rather than robust classification, since detecting that an attack occurs may be even more important than avoiding misclassification. We build on advances in explainability, where activity-map-like explanations are used to justify and validate decisions, by highlighting features that are involved with a classification decision. The key observation is that it is hard to create explanations for incorrect decisions. We propose EXAID, a novel attack-detection approach, which uses model explainability to identify images whose explanations are inconsistent with the predicted class. Specifically, we use SHAP, which uses Shapley values in the space of the input image, to identify which input features contribute to a class decision. Interestingly, this approach does not require to modify the attacked model, and it can be applied without modelling a specific attack. It can therefore be applied successfully to detect unfamiliar attacks, that were unknown at the time the detection model was designed. We evaluate EXAID on two benchmark datasets CIFAR-10 and SVHN, and against three leading attack techniques, FGSM, PGD and C&W. We find that EXAID improves over the SoTA detection methods by a large margin across a wide range of noise levels, improving detection from ∼ 70% to over 90% for small perturbations.
An Association between Religiosity and Consumer Behavior: A Conceptual Piece

Religion is found to be a very important element of culture and it plays an important role by influencing consumers’ attitude and behavior to purchase or consume products or brands. Religiosity is a different thing from religion but within a particular religion, religiosity also influences the consumers’ attitudes and behaviors, according to the values and laws of a particular religion. It is also evident that consumers who have high levels of religiosity follow the rules of religious obligation more rigorously than who have a lower level of religiosity. Religion as an inherent human value has received little attention from consumer researchers. The reason for ignoring the religion in evaluations of consumer processes is that most of the academicians are less religious than the general public and that is why they give less attention to religion to understand consumer behavior. Although a limited number of studies come in consumer literature, but majority section of the scholars ignores this area. Studies on religion and consumer should be conducted with the same level of rigor and objectivity as other influential social characteristics for developing a comprehensive understanding of consumer
Household energy use in Uganda: existing sources, consumption, and future challenges

This paper details patterns of energy consumption for domestic buildings in urban areas of Uganda. The paper shows the range of energy sources employed by households, the level of consumption of energy, as well as common appliances and equipment in use. The findings suggest strong demand for energy, but largely from solid fuel sources, with most households making use of firewood or charcoal for cooking. While currently low by world standards, electrical energy use was largely for lighting and entertainment. Efforts at increasing access to electricity to reduce pressures on dwindling forest resources, although a noble goal, has significant challenges, linked to limited and erratic availability of electricity, approaches to building design, as well as lifestyle transformations that contribute to a growth in energy demand. The study itself contributes to discourse on energy use and energy efficiency in buildings, filling the gap in the availability of information and geared to informing future policy and interventions.
Safeguarded Learned Convex Optimization

Applications abound in which optimization problems must be repeatedly solved, each time with new (but similar) data. Analytic optimization algorithms can be hand-designed to provably solve these problems in an iterative fashion. On one hand, data-driven algorithms can "learn to optimize" (L2O) with much fewer iterations and similar cost per iteration as general-purpose optimization algorithms. On the other hand, unfortunately, many L2O algorithms lack converge guarantees. To fuse the advantages of these approaches, we present a Safe-L2O framework. Safe-L2O updates incorporate a safeguard to guarantee convergence for convex problems with proximal and/or gradient oracles. The safeguard is simple and computationally cheap to implement, and it is activated only when the data-driven L2O updates would perform poorly or appear to diverge. This yields the numerical benefits of employing machine learning to create rapid L2O algorithms while still guaranteeing convergence. Our numerical examples show convergence of Safe-L2O algorithms, even when the provided data is not from the distribution of training data.
NDT_A_226405 3411..3417

Dongping Rao 1 Guiyun Xu Zenghong Lu 2 Huiwei Liang Kangguang Lin Muni Tang 1Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, People’s Republic of China; 2The First Affiliated Hospital of Gannan Medical University, Jiangxi, People’s Republic of China Objective: Despite reports of cognitive dysfunction during the acute phase of depression, there is a lack of studies in patients with treatment-resistant depression (TRD). The aim of this study was to investigate the cognitive function profile of TRD and compare cognitive dysfunction between subjects with TRD and first-episode depression. Patients and methods: The study included 31 patients with TRD and 53 with first-episode depression. Cognitive function was assessed by a series of neuropsychological tools such as the verbal fluency test, Modified Wisconsin Card Sorting Test (M-WCST), Tower of Hanoi test, Chinese-revision of the Wechsler Adult Intelligence Scale (WAIS-RC), and Trail Making Test A and B. Results: There were no significant demographic differences between the TRD, first-episode depression, and normal control groups (gender, age, years of education). The full-scale, verbal, and performance intelligence quotients measured with the WAIS-RC were also not significantly different (p>0.05). The normal group scores were all significantly better than TRD and first-episode depression, and the TRD group performed significantly worse than subjects with first-episode depression on Trail Making Test B, two WCST subscales, and the profile score of the Tower of Hanoi test (all p<0.05). Conclusion: Patients with depression exhibited global impairments in cognitive function, and these were more common in TRD. Poor executive function may play an important role in TRD.
BUDGETARY PARTICIPATION, PERSONALITY AND PERCEPTION

Managerial accounting researchers have long assumed that employees’ perception of their level of participation in the budget setting process accurately reflects their structural level of participation. However, other factors, such as individual differences (personality), may impact how employees perceive their level of participation. Since managers can only control the structural level participation in the budget setting process, it is important to understand the factors which can influence employee perception of participation. This study uses an experiment with students at a Midwest university to test how personality differences affect perception of varying levels of structural participation. Results suggest individuals perceive types of participation differently, and personality significantly impacts how individuals perceive their involvement in the budget setting process.
KEIO/KYOTO JOINT GLOBAL CENTER OF EXCELLENCE PROGRAM Raising Market Quality-Integrated Design of “Market Infrastructure” KEIO/KYOTO GLOBAL COE DISCUSSION PAPER SERIES

We use structural method, that is, Dynamic Stochastic General Equilibrium (DSGE) model with fiscal stabilization rules, for calculating the tax revenue elasticity rate and estimate more plausible value of it. In the short-run, the tax revenue elasticity to output takes negative value and, in medium-run, it takes quite large positive values (from 2.3 to 4) in both permanent and temporary positive productivity shocks. On the other hand, in the long-run, under permanent positive productivity shock remains nearly the value of the tax revenue elasticity converses to about 2.3. But,under temporary one, it decreases and reverses it. *Kazuki Hiraga Faculty of Economics, Keio University KEIO/KYOTO JOINT GLOBAL COE PROGRAM Raising Market Quality-Integrated Design of “Market Infrastructure” Graduate School of Economics and Graduate School of Business and Commerce, Keio University 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan Institute of Economic Research, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan 1 Measuring the Tax Revenue Elasticity to Output in Dynamic Stochastic General Equilibrium Model1 Kazuki Hiraga Faculty of Economics, Keio University Mail:kazukihiraga7041@gmail.com
The Impact of Serum Glucose on the Predictive Value of Serum Lactate for Hospital Mortality in Critically Ill Surgical Patients

Background Lactate has been widely used as a risk indicator of outcomes in critically ill patients due to its ready measurement and good predictive ability. However, the interconnections between lactate metabolism and glucose metabolism have not been sufficiently explored, yet. In this study, we aimed to investigate whether glucose levels could influence the predictive ability of lactate and design a more comprehensive strategy to assess the in-hospital mortality of critically ill patients. Methods We analyzed the clinical data of 293 critically ill patients. The primary outcome was in-hospital mortality. The logistic regression analysis and the area under the receiver operating characteristic curve (AUROC) were applied to evaluate the predictive ability of lactate in association with glucose. Results The lactate level showed significant association with in-hospital mortality, and its predictive ability was also comparable to other prognostic scores such as the SOFA score and APACHE II score. We further divided 293 patients into three groups based on glucose levels: low-glucose group (<7 mmol/L), medium-glucose group (7-9 mmol/L), and high-glucose group (>9 mmol/L). The lactate level was associated with in-hospital mortality in the low- and high- glucose groups, but not in the medium-glucose group, whereas the SOFA score and APACHE II score were associated with in-hospital mortality in all three glucose groups. The AUROC of lactate in the medium-glucose group was also the lowest among the three glucose groups, indicating a decrease in its predictive ability. Conclusions Our findings demonstrated that the predictive ability of lactate to assess in-hospital mortality could be influenced by glucose levels. In the medium glucose level (i.e., 7-9 mmol/L), lactate was inadequate to predict in-hospital mortality and the SOFA score; the APACHE II score should be utilized as a complementation in order to obtain a more accurate prediction.
Recovery Attitudes Questionnaire: Development and evaluation.

A questionnaire to assess attitudes toward recovery from psychiatric disorders was developed in conjunction with consumers from the Hamilton County Recovery Initiative. People with mental disorders, family members, professionals and others were surveyed, and 844 completed questionnaires constituted the final sample. This study reports the development and analysis of the questionnaire. The original 21 item scale was reduced to 7 items loading on two factors: Factor 1 “Recovery is possible and needs faith,” Factor 2, “Recovery is difficult and differs among people. ” Differences in attitudes were found among professionals, people with mental disorders, family members, and others. The questionnaire can be used to help assess feelings about recovery and possibly determine for whom services focusing on advocacy and self-help might be more efficacious than traditional mental heath services.