Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Directed Graphical Models from Nonlinear and Non-Gaussian Data

Traditional constraint-based and score-based methods for learning directed graphical models from continuous data have two significant limitations: (i) they require (in practice) assuming dependencies are linear with Gaussian noise; (ii) they cannot distinguish between Markov equivalent structures. More recent structure learning methods avoid both limitations by directly exploiting characteristics of the observed data distribution resulting from nonlinear effects and non-Gaussian noise. We review these methods and focus on the additive noise model approach, which while more general than traditional approaches also suffers from two major limitations: (i) it is invertible for certain distribution families, i.e. linear Gaussians, and thus not useful for structure learning in these cases; (ii) it was originally proposed for the two variable case with a multivariate extension that requires enumerating all possible DAGs, which is ususally intractable. To address these two limitations, we introduce weakly additive noise models, which extends the additive noise model framework to cases where additive noise models are invertible and noise is not additive. We then provide an algorithm for learning equivalence classes for weakly additive noise models from data which combines a PC style search using recent advances in kernel measures of conditional dependence with greedy local searches for additive noise models. This combined approach provides a more computationally efficient search procedure for when nonlinear dependencies and/or non-Gaussian noise may be present that learns equivalence classes of structures which are often more specific than the Markov equivalence class even in the case of invertible additive noise models and non-additive noise models, addressing the limitations of both traditional structure learning methods and the additive noise model. We evaluate this approach using synthetic data and real climate teleconnection and fMRI data.
Integrin signaling is critical for pathological angiogenesis

The process of postnatal angiogenesis plays a crucial role in pathogenesis of numerous diseases, including but not limited to tumor growth/metastasis, diabetic retinopathy, and in tissue remodeling upon injury. However, the molecular events underlying this complex process are not well understood and numerous issues remain controversial, including the regulatory function of integrin receptors. To analyze the role of integrin phosphorylation and signaling in angiogenesis, we generated knock-in mice that express a mutant β3 integrin unable to undergo tyrosine phosphorylation. Two distinct models of pathological angiogenesis revealed that neovascularization is impaired in mutant β3 knock-in mice. In an ex vivo angiogenesis assay, mutant β3 knock-in endothelial cells did not form complete capillaries in response to vascular endothelial growth factor (VEGF) stimulation. At the cellular level, defective tyrosine phosphorylation in mutant β3 knock-in cells resulted in impaired adhesion, spreading, and migration of endothelial cells. At the molecular level, VEGF stimulated complex formation between VEGF receptor-2 and β3 integrin in wild-type but not in mutant β3 knock-in endothelial cells. Moreover, phosphorylation of VEGF receptor-2 was significantly reduced in cells expressing mutant β3 compared to wild type, leading to impaired integrin activation in these cells. These findings provide novel mechanistic insights into the role of integrin–VEGF axis in pathological angiogenesis.
Pharmacological treatment of deep brain stimulation-induced hypomania leads to clinical remission while preserving motor benefits

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, but can lead to adverse effects including psychiatric disturbance. Little is known about the risk factors and treatment options for such effects. Here, we describe a patient who reproducibly developed stimulation-induced hypomania when using ventrally located electrodes and responded well to pharmacological intervention while leaving the stimulation parameters unchanged to preserve motor benefits. In spite of clinical remission, [15O]-positron-emission-tomography (PET) demonstrated activation patterns similar to those reported during mania. This case, therefore, highlights an important treatment option of adverse effects of DBS, but also points toward the need for investigations of its risk factors and their underlying neurobiological mechanisms.
A Survey of Perturbation Technique For Privacy-Preserving of Data

— Privacy concerns over the ever-increasing gathering of personal information by various institutions led to the development of privacy preserving data. The approach protects the privacy of the data by perturbing the data through a method. The major challenge of data perturbation is to achieve the desired result between the level of data privacy and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving of data for applications and mining systems. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired level of data utility by selectively preserving the mining task and model specific information during the data perturbation process. The multiplicative perturbation algorithm may find multiple data transformations that preserve the required data utility. Thus the next major challenge is to find a good transformation that provides a satisfactory level of privacy data. we are going to handle the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterns and trends from the original database. I am trying to get advantage of both the dimension like more protected data and relative fast.
Adaptive video-aware FEC-based mechanism with unequal error protection scheme

Real-time video services over wireless networks are becoming a part of everyday life and have been used to spread information ranging from education to entertainment content. However, the challenge of dealing with the fluctuating bandwidth, scarce resources and the time-varying error rate of theses networks, unveils the need for an error-resilient video transport. In this context, Forward Error Correction (FEC) approaches are required to provide the distribution of video applications for wireless users with Quality of Experience (QoE) assurance. This work proposes an adaptive cross-layer Video-Aware FEC mechanism with Unequal Error Protection (UEP) scheme to enhance video transmission in wireless networks, while increasing the user satisfaction and improving the usage of wireless resources. The benefit and impact of the proposed mechanism are demonstrated by using simulation and assessed through objective and subjective QoE metrics.
Recognizing Faces from the Eyes OnlyE

The eyes are one of the most important facial features for recognizing human faces. Many face recognition systems today make use of local features (such as eyes) for identiication or veriica-tion of individuals, but no system to our knowledge has studied performance when the only available information is the eyes. In this paper we show that we can obtain 85% correct classiication on the popular ORL face database, when the features are extracted from the eye area only. We compare feature extraction from eigenfeatures and Gabor wavelets with features consisting of simple gray-level pixel values.
Fish Oil Attenuates Omega-6 Polyunsaturated Fatty Acid-Induced Dysbiosis and Infectious Colitis but Impairs LPS Dephosphorylation Activity Causing Sepsis

Clinically, excessive ω-6 polyunsaturated fatty acid (PUFA) and inadequate ω-3 PUFA have been associated with enhanced risks for developing ulcerative colitis. In rodent models, ω-3 PUFAs have been shown to either attenuate or exacerbate colitis in different studies. We hypothesized that a high ω-6: ω-3 PUFA ratio would increase colitis susceptibility through the microbe-immunity nexus. To address this, we fed post-weaned mice diets rich in ω-6 PUFA (corn oil) and diets supplemented with ω-3 PUFA (corn oil+fish oil) for 5 weeks. We evaluated the intestinal microbiota, induced colitis with Citrobacter rodentium and followed disease progression. We found that ω-6 PUFA enriched the microbiota with Enterobacteriaceae, Segmented Filamentous Bacteria and Clostridia spp. , all known to induce inflammation. During infection-induced colitis, ω-6 PUFA fed mice had exacerbated intestinal damage, immune cell infiltration, prostaglandin E2 expression and C. rodentium translocation across the intestinal mucosae. Addition of ω-3 PUFA on a high ω-6 PUFA diet, reversed inflammatory-inducing microbial blooms and enriched beneficial microbes like Lactobacillus and Bifidobacteria, reduced immune cell infiltration and impaired cytokine/chemokine induction during infection. While, ω-3 PUFA supplementation protected against severe colitis, these mice suffered greater mortality associated with sepsis-related serum factors such as LPS binding protein, IL-15 and TNF-α. These mice also demonstrated decreased expression of intestinal alkaline phosphatase and an inability to dephosphorylate LPS. Thus, the colonic microbiota is altered differentially through varying PUFA composition, conferring altered susceptibility to colitis. Overall, ω-6 PUFA enriches pro-inflammatory microbes and augments colitis; but prevents infection-induced systemic inflammation. In contrast, ω-3 PUFA supplementation reverses the effects of the ω-6 PUFA diet but impairs infection-induced responses resulting in sepsis. We conclude that as an anti-inflammatory agent, ω-3 PUFA supplementation during infection may prove detrimental when host inflammatory responses are critical for survival.
Registration-based moving vehicle detection for low-altitude urban traffic surveillance

Vision based vehicle detection attracts much attention in low-altitude platform for urban traffic surveillance which has great potential practical value. The major difficulties of moving vehicle detection are two-fold: 1) the unconstrained motion of the camera platform; 2) crowded vehicles are connected and hard to be segmented. To address these problems, we proposed an improved registration method to eliminate the camera platform motion, thus techniques in stationary vision surveillance such as frame subtraction can perform well on the registered video to detect moving vehicles rapidly and robustly. For registration, control points which should be in motionless regions play an important role; however two consequent images cannot be consistently transformed to the same affine coordinates, because some control points fall into the moving vehicle regions which cover a large area in the camera view. Compared with existing methods, adaptive sized windows are used to efficiently select control points efficiently only in static regions; and then frame subtraction is performed to segment moving regions and moving vehicles can be detected by using appearance based classification. Experimental results show that our method has better overall performance (i.e. higher detection rate, lower false positive rate and higher detection speed) and is promising for real-time application.
Design & analysis of 16 bit RISC processor using low power pipelining

A 16 bit low power pipelined RISC processor is proposed by us in this paper, the RISC processor consists of the block mainly ALU, Universal shift register and Barrel Shifter. We have used modified Harvard architecture that uses separate memories for its instruction & data memory response where as in the other architecture by von Neumann, has only one shared memory for instruction and data, with one data bus and address bus with between data memory & processor memory. The remedial architectural modification has been made in incremental circuit utilized in carry select adder unit of the ALU in the RISC Processor. Operation in the core RISC Processor Fetch, Decode, execute, write back is implemented in the 2 stage pipelining with the positive edge & negative Edge. The process has been realized using XILINX ISE Design suit 13.2 & the Dynamic power is minimized in the RISC Core through the clock gating technique that is an efficient power technique and the total power estimation is done by the X Power analyzer. All the implementation is done in XILINX KINTEX XC7K1607-3fbg676 in it kit 28 nm technology are used. The simulation illustrate the total power dissipated by the processor to be 0.220 watt, and the Latency is 1.5 cycle.
Model-Driven Development of Command and Control Capabilities For Joint and Coalition Warfare

Warfighter operational requirements for Joint tactical battle management and command and control (BM/C2) now reflect warfare capabilities required of networked interoperable systems, as opposed to the previous single-system orientation. However, network-centric warfare won’t realize full combat potential until the Department of Defense (DoD) acquisition processes are geared toward developing system-of-systems materiel responses. DoD acquisition processes for solving interoperability must be transformed from a system-system interface basis, which leads to an unworkably complex N problem, to unprecedented (for DoD) holistic networked-system approaches. Initial such thrusts at the DoD-enterprise level, e.g., architecture frameworks, generalized technical architectures, Net Centric Enterprise Services, and the Net Centric Operations and Warfare Reference Model, are not sufficient to address the real time and complex adaptive system engineering challenges of Joint tactical BM/C2. The Joint Single Integrated Air Picture System Engineering Organization (JSSEO) is pioneering the technical and organizational processes needed for engineering development of information technology systems of systems, and prototyping DoD materiel solutions for network-centric operations. This paper discusses the JSSEO Model Driven ArchitectureTM approach to developing Joint tactical aerospace BM/C2, and points out the implications of broader application to Joint and Coalition command, control, communications, computer, intelligence, surveillance, and reconnaissance system-ofsystems network-centric transformation.
Flexible Wings and Fins: Bending by Inertial or Fluid-Dynamic Forces?1

Abstract Flapping flight and swimming in many organisms is accompanied by significant bending of flexible wings and fins. The instantaneous shape of wings and fins has, in turn, a profound effect on the fluid dynamic forces they can generate, with non-monotonic relationships between the pattern of deformation waves passing along the wing and the thrust developed. Many of these deformations arise, in part, from the passive mechanics of oscillating a flexible air- or hydrofoil. At the same time, however, their instantaneous shape may well emerge from details of the fluid loading. This issue—the extent to which there is feedback between the instantaneous wing shape and the fluid dynamic loading—is core to understanding flight control. We ask to what extent surface shape of wings and fins is controlled by structural mechanics versus fluid dynamic loading. To address this issue, we use a combination of computational and analytic methods to explore how bending stresses arising from inertial-elastic mechanisms compare to those stresses that arise from fluid pressure forces. Our analyses suggest that for certain combinations of wing stiffness, wing motions, and fluid density, fluid pressure stresses play a relatively minor role in determining wing shape. Nearly all of these combinations correspond to wings moving in air. The exciting feature provided by this analysis is that, for high Reynolds number motions where linear potential flow equations provide reasonable estimates of lift and thrust, we can finally examine how wing structure affects flight performance. Armed with this approach, we then show how modest levels of passive elasticity can affect thrust for a given level of energy input in the form of an inertial oscillation of a compliant foil.
CONSTRUCTIVE NEURAL NETWORKS: A REVIEW

In conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training process. In this paper, we review constructive neural network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constructive algorithm. Cascade 2 algorithm is a variant of CCA that is found to be more suitable for regression problems and is reviewed in this paper. We review our recently proposed two constructive algorithms that emphasize on architectural adaptation and functional adaptation during training. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layer nodes, as also the optimum value of the slope parameter of sigmoidal function. The role of adaptive sigmoidal activation function has been verified in constructive neural networks for better generalization performance and lesser training time.
Formants Based Analysis for Speech Recognition

Automatic speech recognition (ASR) is one of the most developing fields of the modern science having a wide range of applications. The purpose of this paper is to introduce statistical methods for speech recognition by extracting formants of the speech and analyzing their behavior. It is a novel way to speech recognition. The whole analysis for speech recognition is based upon first five formants of the speech. The method has been tested for Urdu speech, and result obtained is of high accuracy. There are 80 checkpoints, as a result of the algorithm (method), before Urdu speech is recognized. The method can be used for any other language as well for speech recognition
Capacity bounds for noncoherent fading channels with a peak constraint

A discrete-time single-user channel with temporally correlated Rayleigh fading is considered. Neither the transmitter nor the receiver has channel side information (CSI), and both peak and average power constraints are placed on the inputs. Two lower bounds to the capacity are presented. One is motivated by the technique of decision feedback decoding. The other is related to the information rate with side information present, minus a penalty term to account for the information about the channel that is learned at the receiver. The second lower bound is a slight variation of a bound of Shamai and Marzetta. The bounds are compared numerically to two upper bounds for a channel with Gauss Markov Rayleigh fading. One upper bound is the capacity for complete CSI, with the peak constraint ignored, and the other is based on the capacity per unit energy. In general, the gap between the upper and lower bounds depends on the channel memory, but is quite small for low SNR
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm

Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given. In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query. Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session.
A tool for automated design of language models

An interactive software tool for design and performance analysis of language models (LMs) is described. The tool obviates on-line simulation of the recognition system in which the LM is to be employed. By exploiting parallels with signal detection theory, a profile of the LM is given in a receiver-operating-characteristic-like (ROC) display.
Theory of Dual-sparse Regularized Randomized Reduction

In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e.g., random projection, random hashing), for large-scale high-dimensional classification. Previous theoretical results on randomized reduction methods hinge on strong assumptions about the data, e.g., low rank of the data matrix or a large separable margin of classification, which hinder their applications in broad domains. To address these limitations, we propose dual-sparse regularized randomized reduction methods that introduce a sparse regularizer into the reduced dual problem. Under a mild condition that the original dual solution is a (nearly) sparse vector, we show that the resulting dual solution is close to the original dual solution and concentrates on its support set. In numerical experiments, we present an empirical study to support the analysis and we also present a novel application of the dual-sparse regularized randomized reduction methods to reducing the communication cost of distributed learning from large-scale high-dimensional data.
Stabilization of remote control systems with unknown time varying delays by LMI techniques

In this paper, the stabilization of one class of remote control systems with unknown time varying delays is analysed and discussed using LMI techniques. A discrete time state space model under a static control law for remote control systems is first introduced based on some assumptions on the uncertain term. The time delay is unknown, time varying, and can be decomposed into two parts: one fixed part which is unknown and is an integer multiple of the sampling time; the other part which is randomly varying but bounded by one sampling time. Static controller designs based on delay dependent stability conditions are presented. This system is then extended to a more general case when the randomly varying part of the time delay is not limited to one sampling time. The derivative of the time delay is not limited to be bounded. Hence, the contributions are as follow: (i) for a given controller, we can use these stability criteria to test stability of the resulted system; (ii) we can design a remote controller to stabilize an unstable system. Finally, simulation examples are presented to show the effectiveness of the proposed method and to demonstrate remote stabilization of open loop unstable systems.