Miracle-dz/UniFAR-FTUs huggingface dataset:
Autoimmunity-Associated LYP-W620 Does Not Impair Thymic Negative Selection of Autoreactive T Cells

A C1858T (R620W) variation in the PTPN22 gene encoding the tyrosine phosphatase LYP is a major risk factor for human autoimmunity. LYP is a known negative regulator of signaling through the T cell receptor (TCR), and murine Ptpn22 plays a role in thymic selection. However, the mechanism of action of the R620W variant in autoimmunity remains unclear. One model holds that LYP-W620 is a gain-of-function phosphatase that causes alterations in thymic negative selection and/or thymic output of regulatory T cells (Treg) through inhibition of thymic TCR signaling. To test this model, we generated mice in which the human LYP-W620 variant or its phosphatase-inactive mutant are expressed in developing thymocytes under control of the proximal Lck promoter. We found that LYP-W620 expression results in diminished thymocyte TCR signaling, thus modeling a “gain-of-function” of LYP at the signaling level. However, LYP-W620 transgenic mice display no alterations of thymic negative selection and no anomalies in thymic output of CD4+Foxp3+ Treg were detected in these mice. Lck promoter-directed expression of the human transgene also causes no alteration in thymic repertoire or increase in disease severity in a model of rheumatoid arthritis, which depends on skewed thymic selection of CD4+ T cells. Our data suggest that a gain-of-function of LYP is unlikely to increase risk of autoimmunity through alterations of thymic selection and that LYP likely acts in the periphery perhaps selectively in regulatory T cells or in another cell type to increase risk of autoimmunity.
A MEDIUM-GRAIN RECONFIGURABLE CELL ARRAY FOR DSP

Digital signal processing (DSP) is an essential component of many applications, including multimedia and communications systems. The recent surge in wireless and mobile computing underscores the need for high-performance low power DSP hardware. Reconfigurable hardware balances these requirements with development costs by providing system designers a viable alternative to custom integrated circuits. This paper describes a novel reconfigurable architecture for DSP applications. The device contains an array of medium-grain cells that can perform arithmetic, memory, and control operations. The main features of the architecture are as follows: flexible structures, variable word length, pipeline latches, and error correction. A prototype of the cell is being fabricated in 0.5-μm technology. Circuit simulations indicate that the array achieves a clock frequency of 100 MHz even with this modest technology and a performance comparable to the highest-performance DSP processors today.
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we propose a new kernel formulation that enables the separation of latent space and derives an efficient variational inference method. The performance of our model is demonstrated with an imbalanced medical image dataset.
Transseptal Conduction as an Important Determinant for Cardiac Resynchronization Therapy, as Revealed by Extensive Electrical Mapping in the Dyssynchronous Canine Heart

Background—Simple conceptual ideas about cardiac resynchronization therapy assume that biventricular (BiV) pacing results in collision of right and left ventricular (LV) pacing–derived wavefronts. However, this concept is contradicted by the minor reduction in QRS duration usually observed. We investigated the electric mechanisms of cardiac resynchronization therapy by performing detailed electric mapping during extensive pacing protocols in dyssynchronous canine hearts. Methods and Results—Studies were performed in anesthetized dogs with acute left bundle-branch block (LBBB, n=10) and chronic LBBB with tachypacing-induced heart failure (LBBB+HF, n=6). Activation times (AT) were measured using LV endocardial contact and noncontact mapping and epicardial contact mapping. BiV pacing reduced QRS duration by 21±10% in LBBB but only by 5±12% in LBBB+HF hearts. Transseptal impulse conduction was significantly slower in LBBB+HF than in LBBB hearts (67±9 versus 44±16 ms, respectively), and in both groups significantly slower than transmural LV conduction (≈30 ms). In both groups QRS duration and vector and the epicardial AT vector amplitude and angle were significantly different between LV and BiV pacing, whereas the endocardial AT vector was similar. During variation of atrioventricular delay while LV pacing, and ventriculo-ventricular delay while BiV pacing, the optimal hemodynamic effect was achieved when epicardial AT and QRS vectors were minimal and endocardial AT vector indicated LV preexcitation. Conclusions—Due to slow transseptal conduction, the LV electric activation sequence is similar in LV and BiV pacing, especially in failing hearts. Optimal hemodynamic cardiac resynchronization therapy response coincides with minimal epicardial asynchrony and QRS vector and LV preexcitation.
BalancedBoost: A hybrid approach for real-time network traffic classification

Real-time network traffic classification suffers from class imbalance and concept drift problem. Moreover, many features for offline analysis becomes unavailable due to the time requirement of giving results as quickly as possible. Several techniques, such as boosting and data sampling, have been used to solve the class imbalance problem. In this paper, we proposed a hybrid approach, called BalancedBoost, to address the class imbalance problem. BalancedBoost is based on a combination of data sampling and boosting algorithm. In order to reduce the impact of traffic dynamics and filter the redundant features, a feature selection algorithm based on symmetrical uncertainty is also presented. We conducted experiments on UNIBS datasets and compared our method to other similar proposed approaches including SMOTEBoost, RUSBoost and OverBagging. Experiment results obtained by our method are promising in terms of F-measure. BalancedBoost is able to improve the classification performance of the minority class. It is simple and efficient.
Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.
Nonlinear Autoassociation Is Not Equivalent to PCA

A common misperception within the neural network community is that even with nonlinearities in their hidden layer, autoassociators trained with backpropagation are equivalent to linear methods such as principal component analysis (PCA). Our purpose is to demonstrate that nonlinear autoassociators actually behave differently from linear methods and that they can outperform these methods when used for latent extraction, projection, and classification. While linear autoassociators emulate PCA, and thus exhibit a flat or unimodal reconstruction error surface, autoassociators with nonlinearities in their hidden layer learn domains by building error reconstruction surfaces that, depending on the task, contain multiple local valleys. This interpolation bias allows nonlinear autoassociators to represent appropriate classifications of nonlinear multimodal domains, in contrast to linear autoassociators, which are inappropriate for such tasks. In fact, autoassociators with hidden unit nonlinearities can be shown to perform nonlinear classification and nonlinear recognition.
Generation of Spike Latency Tuning by Thalamocortical Circuits in Auditory Cortex

In many sensory systems, the latency of spike responses of individual neurons is found to be tuned for stimulus features and proposed to be used as a coding strategy. Whether the spike latency tuning is simply relayed along sensory ascending pathways or generated by local circuits remains unclear. Here, in vivo whole-cell recordings from rat auditory cortical neurons in layer 4 revealed that the onset latency of their aggregate thalamic input exhibited nearly flat tuning for sound frequency, whereas their spike latency tuning was much sharper with a broadly expanded dynamic range. This suggests that the spike latency tuning is not simply inherited from the thalamus, but can be largely reconstructed by local circuits in the cortex. Dissecting of thalamocortical circuits and neural modeling further revealed that broadly tuned intracortical inhibition prolongs the integration time for spike generation preferentially at off-optimal frequencies, while sharply tuned intracortical excitation shortens it selectively at the optimal frequency. Such push and pull mechanisms mediated likely by feedforward excitatory and inhibitory inputs respectively greatly sharpen the spike latency tuning and expand its dynamic range. The modulation of integration time by thalamocortical-like circuits may represent an efficient strategy for converting information spatially coded in synaptic strength to temporal representation.
Secure file sharing using cryptographic techniques in cloud

Cloud computing is a type of computing that relies on sharing resources rather than having local servers or personal devices. But sharing resources among many users in the cloud is a challenging issue. The proposed scheme provides the facility of secure storing and sharing the data for dynamic groups in the cloud. By using group signature and encryption techniques any cloud users can efficiently share their data in untrusted cloud. Data Owner can store the file without revealing their identity to others in the cloud. Revocation of the user takes place without updating the private keys of the remaining users. Data Owner or Group Manager can be able to delete the file which is stored in the cloud. The scheme allows that the new registered users can directly decrypt the other group members file which is stored in the cloud using their own private key without contacting the owner of the data file. Specially, it reduces the computation overhead to encrypt the data files and the cipher text size is constant and independent of the revocation users.
Model checking optimization of safe Control Embedded Components with refinement

This paper deals with model checking optimization of Software Embedded Control Components by applying refinement. We introduce a Software Embedded Control Component as an event-triggered software unit composed of an interface for any external interactions and an implementation allowing control actions of physical processes. A control system is assumed to be a composition of components with precedence constraints to control the plant. To ensure safety, an intelligent software agent controls the plant and applies automatic reconfiguration whenever a physical error occurs in the plant. We propose in this paper to model checking these different reconfigurations through a refinement method realized step by step. The contributions of the paper are applied to two Benchmark Production Systems available in our research laboratory.
Ve rifiable Partial Escrow of Inte ge r Factors

We construct an e cient interactive protocol for realizing veri able partial escrow of the factors of an integer n with time-delayed and threshold key recovery features. The computational cost of the new scheme amounts to 10k log2 P multiplications of numbers of size of P , where P is a protocol parameter which permits n of size up to (log2 P ) 4 to be dealt with and k is a security parameter which controls the error probability for correct key escrow under 1=2. The new scheme realizes a practical method for ne tuning the time complexity for factoring an integer, where the complexity tuning has no respect to the size of the integer.
Model-based signal detection in snapping shrimp noise

In a number of scenarios, detecting the presence or absence of a known signal may be of practical interest. One such example lies in a communication setting, where packet detection is a vital first step to decode transmitted data. The detection problem can be formulated as a binary hypothesis test within the Neyman-Pearson (NP) framework. Our scenario of interest is warm shallow waters, where the sea floor is inhabited by colonies of snapping shrimp. The ambient noise in such a case is impulsive and exhibits memory. We investigate the performance of optimal detectors corresponding to four additive noise models in snapping shrimp noise. In the literature, proposed detectors typically take only the amplitude statistics of the noise process into account. By also considering the memory, we show that there is substantial improvement in detection performance. The detector in the latter case is based on the recently introduced stationary α-sub-Gaussian noise with memory order m (αSGN(m)) model, which effectively characterizes the temporal amplitude statistics of the snapping shrimp noise process.
Automated generation and refinement of protein signatures: case study with G-protein coupled receptors

MOTIVATION Previous work had established that it was possible to derive sparse signatures (essentially sequence-length motifs) by examining points of contact between residues in proteins of known three-dimensional (3D) structure. Many interesting protein families have very little tertiary structural information. Methods for deriving signatures using only primary and secondary-structural information were therefore developed. RESULTS Two methods for deriving protein signatures using protein sequence information and predicted secondary structures are described. One method is based on a scoring approach, the other on the Genetic Algorithm (GA). The effectiveness of the method was tested on the superfamily of GPCRs and compared with the established hidden Markov model (HMM) method. The signature method is shown to perform well, detecting 68% of superfamily members before the first false positive sequence and detecting several distant relationships. The GA population was used to provide information on alignment regions of particular importance for selection of key residues.
A platform-based MPEG-4 advanced video coding (AVC) decoder with block level pipelining

We present a baseline MPEG-4 AVC (advanced video coding) decoder based on an optimized platform-based design methodology. With this methodology, we jointly optimize the software and hardware design of the decoder. Overall decoding throughput is increased by synchronizing the software and the dedicated co-processors. The synchronization is achieved at macroblock-level pipelining. In addition, we optimize the decoder software by enhancing the frame buffer management, boundary padding, and content aware inverse transform. To speed up motion compensation and inverse transform, which are the most computationally intensive modules, two dedicated acceleration modules are realized. For comparison, the proposed prototype decoder and MPEG-4 AVC reference decoder are evaluated on an ARM platform, which is one of most popular portable devices. Our experiments show that the throughput of the MPEG-4 reference decoder can be improved by 6 to 7 times. On an ARM966 board, the optimized software without hardware acceleration can achieve a decoding rate up to 5 frames per second (fps) for QCIF video sequences. With the dedicated accelerators, the overall throughput is increased by about 30% to reach 6.6 fps on the average and is up to 10.3 fps for slow motion video sequences.
Entropy based keyframe selection for Multi-Camera Visual SLAM

Although many state-of-the-art visual SLAM algorithms use keyframes to help alleviate the computational requirements of performing online bundle adjustment, little consideration is taken for specific keyframe selection. In this work, we propose two entropy based methods which aim to insert keyframes that will directly improve the system's ability to localize. The first approach inserts keyframes based on the cumulative point entropy reduction in the existing map, while the second approach uses the predicted point flow discrepancy to select keyframes which best initializes new features for the camera to track against in the future. We implement the proposed methods within the Multi-Camera Parallel Mapping and Tracking framework, and demonstrate the effectiveness of our methods using ground truth data collected using an indoor positioning system.
Minimum energy cost reliable routing in ad hoc wireless networks

Energy-aware routing is an effective way to extend the operational lifetime of wireless ad hoc networks. In this paper, we propose a new energy-aware routing scheme called Reliable Minimum Energy Cost Routing (RMECR). RMECR finds routes which require least amount of energy for reliable packet transfer in ad hoc networks. It defines the energy cost of packet forwarding by a node as the fraction of remaining battery energy which is consumed by a node to forward a packet. This includes the energy consumed for retransmission of the packet as well, when the packet or its acknowledgment is lost. We show that RMECR can effectively reduce the energy consumption of nodes and balance the traffic load among them. Furthermore, RMECR is able to find reliable routes, in which constituent links require less number of packet retransmissions due to packet loss. This in turn decreases the latency of packet delivery and saves energy as well.
Effects of Spontaneous Respiration on Canine Left Ventricular Function

The purpose of this study was to determine the mechanism of the decrease in left ventricular stroke volume during spontaneous inspiration. We determined the transmural pressures of the left heart by measuring left atrial and diastolic left ventricular pressures relative to esophageal pressure. We estimated the directional changes in end-systolic and end-diastolic volumes of the left ventricle by determining the transit time of sound transmission between two ultrasonic crystals facing each other across the minor axis of the left ventricle. Left ventricular stroke volume decreased with spontaneous inspiratory effort as pleural pressure fell, regardless of whether lung volume increased or remained constant. The stroke volume was decreased during the fall in pleural pressure because of an increase in end-systolic volume with an essentially unchanged diastolic volume. Thus, the decrease in stroke volume was due to a decrease in ejection, rather than a decrease in filling of the left ventricle. We believe that left ventricular ejection was impeded by the fall in pressure around the heart relative to the pressure in the aorta. In spite of the essentially constant diastolic volume, diastolic left ventricular transmural pressure rose, suggesting that spontaneous inspiration decreases the diastolic compliance of the left ventricle. The change in diastolic compliance contributed to the decrease in stroke volume but was not the primary cause. Circ Res 45: 719-? 28, 1979
A DVENTURE G AMES AND L ANGUAGE L EARNING

Educational video games, or teaching games, can help in language learning by increasing student motivation or by allowing contextualized learning. However, game development is a costly and complex activity with many open issues. In this paper we present a method that allows for rapid game prototyping, thus reducing the risks implied in development. The method is based on the reuse of common structures particular to the filed of language learning (e.g. multiple-choice questions) and takes advantage of the combination of different e-learning platforms. We present a case study of the application of this method for the creation of a language learning adventure game.
Preventing Human Rights Abuses by Regulating Arms Brokering: The U.S. Brokering Amendment to the Arms Export Control Act

Armed militias operating with the complicity of extremists in Rwanda's ruling regime murdered more than 500,000 Tutsi civilians and politically moderate Hutus in 1994.1 The international community failed in its efforts to prevent this tragedy. 2 Instead, governments, private exporters, commercial manufacturers, brokers, transporters, and shipping agents helped to foster conflict by providing arms to the extremists. 3 Perhaps no single set of actions undermined the potential to resolve the civil war and de-escalate the genocide as significantly as the sale of more than $26.9 million in small arms and light weapons to the Rwandan government between 1990 and 1994. 4
Goal-directed learning of hand-eye coordination in a humanoid robot

Visuo-motor coordination is known to be highly important for the development of a broader range of cognitive and motor skills in human infants and can thus be considered one of the key skills for robots to master. In this paper, we investigate how a recent concept in developmental robotics, referred to as goal babbling, relates to a visuo-motor coordination task in the humanoid robot Aldebaran Nao that requires coordinated control of two subsystems of motors, namely head and arm motors. The idea of goal babbling builds on findings in developmental psychology showing that human infants attempt goal-directed movements early on in their development enabling them to rapidly and efficiently bootstrap their motor system. Goal babbling has been shown to be superior to the classical idea of random motor babbling for the learning of body kinematics in robotic systems, in particular for systems with many degrees of freedom. Our results not only support the utility of goal babbling for the acquisition of visuo-motor coordination skills but also suggest that goal babbling is particularly effective in the case where two separate motor sub-systems, head and arm, need to be coordinated.