Miracle-dz/UniFAR-FTUs huggingface dataset:
Forklift Routing Optimization in a Warehouse using a Clustering-based Approach

Order picking in a warehouse is considered to be a time-consuming and costly process that results in loss of profit for the management. Hence a warehouse management team is always looking to improve their picking process and increase their efficiency. In this research, a warehouse with narrow aisles is studied. The aisles are so narrow that a forklift is only allowed to traverse them in one direction thus making them unidirectional. The picking process is modeled first as an uncapacitated vehicle routing problem and then as a capacitated vehicle routing problem. The objective is to minimize the total travel distance. Since the Mixed Integer Programming model takes a long time to solve large instances, we develop a heuristic algorithm both for the uncapacitated and capacitated problems by combining two methodologies of heuristics and machine learning. The algorithm is able to solve the instances to near optimality quickly, finding practical solutions that could potentially be implemented into actual warehouses to reduce order picking time and hence, overall warehouse costs.
Dermoscopic Image Segmentation Through the Enhanced High-Level Parsing and Class Weighted Loss

Accurate skin lesion segmentation plays an important role in the computer aided analysis of melanoma. It is a challenging task due to the variation of skin lesion appearance, the low contrast with background, and the existence of the artifacts in dermoscopic images. In this paper, we try to boost the skin lesion segmentation performance based on the fully convolutional neural network. To this end, we first propose an enhanced high-level parsing (EHP) module to generate meaningful feature representation for skin lesion and make more precise delineation of the detailed lesion structure. Furthermore, to handle the imbalance data distribution of skin lesion and background, we propose a class weighted loss (CWL) to achieve more consistent lesion prediction. Experiment results evaluated on ISBI 2017 database demonstrate the effectiveness and robustness of the proposed architecture on skin lesion segmentation, achieving new state-of-the-art prediction performance.
Text2Math: End-to-end Parsing Text into Math Expressions

We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller

We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demonstration from a third-person perspective. To accomplish this goal, our agent should not only learn to understand the intent of the demonstrated third-person video in its context but also perform the intended task in its environment configuration. Our central insight is to enforce this structure explicitly during learning by decoupling what to achieve (intended task) from how to perform it (controller). We propose a hierarchical setup where a high-level module learns to generate a series of first-person sub-goals conditioned on the third-person video demonstration, and a low-level controller predicts the actions to achieve those sub-goals. Our agent acts from raw image observations without any access to the full state information. We show results on a real robotic platform using Baxter for the manipulation tasks of pouring and placing objects in a box. Project video is available at https://pathak22.github. io/hierarchical-imitation/
Simple-Web-Server: a fast and flexible HTTP/1.1 C++ client and server library

The programming language C++ is commonly used for resource intensive tasks. SimpleWeb-Server is a library that can be utilized in C++ applications to implement web-resources or perform HTTP or HTTPS requests in a simple manner across OS platforms compared to using a networking library directly. Thus, Simple-Web-Server can be helpful for any research software written in C++ that needs to communicate with remote endpoints through HTTP or HTTPS.
Difference of Convolution for Deep Compressive Sensing

Deep learning-based compressive sensing (DCS) has improved the single scale compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of Convolution-based multi-scale DCS (DoC-DCS). Unlike the multi-scale DCS based on a well-designed filter in the wavelet domain, our DoC-DCS jointly learns decomposition, sampling, and reconstruction thereby outperforms other state-of-the-art deep learning based CS methods.
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.
Iotbench: A Benchmark Suite for Intelligent Internet of Things Edge Devices

IoT devices must and will be more intelligent in the future. However, due to the lack of benchmarks representative to the diverse IoT applications, there are limited architecture performance studies on IoT devices. This paper presents IoTBench, the first benchmark suite targeting at IoT edge-device applications. This suite includes seven representative programs from three major IoT categories. We investigate the computational demand and execution efficiency of these benchmarks running on a popular IoT device platform. We also analyze and characterize the energy consumption of IoTBench using analytic approaches. Overall, IoTBench establishes a foundation for innovative architecture design of IoT edge devices.
Human Activity Recognition with Deep Metric Learners

Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-based return by training upon multiple cases simultaneously to incorporate relationship knowledge. This is particularly important in the Human Activity Recognition (HAR) domain, where understanding similarity between cases supports aspects such as personalisation and open-ended HAR. In this paper, we perform a short review of three DMLs and compare their performance across three HAR datasets. Our findings support research which indicates DMLs are valuable to improve similarity-based return and indicate that considering more cases simultaneously offers better performance.
Emotion Recognition by Edge-Weighted Hypergraph Neural Network

Over the past decade, increasing research efforts have been concentrated on emotion recognition from physiological signals due to their capability on emotion information representation. Existing works mainly focus on exploring the relationship between stimulus and subjects, while ignoring the effects of latent correlations among different subjects, which are important for personalized emotion recognition. To tackle this issue, we aim to conduct emotion recognition using multi-modal physiological signals through an edge-weighted hyper-graph neural network, in which complex relationship among subjects is formulated using hypergraph for each modality respectively. In our model, the differences in significance of influence which various samples leave on the classification can be better represented. The major contribution of this network lies in its concern that the associate strengths between various samples are different, which have different impact on the training result. The hyperedge between the vertices with closer correlation should be assigned a larger weight. Reversely, the looser relation, the minor weight. To evaluate the proposed method, experiments have been conducted on the DEAP dataset and ASCERTAIN dataset. Experimental results and comparison with state-of-the-art methods show that the proposed method can achieve better performance.
A Novel Adaptive Resource Allocation Model Based on SMDP and Reinforcement Learning Algorithm in Vehicular Cloud System

In this paper, we propose a novel adaptive cloud resource allocation model based on Semi-Markov Decision Process (SMDP) and Reinforcement Learning (RL) algorithm in vehicular cloud system. The issue of adaptive resource allocation for vehicular request is formed as an SMDP in order to gain the dynamics of vehicular requests’ arrival and departure. An optimized decision is made to guarantee the Quality of Service (QoS) of the vehicular cloud system and the Quality of Experience (QoE) of the vehicular users as well as to maximize the total system reward of the vehicular cloud system in consideration of the balance between the vehicular cloud resource expense and the system income. Furthermore, to capture the mobility feature of the vehicular cloud system, we also apply a neural-network-based RL algorithm to resolve our proposed SMDP-based adaptive cloud resource allocation model. Firstly, we use a Planning algorithm to get the action values under certain state-action pairs, which are the initial samples to train the neural network. Then the RL is used to update the parameters of the neural network, train the neural network and adaptively improve the decision strategy. Subsequently, an adaptive vehicular cloud resource allocation scheme which can approach the optimal strategy is obtained without the knowledge of the distribution function of vehicular requests’ arrival and departure during the RL process. The simulation results show that our proposed adaptive cloud resource allocation model for vehicular cloud system can reduce the probability of delay in processing requests and achieve high system rewards in comparison with the regularly used greedy resource allocation method. The performance of the RL solution approaches that of traditional value iteration solution for our proposed adaptive cloud resource allocation model.
Energy Efficient Routing Protocols and Introduction to Wireless Recharging of Sensor Nodes in Wireless Sensor Networks

— This paper gives a brief review of the concept of sensor networks which has become feasible due to large scale development in micro electro-mechanical systems technology and wireless communications. First of all, sensor node deployment and sensor networks applications are presented, and then various issues regarding sensor node lifetime are discussed. Then, to solve sensor node issues, various energy conservation techniques, their advantages and limitations are discussed. The energy conservation techniques are compared using MATLAB and best routing protocol is shown. Lastly, wireless power transfer to replenish sensor node battery is discussed. A review of wireless power transfer history and various development in this technology is also given to solve the energy issue of sensor node. Research issues for efficiency improvement of wireless sensor network are also discussed.
Advances in Diagnostic and Therapeutic Ultrasound Imaging

Part I: Recent Advances in Ultrasound Instrumentation. Part II: 2-D/3-D Ultrasound in Vascular Imaging. Part III: 2-D/3-D Breast Ultrasound Imaging. Part IV: 3-D Cardiac Ultrasound Imaging. Part V: 3-D Prostrate Ultrasound Imaging. Part VI: Application of 3-D Therapeutic and Image Guidance Ultrasound.
Adaptive Ramp Metering Control for Urban Freeway Using Large-Scale Data

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively.
A Detecting Mechanism for Sybil Attack in Wireless Sensor Network

: Security has become a most important issue for several significant applications provided by wireless sensor networks (WSNs). The intrinsically susceptible features of WSNs employ them susceptible to a diversity of attacks. This paper has centered on how to protect from a principally destructive type of attack called Sybil attack. A Sybil node using only one physical device may produce an random number of extra node identities and can be used to interrupt standard performance of the WSNs, like multi-hop routing which is utilized to discover numerous disjoint paths among source and destination. Recently, there has been a increasing attention in leveraging WSNs to mitigate Sybil attacks. Digital certificates are a way used to show individuality, however, it is not feasible in sensor networks. This paper has proposed a Trust Based Sybil Detection (TBSD) technique to detect Sybil nodes in WSNs. The TBSD scheme is based on manipulative trust values of adjacent sensor nodes and the nodes with the trust values less than a threshold value are detected as Sybil node [4]. The feasibility of TBSD method is demonstrated systematically, while experimental results of TBSD in exposing Sybil attacks is expansively assessed equally mathematically and numerically. The acquire consequence show that the TBSD attains significant attack detection rate than existing techniques.
A ZigBee/Wi-Fi Cooperative Channel Control Method and Its Prototyping

Coexistence between ZigBee and Wi-Fi technologies, which operate within the same frequency band, is increasing with the widespread use of the IoT (Internet of Things). ZigBee devices suffer significant decrease in the sink arrival rate of packets in the presence of Wi-Fi interference. To overcome this problem, many channel control methods have been proposed. These existing methods switch only ZigBee channels to avoid interference with Wi-Fi. In contrast, we propose a cooperative channel control method for improving ZigBee packet arrival rate by controlling both the Wi-Fi and ZigBee channels. Specifically, the proposed method not only controls ZigBee devices and channels but also requests a temporary pause in the use of specific Wi-Fi channels. Finally, we show the effectiveness of the proposed method from the viewpoints of ZigBee’s packet arrival rate and applications’ satisfaction using computer simulations. In addition, the effective action of the proposed method is also demonstrated by experiments with prototyping.
Subtopic-driven Multi-Document Summarization

In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper. That is, the underlying topic can be collectively represented by all the documents in the set. Meanwhile, different documents may cover various different subtopics and the same subtopic can be across several documents. Inspired by topic model, the underlying topic of a document set can also be viewed as a collection of different subtopics of different importance. In this paper, we propose a summarization model called STDS. The model generates the underlying topic representation from both document view and subtopic view in parallel. The learning objective is to minimize the distance between the representations learned from the two views. The contextual information is encoded through a hierarchical RNN architecture. Sentence salience is estimated in a hierarchical way with subtopic salience and relative sentence salience, by considering the contextual information. Top ranked sentences are then extracted as a summary. Note that the notion of subtopic enables us to bring in additional information (e.g. comments to news articles) that is helpful for document summarization. Experimental results show that the proposed solution outperforms state-of-the-art methods on benchmark datasets.
Subnetworks mediating feedforward and feedback processes revealed by multi-area Neuropixels recordings

The visual system is organized hierarchically with feedforward and feedback pathways mediating crossarea communication. However, it is challenging to segregate these connections functionally and thus the logic of information flow remains unclear. Here, we studied this question by simultaneously recording from six visual cortical areas in awake mice with Neuropixels probes. We found two distinct neural ensembles based on their functional connectivity pattern: one ensemble is dominated by connections that drive the activity in the network (‘driver’), while another ensemble is more driven by network activity (‘driven’). ‘Driver’ neurons were more numerous in supragranular layers, whereas ‘driven’ neurons were more abundant in infragranular layers. Interestingly, although both ‘driver’ and ‘driven’ neurons were found across all cortical areas, the proportion of driven-to-driver cells systematically increased across the visual hierarchy. Strong directional information flow between these subnetworks was present during sensory stimulation, but not during spontaneous activity. The ‘driver’ ensemble showed earlier and more transient responses compared to the ‘driven’ ensemble. A rate model of the network recapitulated the link between response latency and functional connectivity. Overall, our study revealed distinct multi-area ensembles with distinct roles in information flow.
iCELIA: A Full-Stack Framework for STT-MRAM-Based Deep Learning Acceleration

A large variety of applications rely on deep learning to process big data, learn sophisticated features, and perform complicated tasks. Utilizing emerging non-volatile memory (NVM)'s unique characteristics, including the crossbar array structure and gray-scale cell resistances, to perform neural network (NN) computation is a well-studied approach in accelerating deep learning applications. Compared to other NVM technologies, STT-MRAM has its unique advantages in performing NN computation. However, the state-of-the-art research have not utilized STT-MRAM for deep learning acceleration due to its device- and architecture-level challenges. Consequently, this paper enables STT-MRAM, for the first time, as an effective and practical deep learning accelerator. In particular, it proposes a full-stack framework iCELIA spanning multiple design levels, including device-level fabrication, circuit-level enhancements, architecture-level synaptic weight quantization, and system-level accelerator design. The primary contributions of iCELIA over our prior work CELIA include a new non-uniform weight quantization scheme and much enhanced accelerator system design. The proposed framework significantly mitigates the model accuracy loss due to reduced data precision in a cohesive manner, constructing a comprehensive STT-MRAM accelerator system for fast NN computation with high energy efficiency and low cost.
Dietary nitrate supplementation as an ergogenic and therapeutic aid

i Table of contents.iii List of tables.vi List of figures.viii Symbols and abbreviations.xii Declaration, publications and communications.xv Acknowledgements.xviii Chapter 1: Introduction History of nitric oxide physiology.1 Skeletal muscle bioenergetics.2 Oxygen uptake kinetics.3 Cardiovascular health.5 Chapter 2: Literature Review Nitric Oxide.7 Nitric oxide production.7 Nitrate-nitrite-nitric oxide pathway.8 Beetroot and typical NO3 intake.10 Plasma nitrate and nitrite concentrations.11 Toxicity.12 Methaemoglobineamia.12 Nitration of proteins.12 Carcinogenic properties.12 Summary.13 Dietary Nitrate Supplementation.13 Blood pressure.13 Oxygen uptake.14 Moderate-intensity exercise.14 Severe-intensity exercise.16 Power-duration relationship.17 Exercise performance.20
Nursing care of the addicted client

ection I: Foundations for Nursing the Addicted Client Introduction: Alcohol, Tobacco, Other Drugs and Society 1. Alcohol, Tobacco, and Other Drug Addictions: Definitions, Categories, and Characteristics 2. Theoretical Perspectives for Addictions Nursing Practice 3. Standards of Care for Services to Addicted Clients Section II: Recognizing Addictions in Nursing Practice Introduction: Responding to Opportunities 4. Maternal-Child Nursing 5. Psychiatric Nursing Section III: Nursing Care of Addicted Clients Introduction: Prerequisites to Care 6. Prevention 7. Screening and Detection 8. Assessment and Diagnosis 9. Detoxification 10. Interventions and Treatment 11. Sobriety-Focused Care 12. Evaluating the Effectiveness of Nursing Care Section IV: Issues Impacting Care of Addicted Clients Introduction: Cognate Concerns 13. Cultural Competence 14. Ethical Issues in the Care of Addicted Clients 15. Legal Issues in the Care of Addicted Clients 16. Intimate Behavior of Clients, Nurses, and Family Members During Treatment and Recovery 17. Attrition 18. Interdisciplinary Collegiality 19. Research in Addictions Nursing Appendices Appendix 1: Alcohol Use Disorders Identification Test (AUDIT) (Revised to Include Drugs) Appendix 2: The CAGE and CAGE-AID Questions (CAGE Adapted to Include Drugs) Appendix 3: DAST--Drug Abuse Screening Test Appendix 4: MAST--Michigan Alcohol Screening Test Appendix 5: Brief MAST--Short Michigan Alcohol Screening Test Appendix 6: Simple Screening Instrument for Alcohol and Other Drug Abuse Appendix 7: The T-ACE Questions Appendix 8: TWEAK Test Appendix 9: Trauma Scale Screening Tool Appendix 10: Biopsychosocial Data Base and Intake Form (c) Appendix 11: Substance Abuse Assessment Instruments Inde