bluuebunny/crossref_metadata_2025 huggingface dataset:
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints
Authors: Yuan Jinyang , Li Bin , Xue Xiangyang
Published: July 2022

Visual scenes are extremely rich in diversity, not only because there are infinite combinations of objects and background, but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a visual scene that contains multiple objects from multiple viewpoints, humans are able to perceive the scene in a compositional way from each viewpoint, while achieving the so-called ``object constancy'' across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have the similar ability. In this paper, we consider a novel problem of learning compositional scene representations from multiple unspecified viewpoints without using any supervision, and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. To infer latent representations, the information contained in different viewpoints is iteratively integrated by neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method is able to effectively learn from multiple unspecified viewpoints.
Fractional Adaptive Linear Units
Authors: Zamora Julio , Rhodes Anthony D. , Nachman Lama
Published: July 2022

This work introduces Fractional Adaptive Linear Units (FALUs), a flexible generalization of adaptive activation functions. Leveraging principles from fractional calculus, FALUs define a diverse family of activation functions (AFs) that encompass many traditional and state-of-the-art activation functions. This family includes the Sigmoid, Gaussian, ReLU, GELU, and Swish functions, as well as a large variety of smooth interpolations between these functions. Our technique requires only a small number of additional trainable parameters, and needs no further specialized optimization or initialization procedures. For this reason, FALUs present a seamless and rich automated solution to the problem of activation function optimization. Through experiments on a variety of conventional tasks and network architectures, we demonstrate the effectiveness of FALUs when compared to traditional and state-of-the-art AFs. To facilitate practical use of this work, we plan to make our code publicly available
Co-promotion Predictions of Financing Market and Sales Market: A Cooperative-Competitive Attention Approach
Authors: Zhang Lei , Xiang Wang , Zhao Chuang , Zhao Hongke , Li Rui , Wu Runze
Published: July 2022

Market popularity prediction has always been a hot research topic, such as sales prediction and crowdfunding prediction. Most of these studies put the perspective on isolated markets, relying on the knowledge of certain market to maximize the prediction performance. However, these market-specific approaches are restricted by the knowledge limitation of isolated markets and incapable of the complicated and potential relations among different markets, especially some with strong dependence such as the financing market and sales market. Fortunately, we discover potentially symbiotic relations between the financing market and the sales market, which provides us with an opportunity to co-promote the popularity predictions of both markets. Thus, for bridgly learning the knowledge interactions between financing market and sales market, we propose a cross-market approach, namely CATN: Cooperative-competitive Attention Transfer Network, which could effectively transfer knowledge of financing capability from the crowdfunding market and sales prospect from the E-commerce market. Specifically, for capturing the complicated relations especially the cooperation or complement of items and enhancing the knowledge transfer between the two heterogeneous markets, we design a novel Cooperative Attention; meanwhile, for finely computing the relations of items especially the competition in specific same market, we further design Competitive Attentions for the two markets respectively. Besides, we also distinguish aligned features and unique features to adapt the cross-market predictions. With the real-world datasets collected from Indiegogo and Amazon, we construct extensive experiments on three types of datasets from the two markets and the results demonstrate the effectiveness and generalization of our CATN model.
Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum
Authors: Zhang Zeyang , Zhang Ziwei , Wang Xin , Zhu Wenwu
Published: July 2022

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions.
Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution.
To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver.
This problem is challenging because it is non-trivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver.
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap. The codes are publicly available.
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Authors: Zheng Liyuan , Fiez Tanner , Alumbaugh Zane , Chasnov Benjamin , Ratliff Lillian J.
Published: July 2022

The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adopt this viewpoint and model the actor and critic interaction as a two-player general-sum game with a leader-follower structure known as a Stackelberg game. Given this abstraction, we propose a meta-framework for Stackelberg actor-critic algorithms where the leader player follows the total derivative of its objective instead of the usual individual gradient. From a theoretical standpoint, we develop a policy gradient theorem for the refined update and provide a local convergence guarantee for the Stackelberg actor-critic algorithms to a local Stackelberg equilibrium. From an empirical standpoint, we demonstrate via simple examples that the learning dynamics we study mitigate cycling and accelerate convergence compared to the usual gradient dynamics given cost structures induced by actor-critic formulations. Finally, extensive experiments on OpenAI gym environments show that Stackelberg actor-critic algorithms always perform at least as well and often significantly outperform the standard actor-critic algorithm counterparts.
Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations
Authors: Zhu Zhuangdi , Lin Kaixiang , Dai Bo , Zhou Jiayu
Published: July 2022

Reinforcement learning (RL) has demonstrated its superiority in solving sequential decision-making problems. However, heavy dependence on immediate reward feedback impedes the wide application of RL. On the other hand, imitation learning (IL) tackles RL without relying on environmental supervision by leveraging external demonstrations. In practice, however, collecting sufficient expert demonstrations can be prohibitively expensive, yet the quality of demonstrations typically limits the performance of the learning policy. To address a practical scenario, in this work, we propose Self-Adaptive Imitation Learning (SAIL), which, provided with a few demonstrations from a sub-optimal teacher, can perform well in RL tasks with extremely delayed rewards, where the only reward feedback is trajectory-wise ranking. SAIL bridges the advantages of IL and RL by interactively exploiting the demonstrations to catch up with the teacher and exploring the environment to yield demonstrations that surpass the teacher. Extensive empirical results show that not only does SAIL significantly improve the sample efficiency, but it also leads to higher asymptotic performance across different continuous control tasks, compared with the state-of-the-art.
Explainable Planner Selection for Classical Planning
Authors: Ferber Patrick , Seipp Jendrik
Published: July 2022

Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks.
A* Search and Bound-Sensitive Heuristics for Oversubscription Planning
Authors: Katz Michael , Keyder Emil
Published: July 2022

Oversubscription planning (OSP) is the problem of finding plans that maximize the utility value of their end state while staying within a specified cost bound. Recently, it has been shown that OSP problems can be reformulated as classical planning problems with multiple cost functions but no utilities. Here we take advantage of this reformulation to show that OSP problems can be solved optimally using the A* search algorithm, in contrast to previous approaches that have used variations on branch-and-bound search. This allows many powerful techniques developed for classical planning to be applied to OSP problems. We also introduce novel bound-sensitive heuristics, which are able to reason about the primary cost of a solution while taking into account secondary cost functions and bounds, to provide superior guidance compared to heuristics that do not take these bounds into account. We propose two such bound-sensitive variants of existing classical planning heuristics, and show experimentally that the resulting search is significantly more informed than with comparable heuristics that do not consider bounds.
Planning to Avoid Side Effects
Authors: Klassen Toryn Q. , McIlraith Sheila A. , Muise Christian , Xu Jarvis
Published: July 2022

In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.
Formula Synthesis in Propositional Dynamic Logic with Shuffle
Authors: Pinchinat Sophie , Rubin Sasha , Schwarzentruber François
Published: July 2022

We introduce the formula-synthesis problem for Propositional Dynamic Logic with Shuffle (PDL || ). This problem, which generalises the model-checking problem againsts PDL || is the following: given a finite transition system and a regular term-grammar that generates (possibly infinitely many) PDL || formulas, find a formula generated by the grammar that is true in the structure (or return that there is none). We prove that the problem is undecidable in general, but add certain restrictions on the input structure or on the input grammar to yield decidability. In particular, we prove that (1) if the grammar only generates formulas in PDL (without shuffle), then the problem is EXPTIME-complete, and a further restriction to linear grammars is PSPACE-complete, and a further restriction to non-recursive grammars is NP-complete, and (2) if one restricts the input structure to have only simple paths then the problem is in 2-EXPTIME. This work is motivated by and opens up connections to other forms of synthesis from hierarchical descriptions, including HTN problems in Planning and Attack-tree Synthesis problems in Security.
Efficient Encoding of Cost Optimal Delete-Free Planning as SAT
Authors: Rankooh Masood Feyzbakhsh , Rintanen Jussi
Published: July 2022

We introduce a novel method for encoding cost optimal delete-free STRIPS Planning as SAT. Our method is based on representing relaxed plans as partial functions from the set of propositions to the set of actions. This function can map any proposition to a unique action that adds the proposition during execution of the relaxed plan. We show that a relaxed plan can be produced by maintaining acyclicity in the graph of all causal relations among propositions, represented by the mentioned partial function. We also show that by efficient encoding of action cost propagation and enforcing a series of upper bounds on the total costs of the output plan, an optimal plan can effectively be produced for a given delete-free STRIPS problem. Our empirical results indicate that this method is quite competitive with the state of the art, demonstrating a better coverage compared to that of competing methods on standard STRIPS planning benchmark problems.
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
Authors: Rigter Marc , Dervovic Danial , Hassanzadeh Parisa , Long Jason , Zehtabi Parisa , Magazzeni Daniele
Published: July 2022

We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by N identical servers. Each task has a price, which is a positive real number, and a class. Each class of task has a different price distribution, service rate, and arrives according to an inhomogenous Poisson process. The objective is to decide which tasks to accept so that the total price of tasks processed is maximised over a finite horizon. We formulate the problem using a discrete time Markov Decision Process (MDP) with a hybrid state space. We show that the optimal value function has a specific structure, which enables us to solve the hybrid MDP exactly. Moreover, we rigorously prove that as the gap between successive decision epochs grows smaller, the discrete time solution approaches the optimal solution to the original continuous time problem. To improve the scalability of our approach to a greater number of servers and task classes, we present an approximation based on state abstraction. We validate our approach on synthetic data, as well as a real financial fraud data set, which is the motivating application for this work.
Equity Promotion in Online Resource Allocation
Authors: Xu Pan , Xu Yifan
Published: July 2022

We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio.
We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.
Efficient Device Scheduling with Multi-Job Federated Learning
Authors: Zhou Chendi , Liu Ji , Jia Juncheng , Zhou Jingbo , Zhou Yang , Dai Huaiyu , Dou Dejing
Published: July 2022

Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for machine learning jobs due to laws or regulations. Federated Learning (FL) emerges as an effective approach to handling decentralized data without sharing the sensitive raw data, while collaboratively training global machine learning models. The servers in FL need to select (and schedule) devices during the training process. However, the scheduling of devices for multiple jobs with FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework to enable the parallel training process of multiple jobs. The framework consists of a system model and two scheduling methods. In the system model, we propose a parallel training process of multiple jobs, and construct a cost model based on the training time and the data fairness of various devices during the training process of diverse jobs. We propose a reinforcement learning-based method and a Bayesian optimization-based method to schedule devices for multiple jobs while minimizing the cost. We conduct extensive experimentation with multiple jobs and datasets. The experimental results show that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 8.67 times faster) and accuracy (up to 44.6% higher).
On the Impact of Spurious Correlation for Out-of-Distribution Detection
Authors: Ming Yifei , Yin Hang , Li Yixuan
Published: July 2022

Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments. While much research attention has been placed on designing new out-of-distribution (OOD) detection methods, the precise definition of OOD is often left in vagueness and falls short of the desired notion of OOD in reality. In this paper, we present a new formalization and model the data shifts by taking into account both the invariant and environmental (spurious) features. Under such formalization, we systematically investigate how spurious correlation in the training set impacts OOD detection. Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set. We further show insights on detection methods that are more effective in reducing the impact of spurious correlation, and provide theoretical analysis on why reliance on environmental features leads to high OOD detection error. Our work aims to facilitate better understanding of OOD samples and their formalization, as well as the exploration of methods that enhance OOD detection. Code is available at https://github.com/deeplearning-wisc/Spurious_OOD.
Local and Global Linear Convergence of General Low-Rank Matrix Recovery Problems
Authors: Bi Yingjie , Zhang Haixiang , Lavaei Javad
Published: July 2022

We study the convergence rate of gradient-based local search methods for solving low-rank matrix recovery problems with general objectives in both symmetric and asymmetric cases, under the assumption of the restricted isometry property. First, we develop a new technique to verify the Polyak-Lojasiewicz inequality in a neighborhood of the global minimizers, which leads to a local linear convergence region for the gradient descent method. Second, based on the local convergence result and a sharp strict saddle property proven in this paper, we present two new conditions that guarantee the global linear convergence of the perturbed gradient descent method. The developed local and global convergence results provide much stronger theoretical guarantees than the existing results. As a by-product, this work significantly improves the existing bounds on the RIP constant required to guarantee the non-existence of spurious solutions.
A*+BFHS: A Hybrid Heuristic Search Algorithm
Authors: Bu Zhaoxing , Korf Richard E.
Published: July 2022

We present a new algorithm called A*+BFHS for solving problems with unit-cost operators where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths between the same pair of nodes. A*+BFHS is based on A* and breadth-first heuristic search (BFHS). A*+BFHS combines advantages from both algorithms, namely A*'s node ordering, BFHS's memory savings, and both algorithms' duplicate detection. On easy problems, A*+BFHS behaves the same as A*. On hard problems, it is slower than A* but saves a large amount of memory. Compared to BFIDA*, A*+BFHS reduces the search time and/or memory requirement by several times on a variety of planning domains.
Learning from Mistakes – a Framework for Neural Architecture Search
Authors: Garg Bhanu , Zhang Li , Sridhara Pradyumna , Hosseini Ramtin , Xing Eric , Xie Pengtao
Published: July 2022

Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In this paper, we investigate if this human learning strategy can be applied in machine learning. We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision. We formulate LFM as a three-stage optimization problem: 1) learner learns; 2) learner re-learns focusing on the mistakes, and; 3) learner validates its learning. We develop an efficient algorithm to solve the LFM problem. We apply the LFM framework to neural architecture search on CIFAR-10, CIFAR-100, and Imagenet. Experimental results strongly demonstrate the effectiveness of our model.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Authors: Kishimoto Akihiro , Bouneffouf Djallel , Marinescu Radu , Ram Parikshit , Rawat Ambrish , Wistuba Martin , Palmes Paulito , Botea Adi
Published: July 2022

Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, pipeline optimization remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized algorithm selection (ML operations) in a fixed ML pipeline structure. BLDS performs multi-fidelity optimization for selecting ML algorithms trained with smaller computational overhead, while controlling its pipeline search based on multi-armed bandit and limited discrepancy search. Our experiments on well-known classification benchmarks show that BLDS is superior to competing algorithms. We also combine BLDS with hyperparameter optimization, empirically showing the advantage of BLDS.
Improving Local Search Algorithms via Probabilistic Configuration Checking
Authors: Luo Weilin , Ye Rongzhen , Wan Hai , Cai Shaowei , Fang Biqing , Zhang Delong
Published: July 2022

Configuration checking (CC) has been confirmed to alleviate the cycling problem in local search for combinatorial optimization problems (COPs). When using CC heuristics in local search for graph problems, a critical concept is the configuration of the vertices. All existing CC variants employ either 1- or 2-level neighborhoods of a vertex as its configuration. Inspired by the idea that neighborhoods with different levels should have different contributions to solving COPs, we propose the probabilistic configuration (PC), which introduces probabilities for neighborhoods at different levels to consider the impact of neighborhoods of different levels on the CC strategy. Based on the concept of PC, we first propose probabilistic configuration checking (PCC), which can be developed in an automated and lightweight favor. We then apply PCC to two classic COPs which have been shown to achieve good results by using CC, and our preliminary results confirm that PCC improves the existing algorithms because PCC alleviates the cycling problem.
Planning with Explanations for Finding Desired Meeting Points on Graphs
Authors: Otaki Keisuke
Published: July 2022

Combinatorial optimization problems are ubiquitous for decision making in planning social infrastructures.
In real-world scenarios, a decision-maker needs to solve his/her problem iteratively until he/she satisfies solutions, but such an iterative process remains challenging.
This paper studies a new explainable framework, particularly for finding meeting points, which is a key optimization problem for designing facility locations.
Our framework automatically fills the gap between its input instance and instances from which a user could obtain the desired outcome, where computed solutions are judged by the user.
The framework also provides users with explanations, representing the difference of instances for deeply understanding the process and its inside.
Explanations are clues for users to understand their situation and implement suggested results in practice (e.g., designing a coupon for free travel).
We experimentally demonstrate that our search-based framework is promising to solve instances with generating explanations in a sequential decision-making process.
A Fast Local Search Algorithm for the Latin Square Completion Problem
Authors: Pan Shiwei , Wang Yiyuan , Yin Minghao
Published: July 2022

The Latin square completion (LSC) problem is an important NP-complete problem with numerous applications. Given its theoretical and practical importance, several algorithms are designed for solving the LSC problem. In this work, to further improve the performance, a fast local search algorithm is developed based on three main ideas. Firstly, a reduction reasoning technique is used to reduce the scale of search space. Secondly, we propose a novel conflict value selection heuristic, which considers the history conflicting information of vertices as a selection criterion when more than one vertex have equal values on the primary scoring function. Thirdly, during the search phase, we record previous history search information and then make use of these information to restart the candidate solution. Experimental results show that our proposed algorithm significantly outperforms the state-of-the-art heuristic algorithms on almost all instances in terms of success rate and run time.
DPCD: Discrete Principal Coordinate Descent for Binary Variable Problems
Authors: Xiong Huan
Published: July 2022

Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning. Generally speaking, binary optimization problems are NP-hard and difficult to solve due to the binary constraints, especially when the number of variables is very large. Existing methods often suffer from high computational costs or large accumulated quantization errors, or are only designed for specific tasks. In this paper, we propose an efficient algorithm, named Discrete Principal Coordinate Descent (DPCD), to find effective approximate solutions for general binary optimization problems. The proposed algorithm iteratively solves optimization problems related to the linear approximation of loss functions, which leads to updating the binary variables that most impact the value of the loss functions at each step. Our method supports a wide range of empirical objective functions with/without restrictions on the numbers of 1s and -1s in the binary variables. Furthermore, the theoretical convergence of our algorithm is proven, and the explicit convergence rates are derived for objective functions with Lipschitz continuous gradients, which are commonly adopted in practice. Extensive experiments on binary hashing tasks and large-scale datasets demonstrate the superiority of the proposed algorithm over several state-of-the-art methods in terms of both effectiveness and efficiency.
Capability analysis of computational fluid dynamics models in wind shield study on Queensferry Crossing, Scotland
Authors: Zhu Licheng , McCrum Daniel , Keenahan Jennifer
Published: July 2022

Bridge aerodynamic studies are essential in ensuring the safety and acceptable performance of long-span bridges vulnerable to the effects of cross-winds. Aerodynamic studies were traditionally carried out in wind tunnel facilities, but there are now greater opportunities for using computational fluid dynamics (CFD) modelling. Few three-dimensional (3D) aerodynamic simulations of lightweight vehicles on bridges exist, but limited validation and verification work has been carried out. In the study reported in this paper, 3D CFD models were developed for Queensferry Crossing – a cable-stayed bridge in Scotland – containing wind shields and sample vehicles. The models considered the wind effects from a range of yaw wind angles and subsequently determined the aerodynamic coefficients of vehicles. The models were verified by means of a mesh sensitivity study, a domain sensitivity study and comparisons with wind tunnel tests. The models were then validated using the same modelling process but with a different type of wind shield and again comparing the results with wind tunnel test data for the same configuration. The results showed that CFD modelling can determine aerodynamic coefficients to a level of accuracy similar to that of wind tunnel tests.
ICE report confirms that systems thinking is improving the delivery of major projects
Authors: McNaughton Andrew
Published: July 2022

ICE has published a follow-up to its well-received 2020 report proposing greater use of systems thinking to improve delivery of major infrastructure projects. Project lead Andrew McNaughton introduces the second report, which confirms that the approach is working in practice.
Enhancing the detection performance of a vision-based occupancy detector for buildings
Authors: Tien Paige Wenbin , Wei Shuangyu , Chow Tin Wai , Darkwa Jo , Wood Christopher , Calautit John Kaiser
Published: July 2022

Occupant behaviour is one of the key parameters that significantly impact the operation of heating, ventilation and air-conditioning (HVAC) systems and the energy performance of buildings. Detailed occupancy information can improve HVAC operation and utilisation of building spaces. Strategies such as vision-based occupancy detection and recognition have recently garnered much interest. This study investigated the performance of a vision-based deep learning detection technique for enhancing building system operations and energy performances. The model used was the Faster region-based convolutional neural network with Inception V2. Two occupancy detection model configurations were developed, tested and evaluated. Both models were analysed based on the application of the detector within a selected case study building, along with an evaluation based on different evaluation metrics. The results suggest the occupancy detector using model 1 provided an overall accuracy of 95.23%, while model 2 (the occupancy activity detector) provided an accuracy of 89.37%. In addition, the F 1 score, which represents the harmonic mean between the precision and recall values, was 0.9756 for model 1 and 0.8298 for model 2. Building energy simulation was used to evaluate and compare the impact of such an approach on indoor occupancy heat gains. The study highlighted the potential of the detection approaches, but further development is necessary, including optimisation of the model, full integration with HVAC controls and further model training and field testing.
Influence of fines content on the undrained flow instability of loess
Authors: Liu F. , Xu Q. , Zhang Y. , Macedo J. E.
Published: July 2022

The influence of fines content (FC) on flow instability of intact and reconstituted Chinese loess is studied under undrained monotonic loading using conventional triaxial tests. The undrained behaviour of a mixed loess system indicates that the increasing plastic fines reduce the peak strength with little effect on the critical state friction angle. The changing FC can result in parallel translation and rotation of the critical state lines (CSLs) of reconstituted loess in the volumetric plane. A coarse-grain- to fine-grain-dominant transition is found as FC attains 55·5%, with a convergence of CSL for FC ranging from 55·5 to 87·9% using the equivalent interfine void ratio. A similar trend is found for intact specimens, suggesting that fines may act as void fillers at low-stress levels, which indicates a competing effect of structure and interfine contacts. The modified state parameter is robust in characterising the flow behaviour of the Chinese loess with revised range and corresponding descriptors.
Soil flow mechanisms around cone penetrometer in layered clays
Authors: Wang Yue , Hossain Muhammad Shazzad , Hu Yuxia
Published: July 2022

The cone penetrometer test is widely used for in situ site investigations and for establishing direct penetrometer-to-foundation or -anchor design correlations. This paper focuses on soil flow mechanisms during the continuous penetration of a cone penetrometer in layered clays. A series of centrifuge tests was conducted with the cone penetrating through soft–stiff, stiff–soft, soft–stiff–soft and stiff–soft–stiff clay profiles. Particle image velocimetry allowed accurate resolution of the soil flow mechanism around the cone where a half-cone model was penetrated into layered clays against a transparent window. The observed soil movement was compared both with previous observations for piles/cones and with movement from the shallow strain path method (SSPM). A comparison with SSPM results showed that the SSPM can provide reasonable evaluations on maximum lateral and vertical displacements even though the upheave movement can be overestimated. The effect of soil layering on the failure mechanisms was studied extensively by exploring soil flow mechanisms and soil displacement paths at various distances from the advancing cone centreline and soil layer interface. The reported characteristics of cone penetration in layered soils provided in-depth understanding of cone penetration responses that will lead to the development of mechanism-based theoretical models for cone penetration in layered fine-grained soils.
Rolling of long-length rails with acceleration
Authors: Solov’ev V. N. , Belolipetskaya E. S.
Published: July 2022

The article explains a new temperature-speed mode of rolling long-length rails. Rails up to 50 m long were rolled on linear mills. The production of rails of longer length required the use of larger mass billets and equipment of a new design. At the modern rail-beam rolling mill, the number of stands has increased and some of the stands are combined into a continuous group. Universal rolling stands began to be used not only as calibration stands. Rails with a length of 100 m are rolled from large-mass billets on mills with a continuous reversible group of universal stands. A significant length of the billet leads to the formation of a «temperature wedge» – a decrease in temperature along the length of the rail during rolling in the last stand of the mill. The calculation showed that a decrease in temperature along the rail length can lead to an increase in its height. A similar problem existed in the production of thin sheet metal at continuous broadband hot rolling mill. Rolling the rolled metal in the finishing group of stands with acceleration made it possible to reduce the cooling time of rear section of the rolled metal and warm up the metal due to more intense deformation. The acceleration value is chosen such that at the exit from the finishing group of stands, temperature of the strip is the same along the length of the strip. In this paper, rolling rails with acceleration is proposed. The acceleration value should ensure the same neck temperature along the length of the rail in the mill last stand, which will reduce the height difference along the length of the rolled product. The same rails height along the length will reduce the cost of grinding and accelerate the rail laying, thereby increasing consumer demand and competitiveness of the product.
Viscosity of niobium oxide systems for production of complex ferroalloys
Authors: Zayakin O. V. , Shartdinov R. R. , Smetannikov A. N. , Kel’ I. N.
Published: July 2022

The relevance of the development of modern technological solutions for the processing of niobium ore materials used for the production of complex ferroalloys of domestic deposits is shown. The need to develop new technological processes of processing is caused by the fact that when switching to new types of niobium concentrates, the chemical and phase compositions of the starting materials change, and, consequently, this leads to a change in the composition and properties of the melting products (both metallic and oxide). Using an electrovibration viscometer, the temperature dependences of viscosity were studied and the crystallization temperatures of oxide melts of the Nb2O5 – SiO2 – CaO – TiO2 – Al2O3 system were calculated. The compositions of the studied samples correspond to phosphorless niobium slags, which can be obtained by carbothermic method from rough concentrates. The samples were obtained by fusing oxide materials in a high-temperature laboratory electric furnace. On the basis of the obtained data, graphical dependences of viscosity – temperature are constructed. It was experimentally established that oxide melts containing 15 – 26 % Nb2O5 are more “long” (having a wide crystallization range) and unfavorable according to the characteristic viscosity – temperature dependence. An increase in the concentration of niobium pentoxide to 40 % translates slags into the category of “short” (with a high crystallization rate). It is shown that an increase in the concentration of niobium oxide Nb2O5 from 15 to 40 % leads to a decrease in the crystallization temperature of melts by 200 °C and a decrease in the viscosity of melts from 1.32 to 0.24 Pa·s at 1350 °C. The improvement of the physico-chemical characteristics of oxide melts with an increase in the concentration of niobium pentoxide can favorably affect the technical and economic indicators of ferroalloy production.
Fractography of fracture surface of CrMnFeCoNi high-entropy alloy after electron-beam processing
Authors: Gromov V. E. , Ivanov Yu. F. , Osintsev K. A. , Vorob’ev S. V. , Panchenko I. A.
Published: July 2022

In the past decade the attention of scientists in the field of physical materials science is attracted to studying the high-entropy alloys. By the technology of wire-arc additive manufacturing (WAAM) a high-entropy alloy (HEA) of a nonequiatomic composition was obtained. Deformation curves obtained under uniaxial tension at a rate of 1.2 mm/min at room temperature using Instron 3369 unit were analyzed in two states: initial/after fabrication and after electron-beam treatment (EBT). EBT was conducted to detect its influence on structural-phase states and mechanical properties. The EBP leads to a decrease in strength and plastic properties of the HEA. By means of scanning electron microscope LEO EVO 50, analysis of structure of fracture surface and the near-surface zone was performed. Dependences of the ultimate strength and relative elongation to failure on EBT parameters were revealed, and it was shown that values of strength and plasticity decrease nonmonotonically with an increase in electron beam energy density in the range ES = 10 – 30 J/cm2 at constant values of duration, frequency, and number of pulses. Along with a pit character of the fracture a presence of micropores and microlayering was detected. Investigation of the HEA’s fracture surface after EBP except for areas with a ductile fracture mechanism revealed the regions with a band (lamellar) structure. At ES = 10 J/cm2, the area of such structure is 25 %; it increases nonmonotonically to 65 % at ES = 30 J/cm2. The diameter of pits of detachment in fracture bands varies in the limits of 0.1 – 0.2 μm, which is considerably less than that in the remainder of the HEA samples. After EBP the thickness of the molten layer varies in the limits of 0.8 – 5.0 μm and grows with an increase in the energy density of electron beam. EBT leads to generation of crystallization cells, the sizes of which change within the range 310 – 800 nm as ES increases from 15 to 30 J/cm2. It is suggested that the defects being formed in surface layers in ЕВР may be the reason for decreasing the HEA’s maximum values of strength and plasticity.
Effect-site concentration of remimazolam at loss and recovery of responsiveness during general anesthesia: a simulation study
Authors: Kim Kyung Mi , Bang Ji-Yeon , Lee Jong Min , Yang Hong Seuk , Choi Byung-Moon , Noh Gyu-Jeong
Published: July 2022

Background: The objective of this study was to investigate the effect-site concentration (Ce) of remimazolam at loss of response (LOR) and recovery of response (ROR) in patients underwent general anesthesia using simulation. In addition, the relationships between patient’s factors and simulated Ce at LOR and ROR were examined.Methods: The medical records of 81 patients who underwent elective surgery under general anesthesia using remimazolam with simulation of Ce between August 4, 2021 and October 12, 2021, were retrospectively reviewed. Remimazolam was administered as an induction dose of 6 or 12 mg/kg/h until the patient became unresponsive, followed by 0.32 mg/kg/h to maintain BIS values below 60. Simultaneously, simulations of manual infusion mode were performed using Asan Pump software and the Ce of remimazolam was simulated using the Schüttler model. Whenever infusion rate of remimazolam was manually changed, the simulated Ce was confirmed almost simultaneously. LOR and ROR, defined as unresponsive and eye-opening to verbal commands, respectively, were recorded in the Asan Pump program.Results: The median (1Q, 3Q) simulated Ce at LOR and ROR were 0.7 (0.5, 0.9) and 0.3 (0.2, 0.4) g/ml, respectively. LOR was achieved in 1.9 min after remimazolam infusion with cumulative doses of 0.3 mg/kg. There was a significant relationship between age and simulated Ce at ROR (Ce at ROR = –0.0043 × age + 0.57, r = 0.30, P = 0.014). Conclusions: For optimal dosage adjustment, simulating Ce while administering remimazolam with a weight-based dose during anesthesia is helpful. Elderly patients may recover from anesthesia at lower Ce of remimazolam.
La presencia de Galdós en los diarios de Andrés Trapiello
Authors: Luque Amo Álvaro
Published: July 2022

En las décadas finales del siglo XX e iniciales del siglo XXI se ha producido el asentamiento del diario personal en el sistema literario español, y entre las obras surgidas en este nuevo contexto destacan algunos diarios que mantienen un poderoso vínculo con la tradición literaria hispánica. Es el caso del que ha sido considerado frecuentemente el diario literario más importante del ámbito español: el Salón de pasos perdidos (1990-2021), de Andrés Trapiello. Este artículo tiene como objetivo analizar la influencia de Benito Pérez Galdós en el desarrollo de este proyecto diarístico que Andrés Trapiello, mediante una poética en marcha, ha llevado a cabo desde 1990 hasta la actualidad. La presencia galdosiana vertebra así buena parte de la obra de Trapiello, de modo que el estudio de la huella de Galdós en determinados rasgos narrativos permitirá entender el carácter literario, al tiempo que autobiográfico, de este diario.
Nueva aproximación al concepto de tópico literario: la trayectoria del tópico 'locura por culpa y remordimiento'
Authors: Nazemi Zahra , Maleki Nasser , Laguna Mariscal Gabriel
Published: July 2022

El objetivo del presente artículo es ofrecer una nueva visión sobre los tópicos literarios tanto en la teoría como en la práctica, contribuyendo a la definición misma de tópico, así como estudiando las similitudes y diferencias de este concepto literario con otros, como motivo, leitmotiv o tema. Centrándonos en la evolución literaria del tópico de la ‘locura por culpa y remordimiento’ desde la tradición clásica hasta el drama estadounidense moderno, pretendemos aplicar nuestra propuesta teórica al análisis de textos literarios. Se rastreará el desarrollo del citado tópico en Orestes de Eurípides (408 a.C.), Eneida de Virgilio (29-19 a.C.), Macbeth de Shakespeare (1606), «Roger Malvin’s Burial» de Nathaniel Hawthorne (1832) y Mourning Becomes Electra de Eugene O’Neill (1931). Se argumenta que la presencia de este tópico en las obras mencionadas sigue la tradición de la versión de Eurípides del mito de Orestes. Los resultados del estudio atestiguan la recurrencia creativa de este tópico en la literatura occidental.
La disparition d'un roman : La quatrieme invention. Journal d'embrouilles vers Compostelle de Bernard de Marsangy
Authors: Iñarrea Las Heras Ignacio
Published: July 2022

La quatrième invention. Journal d'embrouilles vers Compostelle (2005) est un roman de Bernard de Marsangy. Il raconte les aventures de Pierre-Antoine Rablaud pendant son pèlerinage à Compostelle. Ce personnage est un cadre qui abandonne tout pour faire ce voyage. Il sera persécuté par d'anciens partenaires et par les autorités françaises, pour avoir détourné des fonds. Dans le présent travail, nous essayons de montrer comment dans La quatrième invention. Journal d'embrouilles vers Compostelle s’opère un processus de disparition du roman. Cela se produit à deux niveaux : narratif et historique. Dans le premier, le récit se dissout avec la mort ou la disparition de ses personnages. Dans le second, le récit s’évanouit dans la tentative infructueuse de renaissance du bogomilisme et du catharisme au XXIe siècle. La dissolution du roman est pour Marsangy une manière de montrer sa conception de la vie comme une illusion, comme un vide.
Experimental testing of the influence of the operating loading on the flow
characteristics of hydraulic pump
Authors: Kosiba Ján , Tkáč Zdenko , Jablonický Juraj , Shcherbak Valeriia , Ganushchak-Yefimenko Liudmyla , Paumer Martin , Bukoros Tetiana , Čurgaliová Gabriela , Zachar Martin
Published: July 2022

The contribution deals with the influence of the operating loading on the flow characteristics of the hydraulic pump under laboratory conditions. In the test, a new toothed hydraulic pump and hydraulic oil with kinematic viscosity at 40°C, ν = 64.2 mm2
.s-1 were used.
The operational loading was measured during the most difficult agrotechnical operation –
ploughing, at which the loading of the hydraulic pump is the greatest. Laboratory testing
was determined for 200 hours, and the flow characteristics had been detected at 50 hours
intervals. Measurement of the flow characteristics was performed at rated speed n = 1,000
rpm. Measurement of the flow characteristics was carried out in dependence on the loading
pressure from p = 0 MPa to p = 20 MPa. When p = 0 MPa, the value reached n = 91.70%
and when p = 20 MPa, n = 86.68%. Running-in of the hydraulic pump lasted 150 hours, with
an increase of the flow characteristics Δηvol = 1.36% (p = 10 MPa) compared to the initial
state. After working for 200 hours, the flow rate of the hydraulic pump decreases compared
to 150 hours.