Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain-Computer Interface Rehabilitation System Design Based on Motor Imagery

To improve the rehabilitation efficacy for stroke patient, a brain-computer interface (BCI) rehabilitation system based on motor imagery (MI) is investigated in this paper. Firstly, the Electroencephalogram (EEG) signals are preprocessed by Laplacian spatial filter and Butterworth band pass filter with cutoff frequency of 8Hz~24Hz to enhance the interesting components of the EEG signals. Then, the logarithmic band power (LBP) features are extracted and the motion intentions classification model is established based on linear discriminant analysis (LDA) algorithm. Finally, the correct motion intentions recognition results are converted to executive command of our designed rehabilitation hand. The motion imagery dataset from OpenVibe is used to validate our system. Results show that the rehabilitation hand is driven to effectively actuate the hand to perform the pre-set actions according to the MI recognition results.
(Re)conceptualizing institutional change in sport management contexts: the unintended consequences of sport organizations’ everyday organizational life

ABSTRACT Research question: The purpose of this paper is to construct empirically grounded concepts that can aid the explanation of processes of institutional change. This effort is guided by the observation that neither ‘structure-centred' nor ‘actor-centred' explanations of institutional change are equipped to provide a satisfactory explanation of one of organizational institutionalism’s basic assumptions: that organizations are products of and produce their institutional contexts. Therefore, the focus is directed at practitioners' everyday struggle to accomplish their work, and institutional change is conceptualized as an unintended consequence of such mundane ‘muddling through'. Research methods: The text is based on video recordings of board meetings in two sport clubs over one year. Data collection resulted in approximately 33 h of observation data from 17 board meetings. Results and findings: Analysis shows how sport club boards' interpretive processes of meaning making are instances of unintentional coproduction that plant seeds for institutional change. The creation of such seeds is the result of processes of problem–solution approximation and the use of proximal institutional raw material. This shows how sport organizations are crucial actors in the creation, modification, and transformation of the institutional arrangements prescribing appropriate organizational behaviour and enforcing patterns of interest and privilege. This analysis contributes knowledge on how sport organizations unintentionally coproduce increasing government reliance on sport organizations, professionalization, and commercialization. Implications: Such knowledge can make sport organizations and policy-makers aware of how unintentional coproduction might lead to the momentum of processes adverse to their needs and wishes.
Microbial Function and Hydrochemistry within a Stratified Anchialine Sinkhole: A Window into Coastal Aquifer Interactions

Anchialine sinkholes provide insight into coastal aquifer systems and coastal mixing processes. Aquifer microbial community function is usually inferred from hydrochemical information, but there are few direct studies of microbial communities in the Floridan Aquifer. Hospital Hole is a 43 m-deep stratified sinkhole under the Weeki Wachee River, FL, with three distinct brackish layers: a hypoxic layer, a chemocline and a sulfidic anoxic layer. Illumina sequencing and bioinformatic tools were used to reconstruct metabolic functions and interactions of microbial communities in each layer. Each layer appears to originate from different parts of the coastal mixing zone and has a distinct microbial community with unique functions, which are influenced by the respective hydrochemistry. Sulfide oxidation and nitrate reduction are the most abundant functions. Syntrophy between methane oxidizers, methanogens and sulfate reducers is present. Similarities between the hydrochemistry and potential connectivity of Hospital Hole and the Floridan Aquifer coastal mixing zone suggest that microbial communities of Hospital Hole could be a surrogate for the coastal mixing zone of the aquifer in the absence of direct studies. Understanding how groundwater microbial communities react to saltwater intrusion and nutrient flux will be useful in predicting how coastal aquifer regions might react to anthropogenic change.
Joint Inventory, Pricing, and Advertising Decisions with Surplus and Stockout Loss Aversions

The newsvendor models considering decision-makers’ behavioral factors remain a fruitful research area in operation management field in past decade. In this paper, we further extend the current literatures to look into joint inventory, pricing, and advertising decisions considering loss aversion effects under the newsvendor setting. The purpose is to explore how the loss aversions affect the optimal policy of order quantity, price, and advertising effort level. We present an integrated utility model to measure both economic payoff and loss aversion utility of the newsvendor, where surplus loss aversion and stockout loss aversion are first separately defined and quantified. Then, we analyze the optimal solution conditions of the integrated model under exogenous and endogenous price cases, respectively. Under exogenous price case, we find that the uniquely optimal policy exists and is presented in the closed form. Under endogenous price case, the optimal policy is determined under mild conditions; we also provide the solutions when order quantity factor or advertising effort level is fixed in this case. In addition, the sensitivity analysis shows that the loss aversions affect the optimal decisions of order quantity, price, and advertising effort level in a systematic way.
Aerial Sensing and Characterization of Three-Dimensional RF Fields

Beyond military, hobby and recreational use, Unmanned Aerial Vehicles (UAVs) and the potential they represent are being considered seriously for a variety of commercial applications. While a number of regulatory, safety and privacy issues remain to be resolved, several interesting technical challenges have presented themselves. In particular, we predict a trend in which commercial UAVs will move from point-topoint radio systems for command, control and telemetry to using commercial cellular networks, primarily because such networks offer broad coverage. Commercial services have already emerged to provide cloud-based UAV control via cellular networks. But a significant problem remains – these networks were planned, designed and engineered for groundbased terminal devices. The skies are poorly characterized in terms of cellular coverage, leading to risks associated with planning and flying missions into the radio unknown. Prompted by the UAV opportunity, an examination of cellular network planning – from an RF perspective – reveals that the models used are likewise biased by the ground-based assumption. In this research, we focus on the challenges of characterizing cellular and other multi-node networks in three dimensions, leading to a re-consideration of how measurement and modeling can be beneficially combined. This work suggests potential improvements in the speed and accuracy of multi-node radio network planning and deployment with application to UAVs, hastily-formed (e.g., emergency) networks, and general network design and optimization.
Testing the Language Melody Game An ERP , perception and production study of L 2 acquisition of the Swedish word accent – grammar association

Previous research has shown that L1 speakers use Swedish word accents to predict what linguistic material is to follow. This study investigated how L2 learners of Swedish process the word accent – suffix association. Low to intermediate level L2 learners trained this association for two weeks, using the Language Melody Game. Before and after training, participants took part in a preand post-test, in which data on production (read speech), perception and ERPs were collected. The perception test consisted of auditory stimuli with valid and invalid accent – suffix combinations, and the participants were asked about the meaning of the stimuli. After training, a pre-activation negativity (PrAN) effect increased, indicating that the participants used the accents as predictors more extensively after training. A LAN was also found for invalid stimuli, indicating that they had acquired the tone-suffix association rule. However, no P600 was found, suggesting that the participants did not reprocess the ungrammaticality, and relied more on the suffix itself. This was also seen in response times in the posttest, where invalidity did not yield longer response times, as it does for L1 speakers. The perception training also had an effect on production of word accents.
The Expert Systems Business: How It Grew and Died

This article describes the computer environment in the 1980s, the birth of the expert systems software business, the proliferation of companies created to sell expert systems products and services, their various products and business strategies, and finally their collapse and demise in the early 1990s. We describe the marketing strategies adopted by various vendors and the various market niches that evolved during the late 1980s and early 1990s. We offer speculation on the reasons for the growth and collapse of the expert systems market in the 1990s.
On commutators of compact operators via block tridiagonalization: generalizations and limitations of Anderson's approach

We offer a new perspective and some advances on the 1971 Pearcy--Topping problem: Is every compact operator a commutator of compact operators? Our goal is to analyze and generalize the 1970's work in this area of Joel Anderson combined with the work of the last named author of this paper. We reduce the general problem to a simpler sequence of finite matrix equations with norm constraints, while at the same time developing strategies for counterexamples. Our approach is to ask which compact operators $T$ are commutators $AB-BA$ of compact operators $A,B$; and to analyze the implications of Joel Anderson's contributions to this problem, which will yield a generalization of his method. By extending the techniques of Anderson [1] we obtain new classes of operators that are commutators of compact operators beyond those obtained in [17] and [2]. And by employing the techniques of the last named author [22], we found obstructions to extending Anderson's techniques in terms of certain constraints for $T$, with special focus on when $T$ is a strictly positive compact diagonal operator. Some of these constraints involve general universal block tridiagonal matrix forms for operators, and some involve $\mathcal{B(H)}$-ideal constraints. And in terms of these matrix forms, we give some equivalences, some sufficient conditions and some necessary conditions for this Pearcy--Topping problem and its various offshoots to hold true. These matrix forms are a sparsification of matrix representations of an operator (an increase in the proportion of zeros in its corners by a change of basis) and we measure the support density of these forms. And finally we provide some necessary conditions for the Pearcy--Topping problem involving singular numbers and $\mathcal{B(H)}$-ideal constraints.
Improved GPS Positioning for Motor Vehicles Through Map Matching

Absolute positioning systems, especially the Global P o sitioning System (GPS), are playing an increasing role in the tracking of motor vehicles. Such systems, however were net designed to specifically track motor vehicles. Apart from the start and finish of journeys, motor vehicles are restricted to the mad network; a fact not reflected by the position measurements. Techniques exist for carrecting the measurements according to a map of the network but a mathematical framework for this correction process is lacking. The author has developed such a framework resulting in a map-aided estimation process that takes into account the measurement noise statistics to optimally translate the raw position measurements onto the road network. The theoratical performance of this estimator is derived and compared with that achieved using GPS measurements. The effect that Selective Availability (SA) has on the map-aided estimator is analysed and a technique for reducing the effect of SA is demonstrated In addition to the accuracy improvements, the map-aided positioning framework also provides a means for readily incorporationg other sources of positioning information. Driver bchaviour, mad type, and vehicle dynamics can be used to make further improvements in positioning accuracy and road identitication. ’
3D-EDM: Early Detection Model for 3D-Printer Faults

—With the advent of 3D printers in different price ranges and sizes, they are no longer just for professionals. However, it is still challenging to use a 3D printer perfectly. Especially, in the case of the Fused Deposition Method, it is very difﬁcult to perform with accurate calibration. Previous studies have suggested that these problems can be detected using sensor data and image data with machine learning methods. However, there are difﬁculties to apply the proposed method due to extra installation of additional sensors. Considering actual use in the future, we focus on generating the lightweight early detection model with easily collectable data. Proposed early detection model through Convolutional Neural Network shows signiﬁcant fault classiﬁcation accuracy with 96.72% for the binary classiﬁcation task, and 93.38% for multi-classiﬁcation task respectively. By this research, we hope that general users of 3D printers can use the printer accurately.
An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing

Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic meanings, and other task specific attributes. Search in the sentence space is realized by word-level edit operations including insertion, replacement, and deletion. However, such objective function is manually designed with multiple components. Although previous work has shown maximizing this objective yields good performance in terms of true measure of success (i.e. BLEU and iBLEU), the objective landscape is considered to be non-smooth with significant noises, posing challenge for optimization. In this dissertation, we address the research problem of smoothing the noise in the heuristic search objective by learning to model the search dynamics. Then, the learned model is combined with the original objective function to guide the search in a bootstrapping fashion. Experimental results show that the learned models combined with the original search objective can indeed provide a smoothing effect, improving the search performance by a small margin.
Lie algebra actions on module categories for truncated shifted yangians

We develop a theory of parabolic induction and restriction functors relating modules over Coulomb branch algebras, in the sense of Braverman-Finkelberg-Nakajima. Our functors generalize Bezrukavnikov-Etingof’s induction and restriction functors for Cherednik algebras, but their definition uses different tools. After this general definition, we focus on quiver gauge theories attached to a quiver $\Gamma $ . The induction and restriction functors allow us to define a categorical action of the corresponding symmetric Kac-Moody algebra $\mathfrak {g}_{\Gamma }$ on category $ \mathcal {O}$ for these Coulomb branch algebras. When $ \Gamma $ is of Dynkin type, the Coulomb branch algebras are truncated shifted Yangians and quantize generalized affine Grassmannian slices. Thus, we regard our action as a categorification of the geometric Satake correspondence. To establish this categorical action, we define a new class of ‘flavoured’ KLRW algebras, which are similar to the diagrammatic algebras originally constructed by the second author for the purpose of tensor product categorification. We prove an equivalence between the category of Gelfand-Tsetlin modules over a Coulomb branch algebra and the modules over a flavoured KLRW algebra. This equivalence relates the categorical action by induction and restriction functors to the usual categorical action on modules over a KLRW algebra.
Application Communities Detection in Network

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security.
Assessing Network Representations for Identifying Interdisciplinarity

Many studies have sought to identify interdisciplinary research as a function of the diversity of disciplines identified in an article’s references or citations. However, given the constant evolution of the scientific landscape, disciplinary boundaries are shifting and blurring, making it increasingly difficult to describe research within a strict taxonomy. In this work, we explore the potential for graph learning methods to learn embedded representations for research papers that encode their ‘interdisciplinarity’ in a citation network. This facilitates the identification of interdisciplinary research without the use of disciplinary categories. We evaluate these representations and their ability to identify interdisciplinary research, according to their utility in interdisciplinary citation prediction. We find that those representations which preserve structural equivalence in the citation graph are best able to predict distant, interdisciplinary interactions in the network, according to multiple definitions of citation distance.
Spectral decomposition of some non-self-adjoint operators

We consider non-self-adjoint operators in Hilbert spaces of the form $H=H_0+CWC$, where $H_0$ is self-adjoint, $W$ is bounded and $C$ is a metric operator, $C$ bounded and relatively compact with respect to $H_0$. We suppose that $C(H_0-z)^{-1}C$ is uniformly bounded in $z\in\mathbb{C}\setminus\mathbb{R}$. We define the spectral singularities of $H$ as the points of the essential spectrum $\lambda\in\sigma_{\mathrm{ess}}(H)$ such that $C(H\pm i\varepsilon)^{-1}CW$ does not have a limit as $\varepsilon\to0^+$. We prove that the spectral singularities of $H$ are in one-to-one correspondence with the eigenvalues, associated to resonant states, of an extension of $H$ to a larger Hilbert space. Next, we show that the asymptotically disappearing states for $H$, i.e. the set of vectors $\varphi$ such that $e^{\pm itH}\varphi\to0$ as $t\to\infty$, coincide with the generalized eigenstates of $H$ corresponding to eigenvalues $\lambda\in\mathbb{C}$, $\mp\mathrm{Im}(\lambda)>0$. Finally, we define the absolutely continuous spectral subspace of $H$ and show that it satisfies $\mathcal{H}_{\mathrm{ac}}(H)=\mathcal{H}_{\mathrm{p}}(H^*)^\perp$, where $\mathcal{H}_{\mathrm{p}}(H^*)$ stands for the point spectrum of $H^*$. We thus obtain a direct sum decomposition of the Hilbert spaces in terms of spectral subspaces of $H$. One of the main ingredients of our proofs is a spectral resolution formula for a bounded operator $r(H)$ regularizing the identity at spectral singularities. Our results apply to Schr\"odinger operators with complex potentials.
A Deep Learning Approach to Probabilistic Forecasting of Weather

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities. This joint density is then renormalized to produce the conditional forecast distribution. In this method, probabilistic calibration testing plays the role of a regularization procedure, preventing overfitting in the second step, while effective dimensional reduction from the first step is the source of forecast sharpness. We verify the method using a 22-year 1-hour cadence time series of Weather Research and Forecasting (WRF) simulation data of surface wind on a grid.
DOMAIN 5 RESPONDING TO THE ENVIRONMENT The SOS Regulatory Network

All organisms possess a diverse set of genetic programs that are used to alter cellular physiology in response to environmental cues. The gram-negative bacterium Escherichia coli induces a gene regulatory network known as the “SOS response” following exposure to DNA damage, replication fork arrest, and a myriad of other environmental stresses. For over 50 years, E. coli has served as the paradigm for our understanding of the transcriptional and physiological changes that occur after DNA damage. In this chapter, we summarize the current view of the SOS response and discuss how this genetic circuit is regulated. In addition to examining the E. coli SOS response, we include a discussion of the SOS regulatory networks found in other bacteria to provide a broad perspective on the mechanism and diverse physiological responses that ensueto protect cells and maintain genome integrity. All organisms possess a diverse set of genetic programs that are used to alter cellular physiology in response to environmental cues. The gram-negative bacterium Escherichia coli mounts what is known as the “SOS response” following DNA damage, replication fork arrest, and a myriad of other environmental stresses. For over 50 years, E. coli has served as the paradigm for our understanding of the transcriptional and physiological changes that occur following DNA damage (1). In this chapter, we summarize the current view of the SOS response and discuss how this genetic circuit is regulated. In addition to examining the E. coli SOS response, we also include a discussion of the SOS regulatory networks in other bacteria to provide a broader perspective on how prokaryotes respond to DNA damage. OVERVIEW OF THE SOS RESPONSE In E. coli, DNA damage and replication perturbations results in the SOS response, a genetic program that transcriptionally up-regulates over 50 unlinked genes (Table 1). The term “SOS” was coined by Miroslav Radman in Received: 5 February 2008 Accepted: 17 April 2008 Posted: 25 July 2008 Supercedes previous posting at EcoSal.org. Editor: James M. Slauch, The School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Urbana, IL Citation: EcoSal Plus 2013; doi:10.1128/ ecosalplus.5. 4.3. Correspondence: Graham C. Walker: gwalker@ mit.edu Copyright: © 2013 American Society for Microbiology. All rights reserved. doi:10.1128/ecosalplus.5. 4.3 ASMScience.org/EcoSalPlus 1