bluuebunny/crossref_metadata_2025 huggingface dataset:
Mercer Features for Efficient Combinatorial Bayesian Optimization
Authors: Deshwal Aryan , Belakaria Syrine , Doppa Janardhan Rao
Published: September 2022

Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting for combinatorial spaces (e.g., sequences and graphs) that occurs naturally in science and engineering applications. A prototypical example is molecular optimization guided by expensive experiments. The key challenge is to balance the complexity of statistical models and tractability of search to select combinatorial structures for evaluation. In this paper, we propose an efficient approach referred as Mercer Features for Combinatorial Bayesian Optimization (MerCBO). The key idea behind MerCBO is to provide explicit feature maps for diffusion kernels over discrete objects by exploiting the structure of their combinatorial graph representation. These Mercer features combined with Thompson sampling as the acquisition function allows us to employ efficient solvers for finding the next structure for evaluation. Experimental evaluation on diverse real-world benchmarks demonstrates that MerCBO performs similarly or better than prior methods.
Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment
Authors: Fu Mengying , Yuan Tianning , Wan Fang , Xu Songcen , Ye Qixiang
Published: September 2022

In active learning, the ignorance of aligning unlabeled samples' distribution with that of labeled samples hinders the model trained upon labeled samples from selecting informative unlabeled samples. In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignment with sample selection by introducing adversarial classifiers to the convolutional neural network (CNN). Minimizing classifiers' prediction discrepancy (maximizing prediction agreement) drives learning CNN features to reduce the distribution bias of labeled and unlabeled samples, while maximizing classifiers' discrepancy highlights informative samples. Iterative optimization of agreement and discrepancy loss calibrated with an entropy function drives aligning sample distributions in a progressive fashion for effective active learning. Experiments on image classification and object detection tasks demonstrate that ADS is task-agnostic, while significantly outperforms the previous methods when the labeled sets are small.
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
Authors: Fu Zhang-Hua , Qiu Kai-Bin , Zha Hongyuan
Published: September 2022

For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental results based on a large number of instances (with up to 10,000 vertices) show that, this new approach clearly outperforms the existing machine learning based TSP algorithms, and significantly improves the generalization ability of the trained model.
Stabilizing Q Learning Via Soft Mellowmax Operator
Authors: Gan Yaozhong , Zhang Zhe , Tan Xiaoyang
Published: September 2022

Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal difference updates can theoretically cause instability for most linear or non-linear approximation schemes. Mellowmax is a recently proposed differentiable and non-expansion softmax operator that allows a convergent behavior in learning and planning. Unfortunately, the performance bound for the fixed point it converges to remains unclear, and in practice, its parameter is sensitive to various domains and has to be tuned case by case. Finally, the Mellowmax operator may suffer from oversmoothing as it ignores the probability being taken for each action when aggregating them. In this paper we address all the above issues with an enhanced Mellowmax operator, named SM2 (Soft Mellowmax). Particularly, the proposed operator is reliable, easy to implement, and has provable performance guarantee, while preserving all the advantages of Mellowmax. Furthermore, we show that our SM2 operator can be applied to the challenging multi-agent reinforcement learning scenarios, leading to stable value function approximation and state of the art performance.
Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization
Authors: Gu Jiaqi , Feng Chenghao , Zhao Zheng , Ying Zhoufeng , Chen Ray T. , Pan David Z.
Published: September 2022

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption. However, current learning protocols fail to provide scalable and efficient solutions to photonic circuit optimization in practical applications. In this work, we propose a novel on-chip learning framework to release the full potential of ONNs for power-efficient in situ training. Instead of deploying implementation-costly back-propagation, we directly optimize the device configurations with computation budgets and power constraints. We are the first to model the ONN on-chip learning as a resource-constrained stochastic noisy zeroth-order optimization problem, and propose a novel mixed-training strategy with two-level sparsity and power-aware dynamic pruning to offer a scalable on-chip training solution in practical ONN deployment. Compared with previous methods, we are the first to optimize over 2,500 optical components on chip. We can achieve much better optimization stability, 3.7x-7.6x higher efficiency, and save >90% power under practical device variations and thermal crosstalk.
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Authors: Gupta Umang , Ferber Aaron M , Dilkina Bistra , Ver Steeg Greg
Published: September 2022

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise between fairness and accuracy is desired. Another common approach is to limit the ability of a particular adversary who seeks to maximize parity. Unfortunately, representations produced by adversarial approaches may still retain biases as their efficacy is tied to the complexity of the adversary used during training. To this end, we theoretically establish that by limiting the mutual information between representations and protected attributes, we can assuredly control the parity of any downstream classifier. We demonstrate an effective method for controlling parity through mutual information based on contrastive information estimators and show that they outperform approaches that rely on variational bounds based on complex generative models. We test our approach on UCI Adult and Heritage Health datasets and demonstrate that our approach provides more informative representations across a range of desired parity thresholds while providing strong theoretical guarantees on the parity of any downstream algorithm.
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Authors: Hasanbeig Mohammadhosein , Yogananda Jeppu Natasha , Abate Alessandro , Melham Tom , Kroening Daniel
Published: September 2022

This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton so that the generation of a control policy by deep RL is guided by the discovered structure encoded in the automaton. The proposed approach is able to cope with both high-dimensional, low-level features and unknown sparse non-Markovian rewards. We have evaluated DeepSynth's performance in a set of experiments that includes the Atari game Montezuma's Revenge. Compared to existing approaches, we obtain a reduction of two orders of magnitude in the number of iterations required for policy synthesis, and also a significant improvement in scalability.
Liquid Time-constant Networks
Authors: Hasani Ramin , Lechner Mathias , Amini Alexander , Rus Daniela , Grosu Radu
Published: September 2022

We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics, and compute their expressive power by the trajectory length measure in a latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs.
Analysing the Noise Model Error for Realistic Noisy Label Data
Authors: Hedderich Michael A. , Zhu Dawei , Klakow Dietrich
Published: September 2022

Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A popular technique to overcome the negative effects of these noisy labels is noise modelling where the underlying noise process is modelled. In this work, we study the quality of these estimated noise models from the theoretical side by deriving the expected error of the noise model. Apart from evaluating the theoretical results on commonly used synthetic noise, we also publish NoisyNER, a new noisy label dataset from the NLP domain that was obtained through a realistic distant supervision technique. It provides seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances. Parallel, clean labels are available making it possible to study scenarios where a small amount of gold-standard data can be leveraged. Our theoretical results and the corresponding experiments give insights into the factors that influence the noise model estimation like the noise distribution and the sampling technique.
Boosting Multi-task Learning Through Combination of Task Labels - with Applications in ECG Phenotyping
Authors: Hsieh Ming-En , Tseng Vincent
Published: September 2022

Multi-task learning has increased in importance due to its superior performance by learning multiple different tasks simultaneously and its ability to perform several different tasks using a single model. In medical phenotyping, task labels are costly to acquire and might contain a certain degree of label noise. This decreases the efficiency of using additional human labels as auxiliary tasks when applying multi-task learning to medical phenotyping. In this work, we proposed an effective multi-task learning framework, CO-TASK, to boost multi-task learning performance by generating auxiliary tasks through COmbination of TASK Labels. The proposed CO-TASK framework generates auxiliary tasks without additional labeling effort, is robust to a certain degree of label noise, and can be applied in parallel with various multi-task learning techniques.

We evaluated our performance using the CIFAR-MTL dataset and demonstrated its effectiveness in medical phenotyping using two large-scale ECG phenotyping datasets, an 18 diseases multi-label ECG-P18 dataset and an echocardiogram diagnostic from electrocardiogram dataset ECG-EchoLVH. On the CIFAR-MTL dataset, we doubled the average per-task performance gain of the multi-task learning model from 4.38% to 9.78%. With the proposed task-aware imbalance data sampler, the CO-TASK framework can effectively deal with the different imbalance ratios for the different tasks in electrocardiogram phenotyping datasets. The proposed framework combined with noisy annotations as minor tasks increased the sensitivity by 7.1% compared to the single-task model while maintaining the same specificity as the doctor annotations on the ECG-EchoLVH dataset.
Multidimensional Uncertainty-Aware Evidential Neural Networks
Authors: Hu Yibo , Ou Yuzhe , Zhao Xujiang , Cho Jin-Hee , Chen Feng
Published: September 2022

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.
Adversarial Defence by Diversified Simultaneous Training of Deep Ensembles
Authors: Huang Bo , Ke Zhiwei , Wang Yi , Wang Wei , Shen Linlin , Liu Feng
Published: September 2022

Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mostly devised on individual classifiers. Recent studies showed that it is viable to increase adversarial robustness by promoting diversity over an ensemble of models. In this paper, we propose adversarial defence by encouraging ensemble diversity on learning high-level feature representations and gradient dispersion in simultaneous training of deep ensemble networks. We perform extensive evaluations under white-box and black-box attacks including transferred examples and adaptive attacks. Our approach achieves a significant gain of up to 52% in adversarial robustness, compared with the baseline and the state-of-the-art method on image benchmarks with complex data scenes. The proposed approach complements the defence paradigm of adversarial training, and can further boost the performance. The source code is available at https://github.com/ALIS-Lab/AAAI2021-PDD.
Accurate and Robust Feature Importance Estimation under Distribution Shifts
Authors: J. Thiagarajan Jayaraman , Narayanaswamy Vivek , Anirudh Rushil , Bremer Peer-Timo , Spanias Andreas
Published: September 2022

With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often used to enable humans understand and trust these models. In particular, we focus on the class of methods that can reveal the influence of input features on the predicted outputs. Despite their wide-spread adoption, existing methods are known to suffer from one or more of the following challenges: computational complexities, large uncertainties and most importantly, inability to handle real-world domain shifts. In this paper, we propose PRoFILE (Producing Robust Feature Importances using Loss Estimates), a novel feature importance estimation method that addresses all these challenges. Through the use of a loss estimator jointly trained with the predictive model and a causal objective, PRoFILE can accurately estimate the feature importance scores even under complex distribution shifts, without any additional re-training. To this end, we also develop learning strategies for training the loss estimator, namely contrastive and dropout calibration, and find that it can effectively detect distribution shifts. Using empirical studies on several benchmark image and non-image data, we show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
Neural Utility Functions
Authors: Jenkins Porter , Farag Ahmad , Jenkins J. Stockton , Yao Huaxiu , Wang Suhang , Li Zhenhui
Published: September 2022

Current neural network architectures have no mechanism for explicitly reasoning about item trade-offs. Such trade-offs are important for popular tasks such as recommendation. The main idea of this work is to give neural networks inductive biases that are inspired by economic theories. To this end, we propose Neural Utility Functions, which directly optimize the gradients of a neural network so that they are more consistent with utility theory, a mathematical framework for modeling choice among items. We demonstrate that Neural Utility Functions can recover theoretical item relationships better than vanilla neural networks, analytically show existing neural networks are not quasi-concave and do not inherently reason about trade-offs, and that augmenting existing models with a utility loss function improves recommendation results. The Neural Utility Functions we propose are theoretically motivated, and yield strong empirical results.
Power up! Robust Graph Convolutional Network via Graph Powering
Authors: Jin Ming , Chang Heng , Zhu Wenwu , Sojoudi Somayeh
Published: September 2022

Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be vulnerable to topological attacks. To enhance adversarial robustness, we go beyond spectral graph theory to robust graph theory. By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability. By extending the original graph to a sequence of graphs, we also propose a robust training paradigm that encourages transferability across graphs that span a range of spatial and spectral characteristics. The proposed approaches are demonstrated in extensive experiments to simultaneously improve performance in both benign and adversarial situations.
Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network
Authors: Kim Jong-Yeong , Choi Dong-Wan
Published: September 2022

Continual learning has been a major problem in the deep learning community, where the main challenge is how to effectively learn a series of newly arriving tasks without forgetting the knowledge of previous tasks. Initiated by Learning without Forgetting (LwF), many of the existing works report that knowledge distillation is effective to preserve the previous knowledge, and hence they commonly use a soft label for the old task, namely a knowledge distillation (KD) loss, together with a class label for the new task, namely a cross entropy (CE) loss, to form a composite loss for a single neural network. However, this approach suffers from learning the knowledge by a CE loss as a KD loss often more strongly influences the objective function when they are in a competitive situation within a single network. This could be a critical problem particularly in a class incremental scenario, where the knowledge across tasks as well as within the new task, both of which can only be acquired by a CE loss, is essentially learned due to the existence of a unified classifier. In this paper, we propose a novel continual learning method, called Split-and-Bridge, which can successfully address the above problem by partially splitting a neural network into two partitions for training the new task separated from the old task and re-connecting them for learning the knowledge across tasks. In our thorough experimental analysis, our Split-and-Bridge method outperforms the state-of-the-art competitors in KD-based continual learning.
Self-Paced Two-dimensional PCA
Authors: Li Jiangxin , Kang Zhao , Peng Chong , Chen Wenyu
Published: September 2022

Two-dimensional PCA (2DPCA) is an effective approach to reduce dimension and extract features in the image domain. Most recently developed techniques use different error measures to improve their robustness to outliers. When certain data points are overly contaminated, the existing methods are frequently incapable of filtering out and eliminating the excessively polluted ones. Moreover, natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. Unlike previous studies, we explicitly differentiate the samples to alleviate the impact of outliers and propose a novel method called Self-Paced 2DPCA (SP2DPCA)algorithm, which progresses from `easy’ to `complex’ samples. By using an alternative optimization strategy, SP2DPCA looks for optimal projection matrix and filters out outliers iteratively. Theoretical analysis demonstrates the robustness nature of our method. Extensive experiments on image reconstruction and clustering verify the superiority of our approach.
MLA Guide to Digital Literacy, 2nd Ed.
Authors: Carillo Ellen C.
Published: September 2022

The second edition of this best-selling classroom guide helps students understand why digital literacy is a crucial skill for their education, future careers, and participation in democracy. Offering practical guidance for assessing information online, this guide provides students with the tools to locate reliable sources among the clickbait and viral videos that pervade the web. The guide's hands-on activities, germane readings, and lesson plans give students strategies for reading and analyzing data visualizations; finding and evaluating credible sources; learning how to spot fake news; fact-checking; crafting a research question; effectively conducting searches on Google and on library catalogs and databases; finding peer-reviewed publications; evaluating primary sources; and understanding disinformation and misinformation, filter bubbles, propaganda, and satire in a variety of sources—including websites, social media posts, infographics, videos, and more (on platforms like Facebook, Twitter, Instagram, TikTok, and YouTube).
USING A 3D MODELLING TO CALCULATE THE VOLUMES OF OVERBURDEN ROCKS AND MINERALS
Authors:   Perm State University, Alvanyan A. K. , Ovsyannikov M. V. ,   UralGeoProekt LLC
Published: September 2022

The volumes of overburden rocks and minerals were calculated based on the 3D model of the deposit, which was created using results of analysis of the geological structure of the deposit, and construction of the topographic surface. A comparative table of the results of calculating the volumes of overburden rocks and minerals was compiled using the method of geological blocks and 3D models
PROSPECTS FOR SEARCHING THE NON-ANTICLINE HYDROCARBON TRAPS IN DEVONIAN TERRIGENOUS DEPOSITS IN THE NORTHERN REGIONS OF THE VOLGA-URALS
Authors:   JSC Kama Research Institute for Integrated Research of Deep and Superdeep Wells, Sosnin N. E. , Popov V. M. ,   JSC Kama Research Institute for Integrated Research of Deep and Superdeep Wells, Makarova S. V. ,   JSC Kama Research Institute for Integrated Research of Deep and Superdeep Wells, Goryushkina N. S. ,   JSC Kama Research Institute for Integrated Research of Deep and Superdeep Wells, Peretyagina L. A. ,   JSC Kama Research Institute for Integrated Research of Deep and Superdeep Wells
Published: September 2022

The northern regions of the Volga-Ural oil and gas province have not yet exhausted the potential in gaining in oil and gas reserves due to structural deposits. However, there is a need to search for non-anticline traps. Devonian terrigenous deposits are very promising for the detection of non-anticline hydrocarbon traps here. The typification of non-anticlinal traps was carried out. According to the typification, carried out on the basis of geological and geophysical criteria, six zones of distribution of non-anticlinal traps that are promising for the detection of industrial accumulations of hydrocarbons are identified
HYDROGEOLOGICAL ASPECTS OF THE DEVELOPMENT OF WATER-RELATED TECHNOLOGIES OF URBANIZED TERRITORIES ON THE EXAMPLE OF ORENBURG REGION
Authors:   Orenburg State University, Gaev A. Ya. , Kudelina I. V. ,   Orenburg State University, Alferov I. N. ,   Institute of Economics, Ural Branch of the Russian Academy of Sciences, Leonteva T. V. ,   Orenburg State University
Published: September 2022

In the conditions of increasing industrial load especially in combination with arid climate, negative geodynamic processes develop on Earth, including depletion and pollution of water resources. More than 1.5 billion people suffer from the lack of water resources and the processes of pollution of natural waters. For more than two thousand years, people on the planet have been solving water management problems by building water reservoirs and the simplest water management structures. However, in arid climate, the construction of reservoirs is accompanied by the large losses of water due to evaporation. To overcome these difficulties, water management technologies are being developed. They are aimed in increasing the water reserves of water intakes based on the accumulation of a part of the floodwater
GEOLOGICAL AND INDUSTRIAL TYPES OF DEPOSITS. TITANMAGNETITE-ILMENITE TYPE
Authors:   Perm State University, Iblaminov R. G.
Published: September 2022

The identification of geological and industrial types of mineral deposits has an important theoretical and applied significance for their prospecting, resource evaluation and development. This problem has transformed into an independent branch of mineral science. It is shown by the example of the titanmagnetite-ilmenite type of deposits in stratified intrusions, where the main useful elements are the triad Fe, Ti and V. The geochemical, mineralogical, minerogenic, structural, and material aspects of this type are considered. The Kusinskoye field, studied in detail during 30 years of mining, is taken as a base. Based on the analysis of the structure and zonal changes of the material composition, its formation in a single magmatic chamber during crystallization differentiation is proved. Prospects for the development of similar deposits in the world are noted.
DIAGNOSTIC ASSESSMENT OF POLLUTED WATER LEAKS FROM THE SITE OF A CHEMICAL PRODUCTION WASTE LANDFILL USING THE SELF-POTENTIAL METHOD
Authors:   Francisk Skorina Gomel State University, Gusev A. P. , Molyarenko V. L. ,   Francisk Skorina Gomel State University, Pavlovsky A. I. ,   Francisk Skorina Gomel State University, Prilutsky I. O. ,   Francisk Skorina Gomel State University, Andrushko S. V. ,   Francisk Skorina Gomel State University
Published: September 2022

The main goal of the research is a diagnostic assessment of polluted water leaks from the chemical waste landfill site using the self-potential method integrated with other methods. The following tasks were to be solved: locating zones of contaminated water leakage from the drainage canals; detecting polluted water leaks from the sludge reservoir; identifying the discharge zones of contaminated groundwater into the swamps adjacent to the landfill. Electrical prospecting equipment ERA-MAX with non-polarizable electrodes of the VIRG system was used for field measurements. Two measuring techniques of the self-potential method were used: the fixed-base method (the measurement interval was 5 m) and the potential gradient method (measuring line MN = 5 m). The additional methods were as follows: geomagnetic microsurvey and phytoindication. The following diagnostic indicators were estab-lished for the zones of infiltration of contaminated waters from canals and sludge reservoirs: negative fixed-base potential anomalies or negative potential gradient anomalies of the natural electric field, which are characterized by the amplitude of 10-40 mV. The area of discharge of polluted groundwater into the surficial unit is indicated by a positive anomaly of the natural electric field (+ 20-47 mV), spatially associated with the zone of degradation of swamp vegetation
RIVERBED PROCESSES AND STABILITY OF THE NEMAN RIVER COURSE (WITHIN THE REPUBLIC OF BELARUS)
Authors:   Gomel State University F. Skarina, Melezh T. A.
Published: September 2022

The article provides an assessment of the stability of the Neman riverbed. Author has developed a classification of the Neman River course based on the evaluation of its stability and coastal erosion risk. It has been established that presently the nature of riverbed processes in the Neman Valley is heterogeneous in various sections and even in small segments. On base of obtained results, the author has identified the following segments: Stolbtsovskiy, Lyubchanskiy, Mostovskiy, Grodno, and Sredne-Neman.
History Education during COVID-19: Reflections from Makerere University, Uganda
Authors: Sebbowa D
Published: September 2022

The COVID-19 pandemic forced most governments in Africa to temporarily close educational institutions in attempt to reduce the spread of the pandemic. In Uganda particularly, Higher Education Institutions, Universities and schools adopted the online and blended approaches to afford continuity of learning during the lockdown. This article provides a reflection of the opportunities, challenges and lessons learnt in teaching and learning of history during the COVID-19 pandemic. Qualitative data was obtained from a narrative inquiry of the researcher's own teaching experience and interviews with pre-service history teachers from Makerere University. Findings indicated that, while online and blended approaches facilitate history education through Makerere University e-Learning (MUELE) Learning Management System, WhatsApp exchanges, Zoom, emails, mobile phone text messaging and print media; there were persistent challenges such as limited Information Communication Technology (ICT) tools, digital illiteracy, digital divide, increased workloads as well as social-emotional stress and distractions at home. The article concludes with a key lesson for Teacher Education programmes to shift the way they train pre-service history teachers to embrace online learning with access to offline, downloadable, print learning materials to facilitate blended learning approaches. This is relevant in preparation of different generations of teachers to integrate blended pedagogy in History Education in response to the new normal caused by the COVID-19 pandemic.
Practical history lessons as a tool for generating procedural knowledge in history teaching
Authors: Oppong C , Adjepong A , Boadu G
Published: September 2022

Situated in the context of philosophy of history, this article explains the use of substantive concepts and procedural concepts to generate historical understanding and examines the relationship between the two forms of historical knowledge. The paper makes use of both primary (original views of authors) and secondary (views of other authors) materials. The paper notes that substantive knowledge and procedural knowledge play complementary roles in the acquisition of historical understanding. It is argued, in light of the dominant position of substantive knowledge over procedural knowledge, that attention should be given to procedural knowledge as it introduces students to the processes by which history is constructed. The article proposes the use of practical history lessons as a conduit for developing procedural knowledge and attaining historical understanding.Situated in the context of philosophy of history, this article explains the use of substantive concepts and procedural concepts to generate historical understanding and examines the relationship between the two forms of historical knowledge. The paper makes use of both primary (original views of authors) and secondary (views of other authors) materials. The paper notes that substantive knowledge and procedural knowledge play complementary roles in the acquisition of historical understanding. It is argued, in light of the dominant position of substantive knowledge over procedural knowledge, that attention should be given to procedural knowledge as it introduces students to the processes by which history is constructed. The article proposes the use of practical history lessons as a conduit for developing procedural knowledge and attaining historical understanding.
Processing PBMCs for multiplexed scRNA-seq v1
Authors: Xie Ning
Published: September 2022

Purpose: to prepare viable single cells from frozen PBMCs for scRNA-seq, multiplexing strategy is applied to reduce batch effect and cost.
城市微生物菌相調查計畫 — 採集土壤教學 v1
Authors: Wu Hsin-Mao
Published: September 2022

城市微生物菌相調查計畫 — 採集土壤教學
S-Trap™ plate digestion protocol (Protifi) of proteins for LC-MS / proteomics v1
Authors: not provided ronan.ocualain
Published: September 2022

This protocol details the in-house BioMS procedure of S-Trap™ 96-well plate protein clean-up and digestion. It is adapted from the long protocol from Protifi (as on August 2022) - https://files.protifi.com/protocols/s-trap-96-well-plate-long-1-4.pdf
RT-qPCR for detection of SARS-CoV-2 in wastewater v2
Authors: Findlay David
Published: September 2022

As part of the global response to the 2019 novel Coronavirus (SARS-CoV-2) pandemic, it was determined that SARS-CoV-2 RNA was detectable in the faeces of both symptomatic and asymptomatic patients (1). Further analysis demonstrated that a wastewater epidemiological (WWE) approach, similar to that used to track other viruses (i.e. Poliovirus), could be employed to monitor the spread of SARS-CoV-2. The presence of, or changes in concentration of viral RNA within the wastewater network can assist in monitoring the emergence of further viral peaks (2). Thus, monitoring the spread of Covid-19 using the WWE approach has been extensively explored in several countries (3). Viral RNA is extracted from a known volume of sewage treatment works influent (spiked with a know concentration of Porcine Reproductive and Respiratory Syndrome Virus, PRRSv, to act as an internal control) and amplified by Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR). RT-qPCR is a process whereby RNA is first converted to complementary DNA (cDNA) after which it can undergo amplification and quantification via PCR. The amplification of a non-target sequence, in this case PRRSv, can also be carried out to act as an internal control. This procedure, developed by Scottish Environment Protection Agency (SEPA) based on work in the Corbishley lab, Roslin Institute of the University of Edinburgh, outlines the method for amplification and quantification of the RNA extracted from crude sewage samples, but could equally apply to other RNA extracted from other types of environmental sample. For viral RNA extraction from wastewater, please refer to "RNA extraction from wastewater for detection of SARS-CoV-2" by the same authors of this protocol on protocols.io (dx.doi.org/10.17504/protocols.io.bzv5p686). For the data normalisation process to report the data as RNA copies per person, please see the protocol "Data normalisation of RT-qPCR data for detection of SARS-CoV-2 in wastewater" (dx.doi.org/10.17504/protocols.io.b4eqqtdw) References: 1. Jones, D. L., Baluja, M. Q., Graham, D. W., Corbishley, A., McDonald, J. E., Malham, S. K., Hillary, L. S., Connor, T. R., Gaze , W. H., Moura , I. B., Wilcox, M. H., & Farkas , K. (2020).Shedding of SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the environment-based spread of COVID-19.Science of the Total Environment.https://doi.org/10.1016/j.scitotenv.2020.1413644. 2. Fitzgerald, S., Rossi, G., Low, A., McAteer, S., O’Keefe, B., Findlay, D., Cameron, G. J., Pollard, P., Singleton, P. T. R., Ponton, G., Singer, A. C., Farkas, K., Jones, D., Graham, D. W., Quintela-Baluja, M., Tait-Burkard, C., Gally, D., Kao, R., & Corbishley, A.(2021).Site specific relationships between COVID-19 cases and SARS-CoV-2 viral load in wastewater treatment plant influent. Environmental Science and Technology. https://doi.org/10.1021/acs.est.1c05029 3. Wastewater SARS Public Health Environmental Response (W-SPHERE). https://sphere.waterpathogens.org/about
Hardware Implementation of Code Converters Designed to Reduce the Length of Binary Encoded Words
Authors:   The United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk, 220012, Belarus, Bibilo P. N.
Published: September 2022

The problem of synthesis of combinational circuits of code converters designed to reduce the length of words from a given set of encoded binary words is considered. The encoding assumes that different binary words will be encoded by different binary codes of shorter length. Code converters of this type are designed to reduce the length of binary words transmitted in digital systems over data buses when the bit depth of the transmitted words exceeds the bit depth of the data bus. For example, 18-bit or 17-bit words need to be transmitted over a 16-bit data bus. Each such word can be transmitted in two cycles of operation of a digital system, however, this approach reduces the overall performance of the system. One of the approaches to solve such problems is the development of combinational circuits that convert long binary encoded words into shorter ones. The proposed methods for solving the problem of synthesizing circuits of code converters are based on the compilation and logical minimization of such forms of systems of incompletely specified Boolean functions as disjunc­tive normal forms (DNF) and binary decision diagrams (BDD). Using BDD to minimize representations of k-valued functions that depend on Boolean variables is also proposed. Technologically independent logical minimization of functional descriptions of the designed code converters is proposed to be performed by programs for minimizing systems of Boolean functions in the DNF class and programs for joint minimization of BDD representations of systems of completely specified Boolean functions. Minimization of functional descriptions is aimed at reducing the hardware complexity of combinational circuits in the basis of library elements or programmable FPGA elements implementing code converters of the class in question.
Static Analysis of the Source Code of Python Applications
Authors:   Lugansk State Pedagogical University, Lugansk, 91011, Lugansk Peoples Republic, Kapustin D. A. , Shvyrov V. V. ,   Lugansk State Pedagogical University, Lugansk, 91011, Lugansk Peoples Republic, Shulika T. I. ,   Lugansk State Pedagogical University, Lugansk, 91011, Lugansk Peoples Republic
Published: September 2022

One of the popular methods of software code analysis is the static analysis method. This method allows not only to check the code for compliance with the language specification, but also to find potential vulnerabilities. The work performs a static analysis of a corpus of open source Python application. Using the Bandit library, statistical indicators of various categories of potential vulnerabilities are found, a rating table of vulnerabilities found in the studied data set is built. A qualitative analysis of threats is carried out according to their danger based on the CWE catalog data. The purpose of this work is to analyze a corpus of open source Python listings for potential vulnerabilities using a static analyzer and rank threats based on the potential vulnerabilities found. Thus, the study should answer the following questions: Q1. What categories of potential vulnerabilities in the CWE catalog are most common in Python open source projects? Q2. What categories of tests (checks) of the Bandit library found the most vulnerabilities? Q3. Is there a correlation between the obtained statistical data and the OWASP rating data?
Modeling of blood cell surface oscillations as fluid-filled multilayer viscoelastic shells
Authors:   Kharkov National Medical University, Batyuk L. V. , Kizilova Natalya ,   V.N. Karazin Kharkov National University
Published: September 2022

Rheological properties of the red blood cells (RBC) determine their movement in the larger and smaller blood vessels, oxygen and carbon dioxide delivery to/from the cells. Those properties vary significantly with age and health state of an organism. In this paper a new rheological model of RBC as a thin multilayer shell, which includes the cytoskeleton, lipid bilayer, glycocalyx, and hydrate shell as Maxwell's viscoelastic bodies is proposed. Mechanical properties of the rheological model in isotonic, isometric and dynamic experiments are studied. The oscillations of the surfaces of erythrocytes or other cells in the approximation of multilayer viscoelastic shell filled with a viscous fluid are investigated. The expressions for the dynamic Young’s modules and viscosity/fluidity coefficients as functions of the viscoelastic and geometric parameters of the layers are obtained. The problem of propagation of small perturbations along the cell surface is considered. The solutions of the problem in the form of Young and Lamé waves are obtained. The method of identification of the erythrocyte parameters from the experimental measurements of the wave propagation on the basis of the developed mathematical model for the purposes of clinical diagnostics of diseases with use of a microdrop of blood of the patient is proposed.
Taşkın Yayılım Haritalarında Arazi Kullanım Türü Ve Yüzeysel Akış Etkilerinin Değerlendirilmesi: Malatya İli Örneği
Authors: ÇIRAĞ Burak , FIRAT Mahmut
Published: September 2022

Dünya genelinde kentleşme artmaktadır ve buna bağlı olarak kentsel nüfus da artmaktadır. Hızlı kentsel nüfus artışının sonucu olarak yetersiz drenaj sistemleri nedeniyle taşkın yağışı durumunda, binaların bodrum katlarını su basması, ulaşım yollarının kapanması gibi can ve mal kaybına neden olabilecek hasarlar meydana gelmektedir. Bu çalışmada, Malatya ili için arazi kullanım türü ve sızma durumlarına bağlı üç farklı senaryo kapsamında taşkın yayılım haritaları oluşturulmuştur. Bu senaryolar, modelde; arazi kullanım türünün olmadığı, arazi kullanım türünün olduğu ve arazi kullanım türü ile yüzeysel akış değerlerinin birlikte olduğu durumuna dayanmaktadır. Çalışmanın, hidrolojik veya hidrodinamik modellerde arazi kullanımı ve yüzeysel akış verilerinin kullanımı hakkında detaylar sunması amaçlanmıştır. Bu kapsamda, InfoWorks ICM yazılımı kullanılarak oluşturulan modeller, model doğruluğunu arttırması için sayısal yükseklik modeli, bina konum verileri, arazi kullanım türü ve gelecekte meydana gelebilecek yağış yükseklikleri verileriyle desteklenmiştir. Sonuçlar, arazi kullanım türü ile yüzeysel akış değerlerinin birlikte kullanıldığı modele dayalı senaryo da diğer iki senaryoya göre havza genelinde daha az akış kollarının oluştuğunu göstermiştir.
ŞAPHANE (ÇORUM) DAMAR TİPİ ALTIN CEVHERLEŞMESİNDE MULTİSPEKTRAL UYDU GÖRÜNTÜLERİ KULLANILARAK HİDROTERMAL ALTERASYON MİNERAL HARİTALAMASI VE ÇİZGİSELLİK ANALİZİ
Authors: CANBAZ Oktay , ÇAKIR Esra Ünal
Published: September 2022

Uzaktan algılama teknikleri litolojik özelliklerin ve minerallerin haritalanmasında, yapısal özelliklerin ortaya çıkarılmasında oldukça kullanışlı veriler sunmaktadır. Özellikle metalik madenlerin ilk arama aşamalarından bir tanesi olan hidrotermal alterasyon minerallerinin dağılımlarının daha az maliyetle ve kısa sürede haritalanması oldukça önemli sonuçlar ortaya çıkarmaktadır. Çalışma alanında, Eosen volkanizmasına ait ürünler ile hidrotermal çözelti faaliyetlerinin neden olduğu alterasyonlar yoğun olarak gözlenmektedir. Bu yoğun alterasyonlar, yaklaşık KD-GB doğrultulu altın içeren hidrotermal damarlarla ilişkilendirilmektedirler. Altın potansiyeli yüksek olan bu saha sarp topografya ve ulaşılması güç yüzleklere sahiptir. Bölgede arazi çalışmaları güçlükle gerçekleştirilmekte olup çalışmalar sırasında birçok alan gözden kaçabilmektedir. Bu çalışma, ASTER uydu verisinde bitki örtüsü maskeleme (NDVI), bant oranlama gibi görüntü zenginleştirme teknikleri ile spektral sınıflandırma yöntemi uygulanarak hidrotermal alterasyon minerallerinin haritalanmasına yönelik gerçekleştirilmiştir. Ayrıca, damar tipi cevherleşmelerin tespitine yönelik ise Landsat-8 OLI uydu verisinden elde edilen 15 m çözünürlüğe sahip görüntüde otomatik çizgisellik analizi uygulanmıştır. Elde edilen sonuçlar, 84,8 genel doğruluk ve 0,836 kappa katsayısına sahip olup arazi verileri ile yüksek oranda doğruluk göstermektedir. Çalışmada elde edilen haritaların bölgedeki yeni altın ve/veya diğer değerli metalik cevherleşmeler için kullanışlı ve öncü veriler sunacağı düşünülmektedir.
DİZEL MOTORLARDA DİMETİL ETER KULLANIMININ NOx EMİSYONLARINA ETKİLERİ ÜZERİNE BİR DERLEME ÇALIŞMASI
Authors: SEZER İsmet
Published: September 2022

Bu çalışma, dizel motorlarda dimetil eter (DME)’in saf veya yakıt katkısı olarak kullanımı üzerine yapılmış çeşitli çalışmaların sonuçlarından yararlanılarak derlenmiştir. Dizel motorlarda zararlı egzoz emisyonlarını azaltmanın birkaç yöntemi vardır. Bunlardan ilki motor tasarımında ve yakıt enjeksiyon sisteminde modifikasyonlar yaparak yanmanın iyileştirilmesidir, ancak bu pahalı ve zaman alıcı bir yöntemdir. İkinci yöntem ise katalitik konvektör ve partikül fitresi gibi donanımlar kullanmaktır, ancak bu donanımlar motor performansını olumsuz yönde etkiler. Hem egzoz emisyonlarını azaltmak hem de motor performansını artırmak için uygulanan son yöntem çeşitli alternatif yakıtların veya yakıt katkılarının kullanılmasıdır. Dizel motorlardaki en önemli emisyonlar azot oksitler (NOx) ve partikül maddelerdir (PM). Çoğu araştırmacı emisyonları azaltmanın en iyi yolunun doğalgaz, biyogaz, biyodizel gibi alternatif yakıtların veya konvansiyonel veya alternatif yakıtlarla birlikte çeşitli yakıt katkılarının kullanılması olduğunu bildirmektedir. Bu nedenle, alternatif yakıtlar ve yakıt katkıları üzerine yapılan çalışmaların sonuçlarının birlikte değerlendirilmesi pratik uygulamalar için oldukça önemlidir. Bu çalışma, dimetil eterin dizel motorlarda yakıt veya yakıt katkısı olarak kullanılmasının azot oksit (NOx) emisyonları üzerindeki etkilerinin incelenmesine odaklanmıştır.