Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-node Energy Policy for Wireless Sensor Networks

The application of Wireless Charging Vehicle (WCV) based wireless energy transfer technology to recharge the wireless sensor network nodes is attracted a great deal of attention recently. In this paper, a novel multi-node energy supplemental policy based on a circular cell for the wireless rechargeable sensor networks is proposed. In this policy, the WCV is used to charge the sensor node by traversal the center of the circular cells. For achieving the optimal WCV traversal path, a position distribution strategy of lower complexity for circular cells based on k-means algorithm to minimize the cluster quantity of circular cell and smallest enclosing circle algorithm to minimize the radius of each cell are given. Finally, the simulation results show that the position distribution strategy proposed in this paper can help to significantly reduce the WCV traversal time of each charge cycle.
Dedifferentiation of caudate functional connectivity and striatal dopamine transporter density predict memory change in normal aging

Significance The biological changes associated with cognitive decline in aging are complex. Age-related changes in the striatum have been hypothesized as potentially important for predicting declining memory function over the adult life span, but empirical support for this hypothesis is sparse. We provide evidence from human fMRI and PET data for the association of age-related differences in striatal function to memory decline. A reduction of specificity (i.e., dedifferentiation) in functional coupling between the caudate and regions of the default network predicted memory decline over subsequent years in older adults. An age-related reduction in density of presynaptic striatal dopamine transporters also predicted memory decline but was unrelated to functional dedifferentiation, suggesting that relations between age-related changes in striatal functions and memory are multifaceted. Age-related changes in striatal function are potentially important for predicting declining memory performance over the adult life span. Here, we used fMRI to measure functional connectivity of caudate subfields with large-scale association networks and positron emission tomography to measure striatal dopamine transporter (DAT) density in 51 older adults (age 65–86 years) who received annual cognitive testing for up to 7 years (mean = 5.59, range 2–7 years). Analyses showed that cortical–caudate functional connectivity was less differentiated in older compared with younger adults (n = 63, age 18–32 years). Unlike in younger adults, the central lateral caudate was less strongly coupled with the frontal parietal control network in older adults. Older adults also showed less “decoupling” of the caudate from other networks, including areas of the default network (DN) and the hippocampal complex. Contrary to expectations, less decoupling between caudate and the DN was not associated with an age-related reduction of striatal DAT, suggesting that neurobiological changes in the cortex may drive dedifferentiation of cortical–caudate connectivity. Reduction of specificity in functional coupling between caudate and regions of the DN predicted memory decline over subsequent years at older ages. The age-related reduction in striatal DAT density also predicted memory decline, suggesting that a relation between striatal functions and memory decline in aging is multifaceted. Collectively, the study provides evidence highlighting the association of age-related differences in striatal function to memory decline in normal aging.
A Pruned Rnnlm Lattice-Rescoring Algorithm for Automatic Speech Recognition

Lattice-rescoring is a common approach to take advantage of recurrent neural language models in ASR, where a word-lattice is generated from 1st-pass decoding and the lattice is then rescored with a neural model, and an n-gram approximation method is usually adopted to limit the search space. In this work, we describe a pruned lattice-rescoring algorithm for ASR, improving the n-gram approximation method. The pruned algorithm further limits the search space and uses heuristic search to pick better histories when expanding the lattice. Experiments show that the proposed algorithm achieves better ASR accuracies while running much faster than the standard algorithm. In particular, it brings a 4x speedup for lattice-rescoring with 4-gram approximation while giving better recognition accuracies than the standard algorithm.
Pseudo-Supervised Approach for Text Clustering Based on Consensus Analysis

In recent years, neural networks (NN) have achieved remarkable performance improvement in text classification due to their powerful ability to encode discriminative features by incorporating label information into model training. Inspired by the success of NN in text classification, we propose a pseudo-supervised neural network approach for text clustering. The neural network is trained in a supervised fashion with pseudo-labels, which are provided by the cluster labels of pre-clustering on unsupervised document representations. To enhance the quality of pseudo-labels, a consensus analysis is employed to select training samples for the neural network. The experimental results demonstrate that the proposed approach can improve the clustering performance significantly.
Separable Dictionary Learning for Convolutional Sparse Coding via Split Updates

Existing methods for constructing separable 2D dictionary filter banks approximate a set of $K$ non-separable filters via a linear combination of $R\ll K$ separable filters. This approach involves the inefficiency of learning an initial set of non-separable filters, and places an upper bound on the quality of the separable filter banks. In this paper, we propose a method to directly learn a set of $K$ separable dictionary filters from a given image training set by drawing ideas from convolutional dictionary learning (CDL) methods. We show that the separable filters obtained by our method match the performance of an equivalent number of non-separable filters. Furthermore, the computational performance of our learning method is shown to be substantially faster than a state-of-the-art non-separable CDL method for large numbers of filters or large training sets.
Policy Adaptation for Deep Reinforcement Learning-Based Dialogue Management

Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be then used as a database search constraint is added, or the policy for one domain is transferred to another domain, the dialogue state space and action sets both will change. Therefore, the model structures for different domains have to be different. This makes dialogue policy adaptation/transfer challenging. Here a multi -agent dialogue policy (MADP) is proposed to tackle these problems. MADP consists of some slot-dependent agents (S-Agents) and a slot-independent agent (G-Agent). S-Agents have shared parameters in addition to private parameters for each one. During policy transfer, the shared parameters in S-Agents and the parameters in G-Agent can be directly transferred to the agents in extended/new domain. Simulation experiments showed that MADP can significantly speed up the policy learning and facilitate policy adaptation.
Estimating the Number of Posts in Microblogging Services

Analyzing the popularity of microblogging services is of great significance in various applications. The number of posts provides novel insights into the popularity of microblogging services, and is critical to learn about the frequency of use. Existing approaches analyze this parameter by observing posts published by a large number of users, which may lead to underestimate the value since the sampled user may stop to use the service during the observing process. In this paper, we propose a novel method to estimate the number of posts in microblogging services. The basic idea behind this method is to make use of a common API provided by microblogging services, i.e., the public_timeline API. Posts sampled by this API may duplicate among multiple invocations, so the capture-recapture model can be used to estimate the total number of posts. Based on the traditional capture-recapture model, we propose an improved model to address challenges on low sampling probability and unequal sampling probability. We validate the proposed method using a real life Sina Weibo dataset, and the experimental results demonstrate the effectiveness and accuracy of our proposed method.
A combined high gain-higher order sliding mode controller for a class of uncertain nonlinear systems

The use of standard sliding mode controller, usually, leads to the appearing of an undesirable chattering phenomenon affecting the control signal. Such problem can be overcome using a higher-order sliding mode controller (HOSMC) which preserves the main properties of the standard sliding mode and deliberately increases the control smoothness. In this paper, we propose a new HOSMC for a class of uncertain multi-input multi-output nonlinear systems. Based on high gain and integral sliding mode paradigms, the established control scheme removes theoretically the chattering phenomenon and provides the stability of the control system. Numerical simulations are developed to show the effectiveness of the proposed controller when applied to solve a control problem of two water levels into a quadruple tank process.
Super-resolution of Very Low Resolution Faces from Videos

Faces appear in low-resolution video sequences in various domains such as surveillance. The information accumulated over multiple frames can help super-resolution for high magniﬁcation factors. We present a method to super-resolve a face image using the consecutive frames of the face in the same sequence. Our method is based on a novel multi-input-single-output framework with a Siamese deep network architecture that fuses multiple frames into a single face image. Contrary to existing work on video super-resolution, it is model free and does not depend on facial landmark detection that might be diﬃcult to handle for very low-resolution faces. The experiments show that the use of multiple frames as input improves the performance compared to single-inputsingle-output systems
A Video Output Method for H.265/HEVC Video and Audio IP Transmission and Its QoE

This paper exploits the idea of SCS (Switching between error Concealment and frame Skipping) for H.265/HEVC video and audio IP transmission. The method skips output of video frames with a larger ratio of lost slices than a predefined threshold. It continues frame skipping until new GOP. To assess the effectiveness of the method, we evaluate QoE through a subjective experiment. We employ two audiovisual contents and vary the number of slices in a video frame, GOP length, encoding bitrate, and the amount of background traffic. As a result, we find that the method exploiting the idea of SCS can enhance QoE for the content which has large movement.
Multi-Vehicle Motion Planning for Social Optimal Mobility-on-Demand

In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-off the demands' delays and level of violation of the rules of the road to achieve social optimum among the vehicles. Due to operating in the same environment, the interaction between the cars must be taken into account, and can induce further delays. We propose an integrated route and motion planning approach that achieves scalability with respect to the number of cars by resolving potential collision situations locally within so-called bubble spaces enclosing the conflict. The algorithms leverage the road geometries, and perform joint planning only for lead vehicles in the conflict and use queue scheduling for the remaining cars. Furthermore, a framework for storing previously resolved conflict situations is proposed, which can be use for quick querying of joint motion plans. We show the mobility-on-demand setup and effectiveness of the proposed approach in simulated case studies involving up to 10 self-driving vehicles.
Neural Confnet Classification: Fully Neural Network Based Spoken Utterance Classification Using Word Confusion Networks

This paper describes neural ConfNet classification, a novel fully neural network based spoken utterance classification method that uses word confusion networks (ConfNets). Our motivation is to establish a spoken utterance classification method that can precisely understand natural language and robustly handle automatic speech recognition (ASR) errors. Remarkable progress has been made in neural networks for accurate modeling, however, most previous methods could not handle ASR errors since they were developed for reference transcriptions. Therefore, in our work we utilized ConfNets, which are compact and efficient graph representations of ASR hypotheses. Our idea is to regard the ConfNet as a sequence of bag-of-weighted-arcs and introduce a mechanism that converts the bag-of-weighted-arcs into a continuous representation called a modified weighted sum representation. This enables us to flexibly connect ConfNets to arbitrary model structures developed for reference transcriptions. We demonstrate the effectiveness of the neural ConfNet classification in dialogue act, extended named entity, and question type classification tasks.
Delay Sensitive Virtual Network Function Placement and Routing

Network function virtualization (NFV) is a recent concept for sharing multiple resources from one or more physical network infrastructures in order to improve flexibility of design, deployment and management of networking services. Virtual Network functions (VNFs) are hosted in virtual machines for the purpose of serving a user and/or a tenant request where the order of the requested functions are as important as the service itself. Therefore, the placement, chaining and routing of VNFs have a major impact on the overall networking cost which can be translated into financial cost, latency, resources utilization or other suitable metrics. In this paper, we focus on the accumulated delay assuming multipath routing of flows and the assignment of service chains in virtual networks. We formulate the problem of placement and routing as a Mixed Integer Linear Programming (MILP) problem, considering not only the delay cost generated by serving user and/or tenant demand, but also the cost of routing using a bi-objective optimization problem. The proposed solution aims to minimize the overall delay and the simulation results show a considerable gain in performance and a better use of network resources compared to conventional shortest path routing based solutions.
Exploring Digital Transformation and Digital Culture in Service Organizations

Digital transformation changes the way how companies and individuals operate and interact with their surrounding environment. New digital channels provide service companies both opportunities and threaths. One of the valuable opportunities is to affect positively customers' service experiences. Digital Experience (DX) can be defined as meeting and exceeding changing customer expectations via digital channels, platforms and touchpoints. In this paper, we focus on exploring digital transformation and digital culture in the context of service management. The research problem of this study is: How digital transformation and digital culture are visible in service provider organizations? The main contribution of this paper is to provide findings on challenges in digitalization, digitalization patterns that exist in daily service management work and actions service employees are performing to develop and contribute to the digital culture. Multiple sources of evidence from TUPLA project industry partners and narratives on digitalization were used in data collection.
The effects on peripheral nerve damage of the application of local and systemic erythropoietin.

OBJECTIVE To investigate the histopathological and biochemical effects of topical and systemic administrations of erythropoietin (EPO) on crush and transection type peripheral nerve injuries in an experimental rat model. METHODS 128 male Wistar-Albino rats were allocated to 8 groups according to the route of administration of EPO (local/systemic) and type of peripheral nerve injury (crush/transection). groups were compared with respect to histopathological examination and number of axons as well as levels of IL-1β, IL-6, and IL-10. RESULTS Groups receiving EPO either locally or intraperitoneally revealed less scar tissue formation index, lower number of inflammatory cells, reduced number of perineural fibroblasts and increased number of axons (p < 0.001 for all). Levels of IL-1β and IL-6 were lower and IL-10 levels were higher in groups receiving EPO locally or intraperitoneally (p < 0.001). CONCLUSION Iatrogenic nerve injury has remained an area with few therapeutic options. Our results indicated that local and systemic applications of EPO might have a promising potential therapeutic agent for crush or transection type of peripheral nerve injuries. Dose, route of administration and indications should be elucidated in further prospective, randomized, controlled trials (Tab. 3, Fig. 4, Ref. 32).
3D Motion Segmentation of Articulated Rigid Bodies based on RGB-D Data

This paper addresses the problem of motion segmentation of articulated rigid bodies from a single-view RGB-D data sequence. Current methods either perform dense motion segmentation, and consequently are very computational demanding, or rely on sparse 2D feature points, which may not be sufﬁcient to represent the entire scene. In this paper, we advocate the use of 3D semi-dense motion segmentation which also bridges some limitations of standard 2D methods ( e.g . background removal). We cast the 3D motion segmentation problem into a subspace clustering problem, adding an adaptive spectral clustering that estimates the number of object rigid parts. The resultant method has few parameters to adjust, takes less time than the temporal length of the scene and requires no post-processing.
Automatically Linking Digital Signal Processing Assessment Questions to Key Engineering Learning Outcomes

To deliver on the potential outcome-based teaching and learning holds for engineering education, it is important for engineering courses to provide students with different types of deliberate practice opportunities that align to the program's learning outcomes. Working from these requirements, we increased the design and measurement intentionality of a digital signal processing (DSP) course. To align the course's learning outcomes more constructively with its assessment measures, we automated the process of classifying DSP questions according to learning outcomes by introducing a model that integrates topic modeling and machine learning. In this work, we explored the effect of pre-processing procedures in terms of stopword selection and word co-occurrence redundancy issue in question classification inferences. In this work, we proposed a customized variant of the Word Network Topic Model, q-WNTM, which is able to use its pre-classified DSP questions to reliably classify new questions according to the course's learning outcomes.
Motion Estimation and Segmentation of Natural Phenomena

Dense motion estimation for dynamic natural phenomena (water, smoke, fire, etc. ) is a significant open problem. Current approaches tend to be either general, giving poor results, or specialise in one phenomenon and fail to generalise. Segmentation of phenomena is also an open problem. This paper describes an approach to estimate dense motion for dynamic phenomena that is simple, general, and which yields state of the art results. We use our dense motion field to segment phenomena to above state of the art levels. We demonstrate our contributions using lab-based video, video from a public dataset, and from the internet.
Recall Neural Network for Source Separation

This paper presents a novel memory-augmented neural network for single-channel source separation. We propose a recall neural network (RCNN) where a couple of external memories are realized for sequence-to-sequence learning based on an encoder and a decoder. These memories are learned in a two-pass sensing procedure where the mixed signal is encoded and then decoded (or recalled) as context vectors by using a bidirectional long short-term memory (LSTM) and a LSTM, respectively. These context vectors are integrated in a gating layer. A set of attention weights are calculated to attend the hidden state of decoder to implement a recurrent neural network for source separation. A gated attention mechanism is carried out to fulfill a specialized memory network. The regression errors due to two passes of sensing procedure and one pass of gated attention are jointly minimized to estimate the weight parameters of different components in different layers. The experiments on multi-speaker speech enhancement show that the proposed RCNN consistently outperforms LSTM and neural Turing machine in different settings.
Moderate Exercise Enhances Endothelial Progenitor Cell Exosomes Release and Function

Purpose Exercise has cardiovascular benefits which might be related to endothelial progenitor cells (EPC). Meanwhile, there is evidence suggesting that EPC-derived exosomes (EPC-EX) promote vascular repair and angiogenesis through their carried microRNA (miR)-126. In this study, we investigated whether exercise could increase the levels of circulating EPC-EX and their miR-126 cargo, and by which promote the protective function of EPC-EX on endothelial cells (EC). Methods Plasma EPC-EX from sedentary, low, or moderate exercise mice, respectively, denoted as EPC-EXS, EPC-EXL, and EPC-EXM, were isolated using microbead-based sorting techniques and characterized by nanoparticle tracking analysis, Western blot, and quantitative real-time polymerase chain reaction assessments of biomarkers and miR-126. High glucose (25 mM) with hypoxia (1% O2) was used for inducing an EC injury model. The injured EC were treated by coculturing with vehicle, EPC-EXS, EPC-EXL, EPC-EXM, or EPC-EXM + anti–miR-126. After that, EC were used for flow cytometry analysis of apoptosis, assessments of tube formation and migration, and measurements of miR-126 level and its downstream sprouty-related protein-1 (SPRED1) and vascular endothelial growth factor (VEGF). Results 1) Isolated EPC-EX positively expressed exosomal markers (CD63 and Tsg101) and EPC markers (CD34 and VEGFR2). 2) Exercise intensity dependently elevated plasma level of EPC, EPC-EX/EPC ratio, and miR-126 expression in EPC and EPC-EX. 3) Injured EC displayed apoptosis increment, angiogenic dysfunction and miR-126 reduction. 4) EPC-EXM had better effects than EPC-EXS and EPC-EXL on alleviating those changes of injured EC, accompanied with SPRED1 downregulation and VEGF upregulation. 5) The effects of EPC-EXM were abolished by miR-126 knockdown. Conclusions Our data demonstrate that exercise can increase EPC-EX release and miR-126 level and enhance the effects of EPC-EX on protecting EC against injury through the SPRED1/VEGF pathway.