Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Reorganization Underlies Improvement in Stroke‐induced Motor Dysfunction by Music‐supported Therapy

Motor impairments are common after stroke, but efficacious therapies for these dysfunctions are scarce. By extending an earlier study on the effects of music‐supported therapy, behavioral indices of motor function as well as electrophysiological measures were obtained before and after a series of therapy sessions to assess whether this new treatment leads to neural reorganization and motor recovery in patients after stroke. The study group comprised 32 stroke patients in a large rehabilitation hospital; they had moderately impaired motor function and no previous musical experience. Over a period of 3 weeks, these patients received 15 sessions of music‐supported therapy using a manualized step‐by‐step approach. For comparison 30 additional patients received standard rehabilitation procedures. Fine as well as gross motor skills were trained by using either a MIDI‐piano or electronic drum pads programmed to emit piano tones. Motor functions were assessed by an extensive test battery. In addition, we studied event‐related desynchronization/synchronization and coherences from all 62 patients performing self‐paced movements of the index finger (MIDI‐piano) and of the whole arm (drum pads). Results showed that music‐supported therapy yielded significant improvement in fine as well as gross motor skills with respect to speed, precision, and smoothness of movements. Neurophysiological data showed a more pronounced event‐related desynchronization before movement onset and a more pronounced coherence in the music‐supported therapy group in the post‐training assessment, whereas almost no differences were observed in the control group. Thus we see that music‐supported therapy leads to marked improvements of motor function after stroke and that these are accompanied by electrophysiological changes indicative of a better cortical connectivity and improved activation of the motor cortex.
A Spike and Slab RBM Approach to Modeling Natural Images

The spike and slab Restricted Boltzmann Machine (ssRBM) is defined by having both a real valued “slab” variable and a binary “spike” variable associated with each unit in the hidden layer. Earlier work exploring a simple incarnation of the ssRBM model family demonstrated its utility as a model of natural images. In this work, we explore an extension of the ssRBM model – the μ-ssRBM – that includes additional terms to the energy function which we use, in part, to address one of the potential drawback of the original ssRBM introduced in (Courville et al., 2010), specifically, the lack of a guarantee that the model defines a valid density over the whole data domain. We find that while it is possible to parametrize the model to guarantee that all conditionals of the model are well defined, loosening this constraint empirically yields better classification performance with the CIFAR-10 image dataset.
Exploring interaction with a simulated wrist-worn projection display

One of the major limitations of portable computing devices is the small size of their built-in displays. Fortunately, extremely small projection systems are being developed that can be integrated into devices that are small enough to be body-worn, yet can project a large image onto surfaces in the environment. To explore how a user might interact with this near-horizon technology, we created a functional simulation of a wrist-worn projector. We then developed a set of interaction techniques that assume that the wrist-worn computer and projector are equipped with position and orientation sensors, in addition to a touch-sensitive built-in screen. To complement the techniques that rely on the spatial manipulation of the user's forearm and the device itself, we also describe the use of a cursor less watch user interface that minimizes the need for the user to look down at the device's built-in screen. Finally, we present a sample application that illustrates our interaction techniques.
Community Detection-based Analysis of the Human Interactome Network

The study of proteomics reached unexpected levels of interest, as a direct consequence of its discovered influence over some complex biological phenomena, such as problematic diseases like cancer. This paper presents a new technique that allows for an accurate analysis of the human interactome network. It is basically a two-step analysis process that involves, at first, the detection of each protein’s absolute importance through the betweenness centrality computation. Then, the second step determines the functionallyrelated communities of proteins. For this purpose, we use a community detection technique that is based on the edge betweenness calculation. The new technique was thoroughly tested on real biological data and the results prove some interesting properties of those proteins that are involved in the carcinogenesis process. Apart from its experimental usefulness, the novel technique is also computationally effective in terms of execution times. Based on the analysis’ results, some topological features of cancer mutated proteins are presented and a possible optimization solution for cancer drugs design is suggested. Keywords—Betweenness centrality, interactome networks, proteinprotein interactions, protein communities, cancer.
Observer design for invariant systems with homogeneous observations

This paper considers the design of nonlinear observers for invariant systems posed on finite-dimensional connected Lie groups with measurements generated by a transitive group action on an associated homogeneous space. We consider the case where the group action has the opposite invariance to the system invariance and show that the group kinematics project to a minimal realisation of the systems observable dynamics on the homogeneous output space. The observer design problem is approached by designing an observer for the projected output dynamics and then lifting to the Lie-group. A structural decomposition theorem for observers of the projected system is provided along with characterisation of the invariance properties of the associated observer error dynamics. We propose an observer design based on a gradient-like construction that leads to strong (almost) global convergence properties of canonical error dynamics on the homogeneous output space. The observer dynamics are lifted to the group in a natural manner and the resulting gradient-like error dynamics of the observer on the Lie-group converge almost globally to the unobservable subgroup of the system, the stabiliser of the group action.
CARC: A Reliable Routing Mechanism for Cognitive Ad Hoc Networks

In cognitive radio Ad Hoc networks, the events of node failure and link failure maybe happen frequently, especially the primary users will access the authorized channel whenever possible. In order to maintain the connection between cognitive users and resume their communication as soon as possible when the current route is invalid, a reliable routing mechanism CARC is proposed, which employs primary and candidate route to communicate under differential cases. Meanwhile, how to choose candidate route, maintain or rebuild route are also elaborated. Numerical simulations show that the proposed CARC protocol has a higher performance compared with other typical protocols.
A molecular toolbox for rapid generation of viral vectors to up- or down-regulate in vivo neuronal gene expression.

We introduce a molecular toolbox for manipulation of neuronal gene expression in vivo . The toolbox includes promoters, ion channels, optogenetic tools, ﬂuorescent proteins, and intronic artiﬁcial microRNAs.The components are easily assembled into adeno-associated virus (AAV) or lentivirus vectors using recombination cloning. We demonstrate assembly of toolbox components into lentivirus and AAV vectors and use these vectors for in vivo expression of inwardly rectifying potassium channels (Kir2.1, Kir3.1, and Kir3.2) and an artiﬁcial microRNA targeted against the ion channel HCN1 (HCN1 miRNA). We show that AAV assembled to express HCN1 miRNA produces efﬁcacious and speciﬁc in vivo knockdown of HCN1 channels. Comparison of in vivo viral transduction using HCN1 miRNA with mice containing a germ line deletion of HCN1 reveals similar physiological phenotypes in cerebellar Purkinje cells. The easy assembly and re-usability of the toolbox components, together with the ability to up- or down-regulate neuronal gene expression in vivo , may be useful for applications in many areas of neuroscience. Generation of att-ﬂanked cassettes. Cassettes encoding the toolbox components were generated by PCR ampliﬁcation using primers containing the required att recombination site sequences in addition to 18–25 base pairs of template-speciﬁc sequence. Two hundred nanomolars of each primer was added to 1–2 μ l of tem-plate DNA and 22.5 μ l Platinum PCR SuperMix High Fidelity (Invitrogen). Thirty-ﬁve cycles of ampliﬁcation were performed at 94˚C for 30 s, 55˚C for 30 s, and 68˚C for 60 s. and TE incubated at microliter and positive were
Mobile anchor assisted localization in wireless sensor networks with regular polygon path

Mobile anchor assisted localization methods for wireless sensor networks can decrease the cost while improving the accuracy, and the moving trajectory is an interesting and basic issue in this kind of method. This paper proposes the regular polygon path derived from Circle, which is the best determined path. The upper-bound of the polygon and the localization scheme are presented, and the performance is evaluated by simulations.
Effects of Hesel-coil deep transcranial magnetic stimulation for depression – a systematic review

Abstract Background: One third of the depressed patients are not improved by antidepressant drugs and psychological treatments, and there is a need for additional treatments. Repetitive transcranial magnetic stimulation (rTMS) is being developed towards an alternative in treatment-resistant depression. Deep transcranial stimulation (dTMS) with the Hesel-coil (H-coil) is a further development of rTMS aiming to enhance the effect by getting the magnetic pulses to penetrate deeper into the brain. Aims: This report aims to assess the evidence-base for dTMS for depression. The report also includes an assessment of the ethical and economic aspects involved. Methods: A systematic review of the effects of H-coil dTMS on depression was conducted and the scientific support was evaluated using GRADE (Grading of Recommendations Assessment, Development and Evaluation). Results: Only one controlled study was identified. In the sham-controlled randomized study, 212 participants with major depression that had not responded to antidepressant medication were enrolled. A two-point superiority in Hamilton Depression Rating Scale was observed in the dTMS arm vs the sham-arm at 4 weeks, but the difference was not statistically significant. No serious adverse events were reported apart from rare cases of epileptic seizures. Conclusions: The existing scientific support for H-coil dTMS therapy for depression is insufficient. The clinical implication is that the use of dTMS in depression should be restricted to the framework of clinical trials pending further studies. Fortunately, additional studies are underway and the evidence base should presumably improve over the next several years.
Detail aware contrast enhancement with linear image fusion

This paper presents an approach to detail aware contrast enhancement with linear image fusion. The proposed approach is abbreviated as DACE/LIF. Two main stages are involved in the DACE/LIF approach: the conventional histogram equalization (CHE) and linear image fusion (LIF). Though the CHE has the problem of over enhancement, it is noted that the details which is not obvious in the original image are generally revealed after the CHE. Interesting enough, the details shown in the original image and in the equalized image are of a kind of complementary property. In this paper, it is called detail-complementary property (DCP) between the original image and the equalized image. The DCP suggests that the details in the original image and the equalized image may be combined to form an image with better contrast and visual quality. In the light of DCP, details are aware and the LIF is employed to combine the original image and its equalized image by the CHE. Simulation results indicate that the enhanced image by the proposed DACE/LIF approach has better visual quality than that in the original image and that by the compared HE-based approach.
New Methodologies for Differential-Linear Cryptanalysis and Its Extensions

In 1994 Langford and Hellman introduced differential-linear cryptanalysis, which involves building a differential-linear distinguisher by concatenating a linear approximation with such a (truncated) differential that with probability 1 does not affect the bit(s) concerned by the input mask of the linear approximation. In 2002 Biham, Dunkelman and Keller presented an enhanced approach to include the case when the differential has a probability smaller than 1; and in 2005 they proposed several extensions of differential-linear cryptanalysis, including the high-order differential-linear analysis, the differential-bilinear analysis and the differential-bilinearboomerang analysis. In this paper, we show that Biham et al.’s methodologies for computing the probabilities of a differential-linear distinguisher, a high-order differential-linear distinguisher, a differential-bilinear distinguisher and a differential-bilinear-boomerang distinguisher do not have the generality to describe the analytic techniques. Thus the previous cryptanalytic results obtained by using these techniques of Biham et al. are questionable. Finally, from a mathematical point we give general methodologies for computing the probabilities. The new methodologies lead to some better cryptanalytic results, for example, differential-linear attacks on 13-round DES and 10-round CTC2 with a 255-bit block size and key.
Optimism, Self-Differentiation, and Perceived Posttraumatic Stress Disorder Symptoms: Predictors of Satisfaction in Female Military Partners

Female wives/partners of active-duty military personnel, reservists, and veterans (N = 235) who had experienced a combat deployment participated in a study on the contributions of posttraumatic stress disorder (PTSD) symptoms, dispositional optimism, and self-differentiation to romantic relationship satisfaction. Optimism and differentiation were tested as protective factors in the context of PTSD symptoms. As hypothesized, more partner-perceived PTSD symptoms predicted less relationship satisfaction, and optimism contributed uniquely to satisfaction when controlling for symptom severity and the partner’s military status (active duty vs. separated). Contrary to prediction, however, self-differentiation was negatively associated with relationship satisfaction, possibly due to the unique experience of military life and/or being partnered with a man who had experienced a combat deployment. Further analyses revealed that participants who indicated that their partners had formally received a PTSD diagnosis reported greater emotional reactivity and less emotional cutoff. The implications of these results are discussed along with the study’s limitations and recommendations for future research.
Joint uplink power and subchannel allocation in cognitive radio network

In this paper, we consider the resource allocation problem in the uplink transmission of an orthogonal frequency-division multiple access (OFDMA) based cognitive radio (CR) network. The resource allocation aims to maximize the uplink throughput of secondary users (SUs) in CR network under the constraints of the primary user (PU) interference and the transmit power limits of SUs. In general, the optimal joint power and subchannel allocation is known as NP-hard. To ease the computation complexity while maintain good performance, we propose a novel particle swarm optimization (PSO) based joint uplink power and subchannel allocation algorithm to solve this resource allocation problem. Due to the combinatorial nature of the resource allocation problem, our algorithm in which power continuously changes while the subchannel allocation strategy alters in the iteration process can obtain better performance than the existing decomposition based algorithm that subchannel assignment and power allocation are implemented separately. Simulation results show the effectiveness of the proposed algorithm.
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains

We introduce and study a randomized quasi-Monte Carlo method for the simulation of Markov chains up to a random (and possibly unbounded) stopping time. The method simulates n copies of the chain in parallel, using a (d+1)-dimensional, highly uniform point set of cardinality n, randomized independently at each step, where d is the number of uniform random numbers required at each transition of the Markov chain. The general idea is to obtain a better approximation of the state distribution, at each step of the chain, than with standard Monte Carlo. The technique can be used in particular to obtain a low-variance unbiased estimator of the expected total cost when state-dependent costs are paid at each step. It is generally more effective when the state space has a natural order related to the cost function. We provide numerical illustrations where the variance reduction with respect to standard Monte Carlo is substantial. The variance can be reduced by factors of several thousands in some cases. We prove bounds on the convergence rate of the worst-case error and of the variance for special situations where the state space of the chain is a subset of the real numbers. In line with what is typically observed in randomized quasi-Monte Carlo contexts, our empirical results indicate much better convergence than what these bounds guarantee.
Social Media Networking and Assessing Its Security Risks

Over the past several years, we have seen the emergence of new paradigm in the Internet, online social media networking, which provide Internet users to communicate and collaborate with family, friends, social groups, and other community by using social media tools (i.e., Twitter, Facebook, MySpace, and Youtube). The use of social media for communication is becoming more prevalent worldwide, with people from countries of varying economic development increasingly accessing the Internet to participate in networking sites. Online social media networking today is a great tool to meet and network with people sharing similar business interests. However, they can also pose serious security threats to users and their organizations. This paper presents the security risks of online social media networking and then attempts to develop the model for assessing its security risks. Our model can help security professionals for assessing security risks in the existing information systems and designing new security systems of enterprise.
Robust matching algorithm based on SURF

For matching visible image with many similar regions, the SURF matching algorithm based on Euclidean distance has disadvantages of limited matching constrains, higher false matching rate and difficult to remove error points effectively. To resolve these shortcomings above, a robust matching algorithm was proposed in which a combined measure of Cosine Similarity and geometry consistency were adopted to dispose multidimensional feature vectors. We compared with the original matching algorithm that only use Nearest Neighbor (NN) Searching and Random Sample Consensus (RANSAC) algorithm, our method performs well for eliminating error matching points, especially in terms of many similar regions of validity.
NBTI-aware power gating for concurrent leakage and aging optimization

Power and reliability are known to be intrinsically conflicting metrics: traditional solutions to improve reliability such as redundancy, increase of voltage levels, and up-sizing of critical devices do contrast with traditional low-power solutions, which rely on small devices and scaled supply voltages. The emergence of Negative Bias Temperature Instability (NBTI) as the most relevant source of unreliability in sub-90nm technologies has even exacerbated this incompatibility of the two metrics: NBTI manifests itself as an increase of the propagation delay over time, which adds up to the delay penalty introduced by most low-power design solutions. In this work, we show how the most widely adopted leakage reduction solution, that is, power-gating, can overcome this conflict, and how it can be used to naturally reduce the effects of NBTI on delay. Based on this important property, we present a methodology for NBTI-aware power gating that allows synthesizing low-leakage circuits with maximum lifetime.
APDS : The Automated Product Delivery System

The Automated Product Delivery System (APDS) was developed to address a need in industrial manufacturing for a simple way to transfer materials between areas of a manufacturing environment. At the moment, most equipment used for such a task consist of some type of fixed system, such as conveyor belts, elevators, etc. While adequate, they are not easily adaptable to any changes in their environment. Therefore, an automated unit would seem to be more successful in such an application.
What would feminist data visualization look like

by Catherine D'Ignazio Assistant Prof of Data Visualization & Civic Media, Journalism Dept, Emerson College, Boston (USA) ; kanarinka.com. While there is a lot of hype about data visualization, and a lot of new tools for doing it (my colleague Rahul Bhargava and I have counted over 500! ), fewer people are thinking critically about the politics and ethics of representation. This, combined with a chart-scared general public, means that data visualizations wield a tremendous amount of (.)
Workshop on new trends in content-based recommender systems: (CBRecSys 2014)

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.
Vertex-deleted and Edge-deleted Subgraphs

The foremost problem in this area of graph theory is the Reconstruction Conjecture which states that a graph is reconstructible from its collection of vertex-deleted subgraphs. In spite of several attempts to prove the conjecture only very partial results have been obtained. Several of these results do however bring to light interesting structural relationships between a graph and its subgraphs. For example, can all the subgraphs of a nonplanar graph be planar? Some reconstruction results viewed in this light will be discussed here.