bluuebunny/crossref_metadata_2025 huggingface dataset:
Sample-Specific Output Constraints for Neural Networks
Authors: Brosowsky Mathis , Keck Florian , Dünkel Olaf , Zöllner Marius
Published: September 2022

It is common practice to constrain the output space of a neural network with the final layer to a problem-specific value range. However, for many tasks it is desired to restrict the output space for each input independently to a different subdomain with a non-trivial geometry, e.g. in safety-critical applications, to exclude hazardous outputs sample-wise. We propose ConstraintNet—a scalable neural network architecture which constrains the output space in each forward pass independently. Contrary to prior approaches, which perform a projection in the final layer, ConstraintNet applies an input-dependent parametrization of the constrained output space. Thereby, the complete interior of the constrained region is covered and computational costs are reduced significantly. For constraints in form of convex polytopes, we leverage the vertex representation to specify the parametrization. The second modification consists of adding an auxiliary input in form of a tensor description of the constraint to enable the handling of multiple constraints for the same sample. Finally, ConstraintNet is end-to-end trainable with almost no overhead in the forward and backward pass. We demonstrate ConstraintNet on two regression tasks: First, we modify a CNN and construct several constraints for facial landmark detection tasks. Second, we demonstrate the application to a follow object controller for vehicles and accomplish safe reinforcement learning in this case. In both experiments, ConstraintNet improves performance and we conclude that our approach is promising for applying neural networks in safety-critical environments.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Authors: Bui Anh Tuan , Le Trung , Zhao He , Montague Paul , DeVel Olivier , Abraham Tamas , Phung Dinh
Published: September 2022

Ensemble-based Adversarial Training is a principled approach to achieve robustness against adversarial attacks. An important technicality of this approach is to control the transferability of adversarial examples between ensemble members. We propose in this work a simple, but effective strategy to collaborate among committee models of an ensemble model. This is achieved via the secure and insecure sets defined for each model member on a given sample, hence help us to quantify and regularize the transferability. Consequently, our proposed framework provides the flexibility to reduce the adversarial transferability as well as promote the diversity of ensemble members, which are two crucial factors for better robustness in our ensemble approach. We conduct extensive and comprehensive experiments to demonstrate that our proposed method outperforms the state-of-the-art ensemble baselines, at the same time can detect a wide range of adversarial examples with a near perfect accuracy.
Frivolous Units: Wider Networks Are Not Really That Wide
Authors: Casper Stephen , Boix Xavier , D'Amario Vanessa , Guo Ling , Schrimpf Martin , Vinken Kasper , Kreiman Gabriel
Published: September 2022

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity? If so, how do these depend on the architecture, dataset, and hyperparameters? We identify two distinct types of “frivolous” units that proliferate when the network’s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network computes could be expressed without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.
Addressing Action Oscillations through Learning Policy Inertia
Authors: Chen Chen , Tang Hongyao , Hao Jianye , Liu Wulong , Meng Zhaopeng
Published: September 2022

Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective on a wide range of challenging decision making and control tasks. However, these methods typically suffer from severe action oscillations in particular in discrete action setting, which means that agents select different actions within consecutive steps even though states only slightly differ. This issue is often neglected since we usually evaluate the quality of a policy using cumulative rewards only. Action oscillation strongly affects the user experience and even causes serious potential security menace especially in real-world domains with the main concern of safety, such as autonomous driving. In this paper, we introduce Policy Inertia Controller (PIC) which serves as a generic plug-in framework to off-the-shelf DRL algorithms, to enable adaptive balance between the optimality and smoothness in a formal way. We propose Nested Policy Iteration as a general training algorithm for PIC-augmented policy which ensures monotonically non-decreasing updates.Further, we derive a practical DRL algorithm, namely Nested Soft Actor-Critic. Experiments on a collection of autonomous driving tasks and several Atari games suggest that our approach demonstrates substantial oscillation reduction than a range of commonly adopted baselines with almost no performance degradation.
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
Authors: Chen Xin , Xie Lingxi , Wu Jun , Wei Longhui , Xu Yuhui , Tian Qi
Published: September 2022

Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. These methods enjoy great advantages in terms of computational costs, but the sampled sub-networks are not guaranteed to be estimated precisely unless an individual training process is taken. This paper owes such inaccuracy to the inevitable mismatch between assembled network layers, so that there is a random error term added to each estimation. We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates, which consequently leads to better performance of the final architecture. In addition, our approach also enjoys the flexibility of being used under different hardware constraints, since the graph convolutional network has provided an efficient lookup table of the performance of architectures in the entire search space.
Differentially Private Stochastic Coordinate Descent
Authors: Damaskinos Georgios , Mendler-Dünner Celestine , Guerraoui Rachid , Papandreou Nikolaos , Parnell Thomas
Published: September 2022

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private.
Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this vector suffices to hide critical information about individuals, stochastic coordinate descent crucially relies on keeping auxiliary information in memory during training.
This auxiliary information provides an additional privacy leak and poses the major challenge addressed in this work.
Driven by the insight that under independent noise addition, the consistency of the auxiliary information holds in expectation, we present DP-SCD, the first differentially private stochastic coordinate descent algorithm. We analyze our new method theoretically and argue that decoupling and parallelizing coordinate updates is essential for its utility. On the empirical side we demonstrate competitive performance against the popular stochastic gradient descent alternative (DP-SGD) while requiring significantly less tuning.
Knowledge Refinery: Learning from Decoupled Label
Authors: Ding Qianggang , Wu Sifan , Dai Tao , Sun Hao , Guo Jiadong , Fu Zhang-Hua , Xia Shutao
Published: September 2022

Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels while neglecting the relation of classes. Among them, the technique of knowledge distillation proposes to distill the soft label, which contains the knowledge of class relations. However, this technique needs to pre-train an extra cumbersome teacher model. In this paper, we propose a method called Knowledge Refinery (KR), which enables the neural network to learn the relation of classes on-the-fly without the teacher-student training strategy. We propose the definition of decoupled labels, which consist of the original hard label and the residual label. To exhibit the generalization of KR, we evaluate our method in both fields of computer vision and natural language processing. Our empirical results show consistent performance gains under all experimental settings.
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Authors: Fu Haotian , Tang Hongyao , Hao Jianye , Chen Chen , Feng Xidong , Li Dong , Liu Wulong
Published: September 2022

Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Meta-RL policies can easily generalize to new tasks within a few adaptation steps. We argue that improving the quality of context involves answering two questions: 1. How to train a compact and sufficient encoder that can embed the task-specific information contained in prior trajectories? 2. How to collect informative trajectories of which the corresponding context reflects the specification of tasks? To this end, we propose a novel Meta-RL framework called CCM (Contrastive learning augmented Context-based Meta-RL). We first focus on the contrastive nature behind different tasks and leverage it to train a compact and sufficient context encoder. Further, we train a separate exploration policy and theoretically derive a new information-gain-based objective which aims to collect informative trajectories in a few steps. Empirically, we evaluate our approaches on common benchmarks as well as several complex sparse-reward environments. The experimental results show that CCM outperforms state-of-the-art algorithms by addressing previously mentioned problems respectively.
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
Authors: Hertrich Christoph , Skutella Martin
Published: September 2022

The development of a satisfying and rigorous mathematical understanding of the performance of neural networks is a major challenge in artificial intelligence. Against this background, we study the expressive power of neural networks through the example of the classical NP-hard Knapsack Problem. Our main contribution is a class of recurrent neural networks (RNNs) with rectified linear units that are iteratively applied to each item of a Knapsack instance and thereby compute optimal or provably good solution values. We show that an RNN of depth four and width depending quadratically on the profit of an optimum Knapsack solution is sufficient to find optimum Knapsack solutions. We also prove the following tradeoff between the size of an RNN and the quality of the computed Knapsack solution: for Knapsack instances consisting of n items, an RNN of depth five and width w computes a solution of value at least 1 - O(n^2 sqrt(w)) times the optimum solution value. Our results build upon a classical dynamic programming formulation of the Knapsack Problem as well as a careful rounding of profit values that are also at the core of the well-known fully polynomial-time approximation scheme for the Knapsack Problem. Finally, we point out that our results can be generalized to many other combinatorial optimization problems that admit dynamic programming solution methods, such as various Shortest Path Problems, the Longest Common Subsequence Problem, and the Traveling Salesperson Problem.
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Authors: Hu Peng , Peng Xi , Zhu Hongyuan , Aly Mohamed M. Sabry , Lin Jie
Published: September 2022

As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e.g., pruning sparsity and quantization codebook) of each layer. Existing solutions obtain the compression allocation in an iterative/manual fashion while finetuning the compressed model, thus suffering from the efficiency issue. Different from the prior art, we propose a novel One-shot Pruning-Quantization (OPQ) in this paper, which analytically solves the compression allocation with pre-trained weight parameters only. During finetuning, the compression module is fixed and only weight parameters are updated. To our knowledge, OPQ is the first work that reveals pre-trained model is sufficient for solving pruning and quantization simultaneously, without any complex iterative/manual optimization at the finetuning stage. Furthermore, we propose a unified channel-wise quantization method that enforces all channels of each layer to share a common codebook, which leads to low bit-rate allocation without introducing extra overhead brought by traditional channel-wise quantization. Comprehensive experiments on ImageNet with AlexNet/MobileNet-V1/ResNet-50 show that our method improves accuracy and training efficiency while obtains significantly higher compression rates compared to the state-of-the-art.
Multi-scale Graph Fusion for Co-saliency Detection
Authors: Hu Rongyao , Deng Zhenyun , Zhu Xiaofeng
Published: September 2022

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In
this paper, we propose a new co-saliency detection framework
which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment
each image to semantic superpixel clusters as well as generate
different scales/sizes of images for each input image by the
VGG-16 model. Different scales capture different patterns of
the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features,
aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed
GCN method jointly conducts multi-scale feature fine-tune,
graph learning, and feature learning in a unified framework.
We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all
comparison methods in terms of different evaluation metrics.
Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization
Authors: Huang Han-Hsien , Yeh Mi-Yen
Published: September 2022

In this paper, we propose an approach to effectively accelerating the computation of continuous normalizing flow (CNF), which has been proven to be a powerful tool for the tasks such as variational inference and density estimation. The training time cost of CNF can be extremely high because the required number of function evaluations (NFE) for solving corresponding ordinary differential equations (ODE) is very large. We think that the high NFE results from large truncation errors of solving ODEs. To address the problem, we propose to add a regularization. The regularization penalizes the difference between the trajectory of the ODE and its fitted polynomial regression. The trajectory of ODE will approximate a polynomial function, and thus the truncation error will be smaller. Furthermore, we provide two proofs and claim that the additional regularization does not harm training quality. Experimental results show that our proposed method can result in 42.3% to 71.3% reduction of NFE on the task of density estimation, and 19.3% to 32.1% reduction of NFE on variational auto-encoder, while the testing losses are not affected.
Balanced Open Set Domain Adaptation via Centroid Alignment
Authors: Jing Mengmeng , Li Jingjing , Zhu Lei , Ding Zhengming , Lu Ke , Yang Yang
Published: September 2022

Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain.
Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods.
To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples.
Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space.
In this space, we propose to bound the centroid deviation angles to not only increase the intra-class compactness but also enlarge the inter-class margins.
With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes.
In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment.
Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints
Authors: Kalagarla Krishna C. , Jain Rahul , Nuzzo Pierluigi
Published: September 2022

Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages the linear programming formulation of repeated optimistic planning for finite-horizon CMDP to provide a probably approximately correctness (PAC) guarantee on the number of episodes needed to ensure a near optimal policy, i.e., with resulting objective value close to that of the optimal value and satisfying the constraints within low tolerance, with high probability. The number of episodes needed is shown to have linear dependence on the sizes of the state and action spaces and quadratic dependence on the time horizon and an upper bound on the number of possible successor states for a state-action pair. Therefore, if the upper bound on the number of possible successor states is much smaller than the size of the state space, the number of needed episodes becomes linear in the sizes of the state and action spaces and quadratic in the time horizon.
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
Authors: Karia Rushang , Srivastava Siddharth
Published: September 2022

Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded symbolic action models and hand-coded heuristic function generators for efficiency. Learned heuristics for such problems have been of limited utility as they are difficult to apply to problems with objects and object quantities that are significantly different from those in the training data. This paper develops a new approach for learning generalized heuristics in the absence of symbolic action models using deep neural networks that utilize an input predicate vocabulary but are agnostic to object names and quantities. It uses an abstract state representation to facilitate data-efficient, generalizable learning. Empirical evaluation on a range of benchmark domains shows that in contrast to prior approaches, generalized heuristics computed by this method can be transferred easily to problems with different objects and with object quantities much larger than those in the training data.
MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
Authors: Kuznetsov Maksim , Polykovskiy Daniil
Published: September 2022

We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by recursively splitting every node into two. All operations are invertible and can be used as plug-and-play modules. The hierarchical nature of the latent codes allows for precise changes in the resulting graph: perturbations in the first layer cause global structural changes, while perturbations in the consequent layers change the resulting molecule only marginally. Proposed model outperforms existing generative graph models on the distribution learning task. We also show successful experiments on global and constrained optimization of chemical properties using latent codes of the model.
GoT: a Growing Tree Model for Clustering Ensemble
Authors: Li Feijiang , Qian Yuhua , Wang Jieting
Published: September 2022

The clustering ensemble technique that integrates multiple clustering results can improve the accuracy and robustness of the final clustering. In many clustering ensemble algorithms, the co-association matrix (CA matrix), which reflects the frequency of any two samples being partitioned into the same cluster, plays an important role. However, generally, the CA matrix is highly sparse with low value density, which may limit the performance of an algorithm based on it. To handle these issues, in this paper, we propose a growing tree model (GoT). In this model, the CA matrix is firstly refined by the shortest path technique so that its sparsity will be mitigated. Then, a set of representative prototype examples is discovered. Finally, to handle the low value density of the CA matrix, the prototypes gradually connect to their neighborhood, which likes a set of trees growing up. The rationality of the discovered prototype examples is illustrated by theoretical analysis and experimental analysis. The working mechanism of the GoT is visually shown on synthetic data sets. Experimental analyses on eight UCI data sets and eight image data sets show that the GoT outperforms nine representative clustering ensemble algorithms.
A Bayesian Approach for Subset Selection in Contextual Bandits
Authors: Li Jialian , Du Chao , Zhu Jun
Published: September 2022

Subset selection in Contextual Bandits (CB) is an important task in various applications such as advertisement recommendation. In CB, arms are attached with contexts and thus correlated in the context space. Proper exploration for subset selection in CB should carefully consider the contexts. Previous works mainly concentrate on the best one arm identification in linear bandit problems, where the expected rewards are linearly dependent on the contexts. However, these methods highly rely on linearity, and cannot be easily extended to more general cases. We propose a novel Bayesian approach for subset selection in general CB where the reward functions can be nonlinear. Our method provides a principled way to employ contextual information and efficiently explore the arms. For cases with relatively smooth posteriors, we give theoretical results that are comparable to previous works. For general cases, we provide a calculable approximate variant. Empirical results show the effectiveness of our method on both linear bandits and general CB.
Exploración del sistema osteomuscular
Authors:   Universidad Cooperativa de Colombia, Mendivil de la Ossa José Alberto , Gómez Duque Lina María
Published: September 2022

Esta guía de práctica del curso en Semiología del Paciente Sano comprende los aspectos básicos de la semiotecnia y la interpretación de los hallazgos semiológicos del examen del sistema osteomuscular, siguiendo este orden: 1) evaluación de los movimientos activos y pasivos; 2) evaluación de la fuerza muscular, 3) evaluación de los signos de lesión a nivel articular, de tendones y ligamentos, y 4) evaluación de la marcha.
Psychological features of the accuracy of reproduction of a given samplin various psychoemotional states
Authors:   Smolensk State University, Polyakova Irina V.
Published: September 2022

The article is devoted to the study of psychological features of reproduction of a given sample by the right and left hand of the subject in different psycho-emotional states. The relevance of the work is due to the need to reduce the perceptual error when reproducing a sample in different learning conditions. To this end, the subjects were asked to reproduce the sample set by the experimenter in a state of calm wakefulness, in a stressful situation, and immediately after the stress experienced. The accuracy of reproduction of the given sample was measured using a specially designed device that allowed generating a general error within a given time. Fifteen students of the Smolensk State University took part in the experiment. The study found that the accuracy of the sample reproduction depends on the psycho-emotional state of a student. The state of calm wakefulness is optimal for accurate reproduction of a strain gauge sample. Psychological features of the transfer of tensometric skills of the hands are connected to the peculiarities of psychoemotional states and are not elementary operational actions. The pattern of the distribution of the reproduction accuracy results for the reproduction performed by the right and left hand of the subject in different psycho-emotional states turned out to be different. The accuracy of reproducing the sample with the right hand in a state of psychoemotional tension is lower than in a state of calm wakefulness. On the contrary, the error of reproducing the sample with the left hand in a state of psychoemotional tension is lower. The obtained results can be used in psychodiagnostics to clarify the features of experiencing the psychoemotional states by subjects.
Who needs this philosophy today? Part 1. Why there is some doubt about the need to teach philosophy in Russian universities
Authors:   Perm State University, Musayelyan Lyeva A.
Published: September 2022

Culture is a synthetic characteristic of the level of development of human essence, manifested in human activity and the results of this activity, which include philosophy. However, philosophy is not an «ordinary» component of culture, it is «the quintessence of human culture» (Marx). The complexity of understanding a person as a universal social being and the versatility of his activities are the factors that determine the discussions in the interpretation of the essence of philosophy, its tasks, functions, and role in public life. According to the well-known definition of Hegel, philosophy is an epoch expressed in thoughts. To know an era or a particular society, it is necessary to study and understand their philosophy since it is an indicator of the state the existing social reality is in. The historical process is an expression of the developing human essence, which is also manifested in the development of philosophy itself. However, this process is of a contradictory, heterogeneous character, with powerful upsurges alternating with crises, which are also felt in philosophy itself. On the other hand, the state of philosophy is not a simple mirror reflection of the nature of the processes taking place in social reality. Here, as history shows, there is a mutual conditioning. Philosophy can contribute to the deepening of crisis tendencies in public life, but it can also help bring society out of a deep crisis and even change the vector of its historical movement.
How uncanny is the «Uncanny valley»? Experience of deconstructing a discourse
Authors:   Perm National Research Polytechnic University, Stolbova Natalya V. , Seredkina Elena V. ,   Perm National Research Polytechnic University, Myshkin Oleg S.
Published: September 2022

After the Japanese scientist Masahiro Mori first described the phenomenon that he called the «Uncanny Valley», numerous attempts were made to explain it. However, the uncanny valley still remains a problem not only for people who encounter it in technical production (robotics engineers, programmers, and designers of anthropomorphic robots) but also for philosophy, in particular, for philosophical anthropology and ontology. This article attempts not so much to explain this phenomenon — this work primarily remains with psychologists, sociologists, and philosophers who study the human mentality — but demonstrate a program to increase this problem into the schemas of some contemporary ontologies. We will follow Timothy Morton who explores the uncanny valley in his books Hyperobjects: Philosophy and Ecology after the End of the World, Being Ecological, and in a number of articles. We will attempt to demonstrate the insufficiency of both the «classical» (Modern) approaches to the analysis of the technical and the tool analysis proposed by M. Heidegger in Being and Time. Then we will try to show that the problem of the uncanny valley is only an example of the ontological gap the foundation for which was laid by the Modern, and, therefore, its solution is only possible by diffusion it in broader problems of the philosophy of technology. The implementation of such «diffusion» both in the theoretical aspect of ontology and in the aspect of transformation of our practices — everyday, mental, and industrial — can become one of the cornerstones in the foundation of the future ecological coexistence of the human and non-human entities.
MONITORING STUDIES OF GEOLOGICAL PROCESSES OCCURRED AT THE LAKE MEAD AREA SINCE IMPOUNDMENT IN 1935 UNTIL BEGINNING OF XXI CENTURY
Authors:   Perm State University, Kovin O. N.
Published: September 2022

Reservoirs, constructed for flood control, irrigation purposes, electric power and drinking water supply, can trigger significant changes in the geological processes on their territory and surroundings, as well as in climate, biosphere, and environ. In the article presents the results of comprehensive monitoring studies of Lake Mead reservoir on the Colorado River, Nevada, conducted on the request of Bureau of Reclamation of the U.S. Department of the Interior. Lake Mead was created after construction of the Hoover Dam that is one of the first dams on the Colorado River. Experts from geology, geophysics, oceanography, hydrology, and ecology were involved in this study. In result of the comprehensive research, headed by USGS, data on the dynamics of bottom sediments were obtained and analyzed; geodetic measurements of subsidence and earthquakes registration were performed; the main contamination sources were revealed. Furthermore, analysis of collected data allowed estimating the reservoir life up to 400 years.
ABOUT SPECIFIC CHARACTER OF ARGILLACEOUS MINERALS IN SEDIMENTARY FORMATIONS
Authors:   West-Yakutian Scientific Centre of RS (Y) AS, Zinchuck N. N.
Published: September 2022

Argillaceous minerals in deposits of various sedimentary formations are characterized by specific chemical-mineralogical and morphological genetic features changing in different stages of sedimentogenesis, diagenesis, catagenesis, and metagenesis. Argillaceous minerals and their associations in terrigenous, terrigenous carbonate, carbonate, volcanic, and halogenic formations are characterized. Detail characterization is given for argillaceous minerals of early stages of sedimentogenesis and diagenesis (partially catagenesis), where the majority of ancient (Upper Paleozoic and Mesozoic) diamondiferous placers occur. Contained their the kimberlite indicator minerals are used for prospecting the primary deposits of diamonds by the heavy concentrate-mineralogical method. The importance of investigating for forecast-prospecting tasks the material composition of volcanogenic formationand specific features of its distribution during formation of different in age collectors of diamonds on specific diamondiferous regions of the Siberian platform is shown.
LITHOLOGICAL AND ENGINEERING-GEOLOGICAL CHARACTERISTICS OF THE ROCKS OF THE TOLBACHAN FORMATION WITHIN THE MIR PIPE FIELD
Authors:   Institute Yakutniproalmaz PJSC ALROSA, Yannikov A. M. , Yannikova S. A. ,   Institute Yakutniproalmaz PJSC ALROSA, Ovchinnikova M. Yu. ,   Institute Yakutniproalmaz PJSC ALROSA,   Voronezh State University, Korepanov A. Yu. ,   Institute Yakutniproalmaz PJSC ALROSA
Published: September 2022

The Tolbachan suite presents the complex mining and geological conditions for development the Mir primary diamond deposit at depths of 1000 to 1400 meters. The resumption of mining operations at the field requires a comprehensive study of host rocks, especially in the formation intervals of high-pressure reservoirs of mixed fluid saturation. The study of lithological features and determination of the main physical and mechanical parameters of the formation were carried out in 2020–2021 using standard methods based on core samples of experimental wells. As a result of the studies, the main lithological types of rocks of the Tolbachan Formation were described, and the stratification of the strata according to physical and mechanical properties was performed. The conducted studies allow characterizing the mining and geological conditions for the construction of underground mine workings, as well as to predict the main negative factors affecting the mining operations at the deposit.
ON THE IMPACT OF UNDERGROUND MINING ON THE ENGINEERING AND GEOLOGICAL CONDITIONS OF THE KAMA REGION
Authors:   Perm State University, Gainanov S. H. , Aptukov V. N. ,   Perm State University
Published: September 2022

The article is devoted to the current engineering and geological topic - the impact of underground workings on the conditions of construction and engineering structures operation at territory of urban agglomerations. The example of the city of Perm shows the need to take into account their presence and influence on the bearing capacity of the base rocks, especially of the cover quaternary soils. The design method proposes the possibility of using the bedrock as a base, taking into account the depth of workings, the load on their roof and strength characteristics, as well as the type of foundation used in this case.
ABOUT SPECIFIC CHARACTER OF THE PRECAMBRIAN SOURCES OF PLACER DIAMONDS
Authors:   Western-Yakutian Scientific Center of the Sakha Republic Academy of Sciences, Zinchuk N. N. , Bardukhinov L. D. ,   Western-Yakutian Scientific Center of the Sakha Republic Academy of Sciences
Published: September 2022

Basing on generalization of large actual material on complex investigation of diamond, its characteristic in Upper Paleozoic and Mesozoic sedimentary thick layers of the Siberian platform is provided. This allowed obtaining new data on composition features and distribution of diamond in recent sediments of main diamondiferous regions of Lena-Anabar, Central-Siberian and Tungusskaya sub-provinces. Special attention is paid to the Central-Siberian sub-province, where it is convincingly shown that the area of development of individual diamond macro-associations is limited by a specific diamondiferous region. Identified in some diamondiferous regions proximity of typomorphic features of diamonds in sedimentary thick layers of these ages testifies that formation of these collectors was due to washout of more ancient productive layers or Middle Paleozoic primary sources. T was noted that a polygeny of mineralogical associations of diamonds from different in age placers within some diamondiferous regions might be successfully used for forecasting and prospecting primary sources.
Control of mechanical properties of a high-entropy alloy Cantor CoCrFeMnNi
Authors: Gromov V. E. , Konovalov S. V. , Shlyarova Yu. A. , Efimov M. O. , Panchenko I. A.
Published: September 2022

A brief analysis of the work on changing the mechanical properties of the high-energy alloy (HEA) Cantor CoCrFeMnNi in various ways has been performed. The article describes the influence of alloying with aluminum, vanadium, manganese, titanium, silicon, carbon, copper on the hardening of wind turbines obtained by vacuum arc melting, laser melting, arc melting and drip casting, mechanical alloying with subsequent plasma sintering, gas sputtering followed by shock wave and static compaction. It is shown that additives of 2.5 % TiC and 5 % WC significantly improve the tensile strength, but reduce the elongation to failure. The effect of grain size in the range of 4.4 – 155 µm is to increase the tensile strength with a decrease in grain size. Lowering the temperature increases the strength and yield limits for grains of all sizes. Intensive plastic deformation forming nanoscale (~50 nm) grains significantly increases the tensile strength up to 1950 MPa and hardness up to 520 HV. Subsequent isochronous and isothermal annealing allows varying the strength and ductility of wind turbines. The formation of nanostructured-phase states during shock compounding, mechanical alloying and subsequent spark plasma formation significantly increase the tensile strength at room temperature, maintaining excellent plasticity (elongation of approximately 28 %). As one of the methods of modifying the mechanical properties of wind turbines, the authors propose electron-beam processing (EPO). The analysis of the deformation curves of the wind turbine, obtained by the technology of wire-arc additive production, after EPO with an electron beam energy density of 10 – 30 J/cm2, has been carried out; assumptions about the reasons for the decrease in strength and ductility characteristics have been found and substantiated. A comparative analysis of mechanical properties of the Cantor wind turbine obtained by various methods was carried out, and the reasons for discrepancy in the values of strength and plastic parameters were noted.
Thermodynamic modeling of metal reduction in copper-smelting slags and experimental verification of its results
Authors: Adilov G. , Povolotskii A. D. , Roshchin V. E.
Published: September 2022

Over 110 million tons of slag were accumulated in the dumps of the Russian copper-smelting enterprises, and their number is increasing. Environmental taxes and dumps maintenance costs are burdensome, which makes it necessary to make the most complete disposal of these production wastes. At the same time, these slags contain valuable elements, in particular, iron, copper, zinc, selenium, arsenic and some others, the extraction of which can make recycling profitable. The paper presents the results of a thermodynamic calculation of the behavior of copper-smelting slag elements in the mixture with carbon during heating. Modeling was performed using the TERRA software package. The influence of the process temperature in the range of 600 – 1750 °C on reduction of iron, zinc and silicon was analyzed at the amount of carbon in the system corresponding to the stoichiometry of iron reduction reactions and exceeding the stoichiometric one. It was established that when heated above 650 °C, metallic iron appears in the system, and its full reduction is completed at 1250 °C. The appearance of metallic zinc is observed in two temperature ranges: in the first, appearance of zinc is observed with a simultaneous decrease in concentration of zinc oxide; in the second, an increase in concentration of metallic zinc with a simultaneous decrease in concentration of zinc sulfide. At temperatures above 1650 °C, silicon appears in the system. Under laboratory conditions, the processes of solid-phase reduction of iron with the capture of zinc oxide and separation of the reduction products were tested. It was established that as a result of pyrometallurgical separation by melting reduction products, iron-carbon alloys (steel and cast iron) and alloys with high silicon content can be obtained. The results of the work can be used in development of theoretical and technological foundations for the processing of copper smelting slags, which are not processed by existing technologies.
Contextos y realidades ciudadanas: bases para el desarrollo sostenible
Authors:   Universidad Simón Bolívar, Ramírez-Martínez Carolina , Serrano-Correa Hunter-Dooglas ,   Universidad Simón Bolívar, Villamizar Peña Karlem Yomari ,   Universidad Simón Bolívar, Vidales Rivera María José ,   Universidad Simón Bolívar, Padilla Delgado Héctor Antonio ,   Universidad Autónoma de Ciudad Juárez, Alba-Niño Magali ,   Universidad Simón Bolívar, Maldonado Parada Karla Alejandra ,   Universidad Simón Bolívar, Carreño-Paredes Myriam-Teresa ,   Universidad Simón Bolívar, Mendoza Peñaranda Álvaro ,   Universidad Simón Bolívar, Mora-Ramírez Miguel-Antonio ,   Universidad Simón Bolívar, Castellanos Silvia Dayana ,   Universidad Simón Bolívar, Ortega-Ortega Jhorman-Yarokssi ,   Universidad Simón Bolívar, Arciniegas-Kopp Laura-Patricia ,   Universidad Simón Bolívar, Vargas-Clavijo Onofre ,   Universidad Simón Bolívar, Buitrago-Carrillo Edith-Dayana ,   Universidad Simón Bolívar, Ortega Suárez Luz Dary ,   Universidad Simón Bolívar, Salazar-Torres Juan Pablo ,   Universidad Simón Bolívar
Published: September 2022

En Contextos y realidades ciudadanas: bases para el desarrollo sostenible se muestra un recorrido por diversas situaciones de pobreza, migración, convivencia, desigualdad de género, desafiliación económica, de relaciones y de ciudadanía, poniendo rostro a múltiples realidades en contextos de frontera que nos permiten comprender situaciones 'glocales' -particularidades que nos llevan a comprender lo propio, pero también de ciudadanía global-. No es un texto desalentador en cuanto a las diversas problemáticas expresadas, por el contrario, muestra los rostros de las vivencias a la vez que articula bases teóricas y conceptuales que representan estrategias de transformación. Cada capítulo se articula con los objetivos de desarrollo sostenible de una manera práctica que lo llevará a comprender e impulsar acciones para este nuevo decenio en el cual los ODS llevan un recorrido importante y se posesionan para sentirlos y adherirnos desde las prácticas cotidianas y no desde una mirada delegada a sectores públicos u ONG's para abrirnos espacios cotidianos en los cuales podamos sumar a diario.
Innovación educativa en contexto. Una mirada a la práctica docente desde de la academia
Authors:   Universidad Simón Bolívar, Mendoza Pacheco Manuel Antonio , Ortiz-Padilla Myriam Esther ,   Universidad Simón Bolívar, Gómez Fontalvo Vicky Johana ,   Universidad Simón Bolívar, Mejía Gómez Gina Judith ,   Universidad Simón Bolívar, Barros-Moncada Melisa ,   Universidad Simón Bolívar, Sánchez-Salinas Caterine ,   Universidad Simón Bolívar, Escudero-Cabarcas Johana ,   Universidad Simón Bolívar, Pineda-Alhucema Wilmar ,   Universidad Simón Bolívar, Llanes Plata Ricardo ,   Universidad Simón Bolívar, Tapias Montenegro Dugeinys ,   Universidad Simón Bolívar, Jiménez Cantillo Yira ,   Universidad Simón Bolívar, Gravini Donado Marbel ,   Universidad Simón Bolívar, Bermejo Bolaño Inmaculada ,   Universidad Simón Bolívar, Galezo Cárdenas Norela ,   Universidad Simón Bolívar, Mejía Padilla Manuel ,   Universidad Simón Bolívar, Turizo Arzuza Maritza Elena ,   Universidad Simón Bolívar, González Gutiérrez Ingris ,   Universidad Simón Bolívar, Mejía-Rodríguez Dania ,   Universidad Simón Bolívar, Rubio-Castro Rosmira ,   Universidad Simón Bolívar, Torres Algarín Sofía Isabel ,   Universidad Simón Bolívar, Acosta Santodomingo Berthina Beatriz ,   Universidad Simón Bolívar, Mercado Yejas Yazmin De los Milagros ,   Universidad Simón Bolívar, Villalba Villadiego Anuar ,   Universidad Simón Bolívar, Escorcia Sarmiento María ,   Universidad Simón Bolívar, Navarro Oliveros Alberto ,   Universidad Simón Bolívar, Uribe Escorcia Judith ,   Universidad Simón Bolívar, Henao-Gil José ,   Universidad Simón Bolívar, Castro Hernández Carlos José ,   Universidad Simón Bolívar, Narváez-Vélez Jorge Adalberto ,   Universidad Simón Bolívar, Toloza-Toloza Jhon Jairo ,   Universidad Simón Bolívar, Campo-Ternera Lilia Angélica ,   Universidad Simón Bolívar, Fonseca Martínez Álvaro ,   Universidad Simón Bolívar, Echávez Molina Carlos ,   Universidad Simón Bolívar, Ramírez Meza Cianiris ,   Universidad Simón Bolívar
Published: September 2022

Innovación educativa en contexto: Una mirada a la práctica docente desde la academia Tomo I y II, permite recoger la experiencia de Maestros que a través de la investigación identifican y dan solución a las preocupaciones educativas de su contexto y con ello aportan a la calidad educativa. En el desarrollo de estas investigaciones se vislumbra el compromiso de los docentes investigadores en la búsqueda de alternativas de solución a través de las instituciones educativas de diferentes ciudades y municipios del departamento de Magdalena a partir de la reflexión sobre la práctica docente. Se vislumbra la experiencia cualificadora que desde la Maestría en Educación de la Universidad Simón Bolívar propende por la formación de docentes investigadores procurando la transformación de la calidad educativa de la región y el país, a partir de la reflexión de la propia práctica docente y mirando fortalezas y oportunidades de mejora para reinventar su quehacer a través de la innovación desde las diferentes áreas del conocimiento.
Arbeitsfähigkeit und sozialmedizinische Begutachtung bei Fatigue-Syndromen
Authors:   Institut für Sozialmedizin, Rehabilitationswissenschaften und Versorgungsforschung, Hochschule Nordhausen, Bassler Markus
Published: September 2022

Die sozialmedizinische Begutachtung orientiert sich eng an der 2001 neu eingeführten „Internationalen Klassifikation der Funktionsfähigkeit, Behinderung und Gesundheit“ (ICF) der Weltgesundheitsorganisation (WHO). Die Beurteilung der beruflichen Leistungsfähigkeit stützt sich gemäß der ICF-Kriterien vorrangig auf den Abgleich der dem Individuum möglichen Aktivitäten und Fähigkeiten mit dem jeweiligen beruflichen Anforderungsprofil. Im Zusammenhang damit implizieren die Begriffe „Arbeits(un)fähigkeit“, „Erwerbs(un)fähigkeit bzw. „Erwerbsminderung“ deutlich mehr als bisher sozialrechtlich relevante Aspekte.