Miracle-dz/UniFAR-FTUs huggingface dataset:
Freezing of gait in Parkinson's disease: a perceptual cause for a motor impairment?

While freezing of gait (FOG) is typically considered a motor impairment, the fact that it occurs more frequently in confined spaces suggests that perception of space might contribute to FOG. The present study evaluated how doorway size influenced characteristics of gait that might be indicative of freezing. Changes in spatiotemporal aspects of gait were evaluated while walking through three different-sized doorways (narrow (0.675 m wide × 2.1 m high), normal (0.9 m wide × 2.1 m high) and wide (1.8 m wide × 2.1 m high)) in three separate groups: 15 individuals with Parkinson's disease confirmed to be experiencing FOG at the time of test; 16 non-FOG individuals with Parkinson's disease and 16 healthy age-matched control participants. Results for step length indicated that the FOG group was most affected by the narrow doorway and was the only group whose step length was dependent on upcoming doorway size as indicated by a significant interaction of group by condition (F(4,88)=2.73, p<0.034). Importantly, the FOG group also displayed increased within-trial variability of step length and step time, which was exaggerated as doorway size decreased (F(4,88)=2.99, p<0.023). A significant interaction between group and condition for base of support measures indicated that the non-FOG participants were also affected by doorway size (similar to Parkinson's disease FOG) but only in the narrow doorway condition. These results support the notion that some occurrences of freezing may be the result of an underlying perceptual mechanism that interferes with online movement planning.
A Collision‐resilient Flying Robot

Flying robots that can locomote efficiently in GPS‐denied cluttered environments have many applications, such as in search and rescue scenarios. However, dealing with the high amount of obstacles inherent to such environments is a major challenge for flying vehicles. Conventional flying platforms cannot afford to collide with obstacles, as the disturbance from the impact may provoke a crash to the ground, especially when friction forces generate torques affecting the attitude of the platform. We propose a concept of resilient flying robots capable of colliding into obstacles without compromising their flight stability. Such platforms present great advantages over existing robots as they are capable of robust flight in cluttered environments without the need for complex sense and avoid strategies or three‐dimensional mapping of the environment. We propose a design comprising an inner frame equipped with conventional propulsion and stabilization systems enclosed in a protective cage that can rotate passively thanks to a three‐axis gimbal system, which reduces the impact of friction forces on the attitude of the inner frame. After addressing important design considerations thanks to a collision model and validation experiments, we present a proof‐of‐concept platform, named GimBall, capable of flying in various cluttered environments. Field experiments demonstrate the robot's ability to fly fully autonomously through a forest while experiencing multiple collisions.
SCHEDULING OF DIRECT LOAD CONTROL USING GENETIC PROGRAMMING

A modified Genetic Programming (GP) called block deepening GP (BDGP) is proposed in this paper to optimize the scheduling of direct load control (DLC). A search scheme similar to the breadth first search approach in artificial intelligence is adopted in the BDGP to search for the optimal scheduling arrangements. BDGP is composed of a master GP and a slave GP. As the master GP evaluates the status combination of all load groups at every time step, it calls upon the slave GP to simultaneously look ahead an additional D steps to evaluate the best possible load difference that could result. The best status combinations in the following D steps associated with the status combination under evaluation are determined globally in the following D-step block. Since the proposed BDGP optimizes the scheduling for DLC aiming to minimize the load difference in the next time step and the following D time steps, the scheduling results obtained by BDGP are closer to the globally optimal solution.
Adaptive Multi-Agent Systems for Constrained Optimization

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.
Microvesicles preparation from mesenchymal stem cells

Background: Extracellular vesicles are particles ranged from 30 nm to 5μm and subcategorized into three groups; exosomes, microvesicles and apoptotic bodies, each of which have different biological impact. Lack of a standard method for the detection and isolation of MVs has led to a challenging issue that is a worth considering. In this study, we isolated MVs from the conditioned medium of UC-MSCs by four different schemes of ultracentrifugation. Methods: We examined the efficacy of differential centrifugation ranging from 10,000×g to 60,000×g on UCMSCs- derived microvesicles yield and purity. The fractions were evaluated by Dynamic Light Scattering (DLS) method, total protein quantification and flow cytometry. Results: UC-MSCs were spindle cells that adhered to plastic culture flasks. These cells expressed MSC markers such as CD44 and CD73, whereas were negative for hematopoietic markers CD45 and CD34. UC-MSCparticles were successfully isolated. Particles were heterogeneous vesicles of approximately 50 to 1250 nm in diameter that bear the surface-expressed molecules UC-MSCs such as; CD90, CD106, CD166 and CD44, and negative for CD34, CD63, and CD9. According to the results of DLS method, centrifugation at 10,000, 20,000, 40,000 and 60,000 ×g, all gave MVs of less than 1000 nm. It is of notion that only at the centrifugation rates of 40,000 and 60,000×g, particles of less than 100 nm in diameter were also obtained. Conclusion: The choice of exact speed greatly influences the purity of MVs and their yield. Our findings indicate that centrifugation at 20,000×g is appropriate for the purification of UC-MSC-MVs.
A gm/ID-based synthesis tool for pipelined analog to digital converters

This paper presents a circuit-level synthesis tool for pipelined ADCs by consulting the circuit-design experience. A top-down systematic design procedure for a conventional pipelined ADC is summarized. In order to decrease the design period for analog circuit sizing, a design automation methodology based on gm/ID concept is manipulated in the synthesis process. With the proposed design automation flow for pipelined ADCs, the developed synthesis tool can produce satisfactory circuit performance within reasonable simulation time.
Automated 3D model‐based free‐form object recognition

In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.
Competence and Resilience in Development

Abstract: The first three waves of research on resilience in development, largely behavioral in focus, contributed a compelling set of concepts and methods, a surprisingly consistent body of findings, provocative issues and controversies, and clues to promising areas for the next wave of resilience research linking biology and neuroscience to behavioral adaptation in development. Behavioral investigators honed the definitions and assessments of risk, adversity, competence, developmental tasks, protective factors, and other key aspects of resilience, as they sought to understand how some children overcome adversity to do well in life. Their findings implicate fundamental adaptive systems, which in turn suggest hot spots for the rising fourth wave of integrative research on resilience in children, focused on processes studied at multiple levels of analysis and across species.
Evolutionary Extreme Learning Machine Based Weighted Nearest-Neighbor Equality Classification

Feature significance plays an important role in the classification tasks. The performance of a classifier would be degraded due to the existence of the irrelevant features, which are often inevitable in the real applications. In order to distinguish the impacts implicated in the features and improve the performances of the classification methods, this paper presents a hybrid learning approach, entitled evolutionary extreme learning machine based weighted nearest-neighbor equality algorithm (EE-WNNE). In such method, the measure of the significance levels of the features are induced by the weights on the related links associated with the individual input nodes in the evolutionary extreme learning machine (E-ELM) algorithm. These feature weights are utilized to implement a weighted nearest-neighbor equality method to perform the subsequent classification tasks. Systematic experimental results demonstrate that the proposed approach generally outperform many state-of-the-art classification techniques.
Dividend drop ratios and tax theory: An intraday analysis under different tax and price quoting regimes

We calculate dividend drop ratios over periods with changing quotation and taxation frameworks to assess the validity of competing explanations. Using intraday prices adjusted for non-trading, we provide a more accurate picture of price changes due to dividend payments than those produced in previous literature. Intraday estimates for dividend drop ratios are consistently higher than those calculated with end of day prices. Further findings indicate that stocks trading ex-dividend, on average, underperform the market over the following month. We attribute this phenomenon to dividend capture trading by tax advantaged and tax indifferent market participants.
Enhanced Sympathetic Arousal in Response to fMRI Scanning Correlates with Task Induced Activations and Deactivations

It has been repeatedly shown that functional magnetic resonance imaging (fMRI) triggers distress and neuroendocrine response systems. Prior studies have revealed that sympathetic arousal increases, particularly at the beginning of the examination. Against this background it appears likely that those stress reactions during the scanning procedure may influence task performance and neural correlates. However, the question how sympathetic arousal elicited by the scanning procedure itself may act as a potential confounder of fMRI data remains unresolved today. Thirty-seven scanner naive healthy subjects performed a simple cued target detection task. Levels of salivary alpha amylase (sAA), as a biomarker for sympathetic activity, were assessed in samples obtained at several time points during the lab visit. SAA increased two times, immediately prior to scanning and at the end of the scanning procedure. Neural activation related to motor preparation and timing as well as task performance was positively correlated with the first increase. Furthermore, the first sAA increase was associated with task induced deactivation (TID) in frontal and parietal regions. However, these effects were restricted to the first part of the experiment. Consequently, this bias of scanner related sympathetic activation should be considered in future fMRI investigations. It is of particular importance for pharmacological investigations studying adrenergic agents and the comparison of groups with different stress vulnerabilities like patients and controls or adolescents and adults.
Trading Activity and Bid-Ask Spreads of Individual Equity Options

We empirically examine the impact of trading activities on the liquidity of individual equity options measured by the proportional bid-ask spread. There are three main findings. First, the option return volatility, defined as the option price elasticity times the stock return volatility, has a much higher power in explaining the spread variations than the commonly considered liquidity determinants such as the stock return volatility and option trading volume. Second, after controlling for all the liquidity determinants, we find a maturity-substitution effect due to expiration cycles. When medium-term options (60-90Â days maturity) are not available, traders use short-term options as substitutes whose higher volume leads to a smaller bid-ask spread or better liquidity. Third, we also find a moneyness-substitution effect induced by the stock return volatility. When the stock return volatility goes up, trading shifts from in-the-money options to out-of-the-money options, causing the latter's spread to narrow.
Exosomes for Intramyocardial Intercellular Communication

Cross-talk between different cell types plays central roles both in cardiac homeostasis and in adaptive responses of the heart to stress. Cardiomyocytes (CMs) send biological messages to the other cell types present in the heart including endothelial cells (ECs) and fibroblasts. In turn, CMs receive messages from these cells. Recent evidence has now established that exosomes, nanosized secreted extracellular vesicles, are crucial mediators of such messages. CMs, ECs, cardiac fibroblasts, and cardiac progenitor cells (CPCs) release exosomes carrying nonrandom subsets of proteins, lipids, and nucleic acids present in their cells of origin. Exosomes secreted from CMs are internalized by fibroblasts and regulate gene expression in these cells as well as in ECs. CPC-derived exosomes protect CMs against apoptosis while also stimulating angiogenesis. They are rich in cardioprotective and proangiogenic microRNAs such as miR-146, miR-210, and miR-132. When injected into infracted hearts in vivo, CPC-derived exosomes reduce infarct size and improve cardiac function. Thus, exosomes are emerging both as key mediators of intercellular communication in the heart and as therapeutic candidates for heart disease.
Social communication of predator-induced changes in Drosophila behavior and germ line physiology

Behavioral adaptation to environmental threats and subsequent social transmission of adaptive behavior has evolutionary implications. In Drosophila, exposure to parasitoid wasps leads to a sharp decline in oviposition. We show that exposure to predator elicits both an acute and learned oviposition depression, mediated through the visual system. However, long-term persistence of oviposition depression after predator removal requires neuronal signaling functions, a functional mushroom body, and neurally driven apoptosis of oocytes through effector caspases. Strikingly, wasp-exposed flies (teachers) can transmit egg-retention behavior and trigger ovarian apoptosis in naive, unexposed flies (students). Acquisition and behavioral execution of this socially learned behavior by naive flies requires all of the factors needed for primary learning. The ability to teach does not require ovarian apoptosis. This work provides new insight into genetic and physiological mechanisms that underlie an ecologically relevant form of learning and mechanisms for its social transmission. DOI: http://dx.doi. org/10.7554/eLife.07423. 001
The representational adequacy of Hybrid

The Hybrid system (Ambler et al. 2002b), implemented within Isabelle/HOL, allows object logics to be represented using higher order abstract syntax (HOAS), and reasoned about using tactical theorem proving in general, and principles of (co)induction in particular. The form of HOAS provided by Hybrid is essentially a lambda calculus with constants. Of fundamental interest is the form of the lambda abstractions provided by Hybrid. The user has the convenience of writing lambda abstractions using names for the binding variables. However, each abstraction is actually a definition of a de Bruijn expression, and Hybrid can unwind the user's abstractions (written with names) to machine friendly de Bruijn expressions (without names). In this sense the formal system contains a hybrid of named and nameless bound variable notation. In this paper, we present a formal theory in a logical framework, which can be viewed as a model of core Hybrid, and state and prove that the model is representationally adequate for HOAS. In particular, it is the canonical translation function from λ-expressions to Hybrid that witnesses adequacy. We also prove two results that characterise how Hybrid represents certain classes of λ-expression. We provide the first detailed proof to be published that proper locally nameless de Bruijn expressions and α-equivalence classes of λ-expressions are in bijective correspondence. This result is presented as a form of de Bruijn representational adequacy, and is a key component of the proof of Hybrid adequacy. The Hybrid system contains a number of different syntactic classes of expression, and associated abstraction mechanisms. Hence, this paper also aims to provide a self-contained theoretical introduction to both the syntax and key ideas of the system. Although this paper will be of considerable interest to those who wish to work with Hybrid in Isabelle/HOL, a background in automated theorem proving is not essential.
INPUT-DRIVEN LANGUAGE LEARNING

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.
An abrupt change detection heuristic with applications to cyber data attacks on power systems

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
Hard Learning the Easy Way: Backpropagation with Deformation

T he backpropagation algorithm for feed-forwa rd layer ed neural networks is ap plied to a problem domain of vari abl e difficulty. The algori thm in its basic form is shown to be very sens itive to stepsize and momentum parameters as problem di fficulty is increased. To counter this we suggest a way of changing them during learning for a faster and more stable gradient descent . A technique for the deform ation of the error surface is introduced as a way of using t he algorithm to learn hard problem domai ns by gr adually changing the shape of the error sur face from a gentle to the final craggy form . This deformation procedure is applied to a second problem domain and is shown to improve the learn ing performan ce by gradually increasing the difficulty of th e problem domain so that the net may build upon past exp erience, rather than being subjecte d to a complicated set of assoc iat ions from the st ar t .
deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

Summary In metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy. Availability the C++ source code of deSPI is available at https://github.com/hitbc/deSPI Contact ydwang@hit.edu. cn Supplementary information Supplementary data are available at Bioinformatics online.
Cellphone Addiction and Academic Stress Among University Students in Thailand

Two phenomena that are ubiquitous in universities today are cellphones and stress. The purpose of this study is to assess students’ perceptions of cellphone addiction and academic stress. A survey was conducted among 243 students at a university in Thailand. Results revealed that there was no difference in cellphone addiction and academic stress for class and gender but there was a difference by major. Results also indicated a mild correlation between cellphone addiction and academic stress. This relationship between cellphone addiction and academic stress is moderated strongly by gender with the relationship being stronger among men than among women.
Building up Shared Knowledge with Logical Information Systems

Logical Information Systems (LIS) are based on Logical Con- cept Analysis, an extension of Formal Concept Analysis. This paper de- scribes an application of LIS to support group decision. A case study gathered a research team. The objective was to decide on a set of po- tential conferences on which to send submissions. People individually used Abilis, a LIS web server, to preselect a set of conferences. Start- ing from 1041 call for papers, the individual participants preselected 63 conferences. They met and collectively used Abilis to select a shared set of 42 target conferences. The team could then sketch a publication planning. The case study provides evidence that LIS cover at least three of the collaboration patterns identified by Kolfschoten, de Vreede and Briggs. Abilis helped the team to build a more complete and relevant set of information (Generate/Gathering pattern); to build a shared un- derstanding of the relevant information (Clarify/Building Shared Un- derstanding); and to quickly reduce the number of target conferences (Reduce/Filtering pattern).