Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Structures for Cyclic Convolution Using Improved First-Order Moment Algorithm

This paper first presents a decomposition scheme to reduce the computation time and make the first-order moment-based cyclic convolution well suited for hardware implementation. By decomposing the fixed convolution kernel into similar subparts and using their preprocessing results as control signals, each subpart of cyclic convolution can be calculated with a basic computing substructure. Due to the flexibility of decomposition, a trade-off between computation time and hardware complexity exists. And for a pair of fixed decomposition coefficients, the similarity among subparts leads to the time-efficient structure and the area-efficient structure for cyclic convolution without limitation on the convolution length N and the word length L. Since the basic computing substructure only contains a simple control module, several circularly right-shift registers and N accumulation units, there is no requirement for multipliers and large memory. Comparisons in terms of area-delay product, area-time product and power consumption with the existing memory-based structures have been made to demonstrate the efficiency and effectiveness of the proposed structures. Using the same metrics, the comparison results further show significant improvement of the proposed designs over the previous first-order moment-based structure.
Complex random vectors and ICA models: identifiability, uniqueness, and separability

In this paper, the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solving real-valued ICA problems to complex-valued models. Relevant properties of complex random vectors are described in order to extend the Darmois-Skitovich theorem for complex-valued models. This theorem is used to construct a proof of a theorem for each of the above ICA model concepts. Both circular and noncircular complex random vectors are covered. Examples clarifying the above concepts are presented
Mapping DNA cleavage by the Type ISP restriction-modification enzymes following long-range communication between DNA sites in different orientations

The prokaryotic Type ISP restriction-modification enzymes are single-chain proteins comprising an Mrr-family nuclease, a superfamily 2 helicase-like ATPase, a coupler domain, a methyltransferase, and a DNA-recognition domain. Upon recognising an unmodified DNA target site, the helicase-like domain hydrolyzes ATP to cause site release (remodeling activity) and to then drive downstream translocation consuming 1–2 ATP per base pair (motor activity). On an invading foreign DNA, double-strand breaks are introduced at random wherever two translocating enzymes form a so-called collision complex following long-range communication between a pair of target sites in inverted (head-to-head) repeat. Paradoxically, structural models for collision suggest that the nuclease domains are too far apart (>30 bp) to dimerise and produce a double-strand DNA break using just two strand-cleavage events. Here, we examined the organisation of different collision complexes and how these lead to nuclease activation. We mapped DNA cleavage when a translocating enzyme collides with a static enzyme bound to its site. By following communication between sites in both head-to-head and head-to-tail orientations, we could show that motor activity leads to activation of the nuclease domains via distant interactions of the helicase or MTase-TRD. Direct nuclease dimerization is not required. To help explain the observed cleavage patterns, we also used exonuclease footprinting to demonstrate that individual Type ISP domains can swing off the DNA. This study lends further support to a model where DNA breaks are generated by multiple random nicks due to mobility of a collision complex with an overall DNA-binding footprint of ∼30 bp.
Further investigation into multilingual training and adaptation of stacked bottle-neck neural network structure

Multilingual training of neural networks for ASR is widely studied these days. It has been shown that languages with little training data can benefit largely from multilingual resources. We have evaluated possible ways of adaptation of multilingual stacked bottle-neck hierarchy to target domain. This paper extends our latest work and focuses on the impact certain aspects have on the performance of an adapted neural network feature extractor. First, the performance of adapted multilingual networks preliminarily trained on different languages is studied. Next, the effect of different target units - phonemes vs. triphone states - used for multilingual NN training is evaluated. Then the impact of an increasing number of languages used for multilingual NN training is investigated. Here the condition of constant amount of data is added to separately control the influence of larger language variability and larger amount of data. The effect of adding languages from a different domain is also evaluated. Finally a study is performed where a language with the phonetic structure similar to the target's one is added to multilingual training data.
Optimal Temporal Logic Control for Deterministic Transition Systems With Probabilistic Penalties

We consider an optimal control problem for a weighted deterministic transition system required to satisfy a constraint expressed as a linear temporal logic (LTL) formula over its labels. By assuming that the executions of the system incur time-varying penalties modeled as Markov chains, our goal is to minimize the expected average cumulative penalty incurred between consecutive satisfactions of a desired property. Using concepts from theoretical computer science, we provide two solutions to this problem. First, we derive a provably correct optimal strategy within the class of strategies that do not exploit values of penalties sensed in real time. Second, we show that by taking advantage of locally sensing the penalties, we can construct heuristic strategies leading to lower collected penalty. While still ensuring satisfaction of the LTL constraint, we cannot guarantee optimality in the latter case. We provide a user-friendly implementation of the proposed algorithms and analysis of two case studies.
Caring for Clients and Families With Anxiety

This study elucidated Japanese home care nurses’ experiences of supporting clients and families with anxiety. We interviewed 10 registered nurses working in home care agencies and analyzed the data using grounded theory to derive categories pertaining to the nurses’ experiences of providing care. We conceptualized nurses’ approaches to caring for anxiety into three categories: First, they attempted to reach out for anxiety even when the client/family did not make it explicit; second, they tried to alter the outlook of the situation; and third, they created comfort in the lives of the client/family. The conceptualizations of nurses’ strategies to alleviate client/family anxiety may reflect Japanese/Eastern cultural characteristics in communication and their view of the person and social care system, but these conceptualizations may also inform the practice of Western nurses by increasing awareness of skills they may also have and use.
OR FORUM - Catching the "Network Science" Bug: Insight and Opportunity for the Operations Researcher

Recent efforts to develop a universal view of complex networks have created both excitement and confusion about the way in which knowledge of network structure can be used to understand, control, or design system behavior. This paper offers perspective on the emerging field of “network science” in three ways. First, it briefly summarizes the origins, methodological approaches, and most celebrated contributions within this increasingly popular field. Second, it contrasts the predominant perspective in the network science literature (that abstracts away domain-specific function and instead focuses on graph-theoretic measures of system structure and dynamics) with that of engineers and practitioners of decision science (who emphasize the importance of network performance, constraints, and trade-offs). Third, it proposes optimization-based reverse engineering to address some important open questions within network science from an operations research perspective. We advocate for increased, yet cautious, partic.
Spatio-Temporal Analysis of Throughput for Single-Hop CSMA Networks

Throughput model for non-persistent CSMA networks which was proposed by Kleinrock and Tobagi has been widely used, although it provides a loose lower bound when nodes are distributed in a large area because the analysis assumes that the propagation delay between each pair of users equals to the largest propagation delay in the network. We present a throughput analysis which considers the spatial distribution of nodes. We obtain a simple throughput expression which predicts throughput with an 8% maximum error whereas the earlier model results in a 44% error when the maximum propagation delay equals to the packet transmission time.
Wnt/β-catenin signaling regulates nephron induction during mouse kidney development

Mammalian nephrons form as a result of a complex morphogenesis and patterning of a simple epithelial precursor, the renal vesicle. Renal vesicles are established from a mesenchymal progenitor population in response to inductive signals. Several lines of evidence support the sequential roles of two Wnt family members, Wnt9b and Wnt4, in renal vesicle induction. Using genetic approaches to specifically manipulate the activity of β-catenin within the mesenchymal progenitor pool in mice, we investigated the potential role of the canonical Wnt pathway in these inductive events. Progenitor-cell-specific removal of β-catenin activity completely blocked both the formation of renal vesicles and the expected molecular signature of an earlier inductive response. By contrast, activation of stabilizedβ -catenin in the same cell population causes ectopic expression of mesenchymal induction markers in vitro and functionally replaces the requirement for Wnt9b and Wnt4 in their inductive roles in vivo. Thus, canonical Wnt signaling is both necessary and sufficient for initiating and maintaining inductive pathways mediated by Wnt9b and Wnt4. However, the failure of induced mesenchyme with high levels of β-catenin activity to form epithelial structures suggests that modulating canonical signaling may be crucial for the cellular transition to the renal vesicle.
System identification for output-dependent bounded noises and its application in learning personalized thermal comfort model

When the output observation noise is output-dependent, identifying the unknown system parameters becomes challenging. Traditional methods based on Mean Square Error, even the ones with corrections still have biased estimations in this case. Many practical cases such as bounded sensor, uncertainty of expression in human involved system identification, and even in physiological or biological model identification actually have this problem. In this paper, some algorithms were proposed to obtain the unbiased estimation of parameters for input-output-nonlinear but identification-linear system under output-dependent bounded noise. We utilized the truncated probability distribution to model the noise and gave the unbiased estimation algorithms of the system parameters as well as noise parameter if unknown. Asymptotic properties of the algorithms indicate that the algorithms converge to the true parameters. Besides illustrative numerical example, we also utilized the algorithm in a real world application to identify the personalized thermal comfort model using human noisy voting data. Results revealed the effectiveness and applicability of the proposed algorithms.
Capturing Outlines of Planar Images by Cubic Spline using Stochastic Evolution

This paper is concerned with a new technique of curve fitting. The technique has various phases including extracting outlines of images, detecting corner points from the detected outline, addition of extra knot points if needed. The last phase makes a significant contribution by making the technique automated. It uses the idea of stochastic evolution to optimize the shape parameters in the description of the generalized cubic spline. It ultimately produces optimal results for the approximate vectorization of the digital contour obtained from the planar images.
A Generic Algorithm for Program Repair

Relative correctness is the property of a program to be more-correct than another with respect to a specification, whereas traditional (absolute) correctness distinguishes between two classes of candidate programs with respect to a specification (correct and incorrect), relative correctness defines a partial ordering between candidate programs, whose maximal elements are the (absolutely) correct programs. In this paper we argue that relative correctness ought to be an integral part of the study of program repair, as it plays for program repair the role that absolute correctness plays for program construction: in the same way that absolute correctness is the criterion by which we judge the process of deriving a program P from a specification R, we argue that relative correctness ought to be the criterion by which we judge the process of repairing a program P to produce a program P' that is more-correct than P with respect to R. In this paper we build on this premise to design a generic program repair algorithm, which proceeds by successive increases of relative correctness until we achieve absolute correctness. We further argue that in the same way that correctness ideas were used, a few decades ago, as a basis for correct-by-design programming, relative correctness ideas may be used, in time, as a basis for more-correct-by-design program repair.
Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation

Database query engines typically rely upon query size estimators in order to evaluate the potential cost of alternate query plans. In multi-dimensional database systems, such as those typically found in large data warehousing environments, these selectivity estimators often take the form of multi-dimensional histograms. But while single dimensional histograms have proven to be quite accurate, even in the presence of data skew, the multi-dimensional variations have generally been far less reliable. In this paper, we present a new histogram model that is based upon an r-tree space partitioning. The localization of the r-tree boxes is in turn controlled by a Hilbert space filling curve, while a series of efficient area equalization heuristics restructures the initial boxes to provide improved bucket representation. Experimental results demonstrate significantly improved estimation accuracy relative to state of the art alternatives, as well as superior consistency across a variety of record distributions.
Efficacy of autologous platelet-rich plasma use for orthopaedic indications: a meta-analysis.

BACKGROUND The recent emergence of autologous blood concentrates, such as platelet-rich plasma, as a treatment option for patients with orthopaedic injuries has led to an extensive debate about their clinical benefit. We conducted a systematic review and meta-analysis to determine the efficacy of autologous blood concentrates in decreasing pain and improving healing and function in patients with orthopaedic bone and soft-tissue injuries. METHODS We searched MEDLINE and Embase for randomized controlled trials or prospective cohort studies that compared autologous blood concentrates with a control therapy in patients with an orthopaedic injury. We identified additional studies by searching through the bibliographies of eligible studies as well as the archives of orthopaedic conferences and meetings. RESULTS Twenty-three randomized trials and ten prospective cohort studies were identified. There was a lack of consistency in outcome measures across all studies. In six randomized controlled trials (n = 358) and three prospective cohort studies (n = 88), the authors reported visual analog scale (VAS) scores when comparing platelet-rich plasma with a control therapy across injuries to the acromion, rotator cuff, lateral humeral epicondyle, anterior cruciate ligament, patella, tibia, and spine. The use of platelet-rich plasma provided no significant benefit up to (and including) twenty-four months across the randomized trials (standardized mean difference, -0.34; 95% confidence interval [CI], -0.75 to 0.06) or the prospective cohort studies (standardized mean difference, -0.20; 95% CI, -0.64 to 0.23). Both point estimates suggested a small trend favoring platelet-rich plasma, but the associated wide confidence intervals were consistent with nonsignificant effects. CONCLUSIONS The current literature is complicated by a lack of standardization of study protocols, platelet-separation techniques, and outcome measures. As a result, there is uncertainty about the evidence to support the increasing clinical use of platelet-rich plasma and autologous blood concentrates as a treatment modality for orthopaedic bone and soft-tissue injuries.
Automatic recognition of movement patterns in the vojta-therapy using RGB-D data

Vojta-therapy is a useful technique for the treatment of physical and mental impairments in humans, and is very effective for children of less than 6 months. During the therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes the previously blocked connections between the spinal cord and brain available, and after a few session, patients can perform these movements without any external stimulation. The treatment must be performed several times a day or week and can last for a few weeks or months. Therefore, the therapists may recommend an at-home continuation of the therapy. An automatic vision-based system is required which can analyze and verify the correct pattern of a patient's body parts movement during the therapy process at home, ultimately revealing the accuracy of given treatment. We captured a dataset of more than 15,000 images from a Microsoft Kinect camera and a novel segmentation technique in RGB-D data is proposed to segment the patient's body region from the scene using a k-means clustering algorithm. The movement patterns of a patient's body parts are analyzed and a support vector machine (SVM) is trained to classify the correct movements. The classification results show that the proposed method is highly useful to recognize the correct movement patterns.
Ramification amplification-based microfluidic system for MicroRNA detection

MicroRNAs, a group of endogenous non-coding single-strand RNAs typically of 21~23nt in length, have been convincingly shown to play a critical role in a wide variety of physiological processes such as cell proliferation, differentiation, apoptosis, tumor metastasis, etc. Here we incorporated a microfluidic device with a miRNA detection method based on ramification amplification (RAM) to increase the detection throughput and reduce the labor cost. Four different reactions could be performed in a microfluidic device: (i) reverse-transcription of miRNA, (ii) annealing of circular probe (C-Probe), (iii) C-Probe ligation, and (iv) Ramification amplification. Through monitoring the intensity of fluorescent signal, tens of miRNA can be quantified in a chip in parallel. Thus, these primary results demonstrate that this new device has advantages in throughput and cost, and it might also be used for the detection of miRNA in dangerous samples, such as virus because of its automation control system.
Antibiotic cyclic AMP signaling by “primed” leukocytes confers anti‐inflammatory cytoprotection

The mechanism underlying anti‐inflammatory effects of macrolide antibiotics remains uncertain. In this study, we first show the evidences concerning the possible link between leukocytic cyclic adenosine monophosphate (cAMP) signaling and the mechanism of anti‐inflammatory, cytoprotective actions of macrolides. The clinical range of macrolides (i.e., erythromycin, roxithromycin, and clarithromycin) preferentially inhibited nuclear factor‐κB activation mediated by reactive oxygen intermediates, inducing cAMP‐dependent signaling [i.e., cAMP and cAMP‐responsive element‐binding protein (CREB)] by “primed” but not “resting” leukocytes. In this context, cAMP/CREB inhibition with adenosine 3′:5′‐cyclic monophosphothioate, rp‐isomer (rp‐cAMPs) and CREB decoy oligonucleotides reduced the anti‐inflammatory actions of macrolides. These results thus indicate that macrolide‐induced cAMP/CREB signaling, selectively by primed leukocytes, plays a major role in the mechanism of anti‐inflammatory actions of macrolides.
Exploring an Effective Incentive System on a Groupware

Groupware is an effective form of media for knowledge sharing and active open communication. One remaining important issue is how to design groupware in which vast amounts of beneficial content are provided and active discussion is facilitated. The behavior of information in such a medium resembles public-goods games because users voluntarily post beneficial information that creates media values. Many studies on such games have shown the effects of rewards or punishments in promoting cooperative behavior. In this paper, we show what types of incentive systems of rewards and punishments promote and maintain effective information behaviors or cooperative regimes in actual groupware. Our agent-based simulation demonstrates that a meta-reward system in which rewarders can gain other benefits for their own reward actions will probably encourage cooperation. Counterintuitively, our simulation also demonstrates that a system that applies sanctioning functions does not necessarily promote cooperation. Interestingly, a first-order reward system without any second-order incentives impedes the formation of cooperative regimes, while this is not the case with first-order punishment systems without second-order incentives. These findings may elucidate how successful groupware operates.
SELF-DISCLOSURE AND SELF-ESTEEM IN OLD AGE

The present study explored the structure of self-disclosure in old age, and examined the relation between self-esteem and the amount of self-disclosure. In this study, it was understood that self-disclosure had the function of stimulating"integrity" (Erikson et al., 1986) and seeking supports. Subjects were 30 males and 76 females over 60 years of age. It was found that the closer the relationship between the subject and his/her target became, the more the amount of self-disclosure were. The experiences of losing were found more intimate for elder than his/her past and present positive matters. The amount of self-disclosure in elder with middle selfesteem was higher than the lower one. The elder with high self-esteem had the tendency of disclosing his/her past experiences and positive matters to an intimate person, and of showing restrain in his/her losing experiences to his/her less familiar person.