Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimentally validated bounding models for the Scout II quadrupedal robot

In this paper the authors discuss control and modeling issues significant for constructing simple running controllers and building experimentally validated simulation models for dynamically stable robots. A bounding controller resulting in dynamically stable running up to 1.3 m/s for the Scout II quadruped is presented. Simulation models are built and compared with experimental data to test the viability of various simplifying assumptions common in the literature for running robots. The authors demonstrate the need for including motor saturation and nor rigid torque transmission characteristics into simulation models. Similar issues are likely to be important in other legged robots as well. An extensive suite of experimental results documents the robot's performance and validates simulation models.
Response Time Approximations in Fork-Join Queues

Fork-join queueing networks model a network of parallel servers in which an arriving job splits into a number of subtasks that are serviced in parallel. Fork-join queues can be used to model disk arrays. A response time approximation of the fork-join queue is presented that attempts to comply with the additional constraints of modelling a disk array. This approximation is compared with existing analytical approximations of the fork-join queueing network.
News Analysis Based on Meta-synthesis Approach

The World Wide Web is a major source of the news now. The news analysis play more and more important roles in the political decision, financial analysis, invest decision, market forecast and so on. This paper proposes a news analysis system based on the hall for workshop of metasynthetic engineering. In this system, in order to analyses online news, the news articles are active fetched, and being classified into standard categories. And then by means of adaptive clustering, the correlative news is obtained into the same cluster, helping the user to reduce the time to search the news which he cares about. Moreover, the news comments are classified into categories such as negative, positive, which are also grouped into clusters helping the experts to get the view of the common people to the news. So the news attention analysis can be obtained to help the experts find which news is the people concerned most effectively.
A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this article we explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. We apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable.
A Fast Large Size Image Segmentation Algorithm Based on Spectral Clustering

As spectral clustering has the advantage of recognizing non-convex distribution, it has been widely used in image segmentation and other areas. However, when the spectral clustering algorithm deals with large size images, in order to get the affinity matrix, it costs a lot of computation time, so its applications are limited. To solve this problem, this paper proposes a fast image segmentation algorithm based on spectral clustering. Firstly, we separate the large size image into several smaller images which are segmented in advance, and combine the segmentation results of each smaller image. Then a point is randomly selected in the integrated results to constitute the feature data of the large size image. The feature data is clustered to get the image segmentation results by using the spectral clustering method. Finally, we show the effectiveness of the proposed algorithm by experiments and compare with the common used image segmentation method.
A New Version of Adaptive RED with Reduced Dependency on Parameterisation

The Random Early Detection (RED) Active queue management algorithm was created to achieve a high quality of service including high throughput and low delay, also to rectify some disadvantages found in Tail Drop mechanism. However there are many control parameters in RED that have large performance effects. In [4] the authors suggest different parameter setting guidelines, these setting are to be taken into account according to different traffic characteristics. Moreover average queue size in RED is very sensitive to the level of congestion and to RED parameter settings. Researchers in [5] tried to solve the problems with RED by making changes to the overall RED algorithm to introduce a new mechanism called Adaptive RED (ARED). According to ARED, when the average queue size is between minimum threshold 1 L and maximum threshold 2 L , then the maximum probability max Pd is multiplicatively scaled up by factor a or scaled down by factor ß depending on the current status of the traffic load. The main advantage of ARED is that it works adaptively for setting its parameters in response to the changing load. On the other hand the limitations are its vague optima l policy of parameter change and the fact that it adds more parameters to the RED algorithm. Our goal in this paper is to reduce the number of parameters that ARED uses by doing some modification to the ARED algorithm, while keeping the basic idea intact. In this algorithm we eliminate the two scaling factors a and ß and still manage to keep the average queue size fluctuating around the target queue size, which is set half way between the minimum threshold 1 L and the Maximum threshold 2 L , by scaling the maximum probability max Pd up and down according to a target queue size position in the queue, where max Pd is a function of the average queue size and the dropping probability . pd Java platform simulation is used to get the results from the proposed model and compare them to the adaptive RED results.
An Overview on Ad Hoc Networks

The wireless ad hoc networks consist of a collection of wireless nodes, that communicate over a common wireless medium. The nodes communicate without an infrastructure, such as base station, wired access point, etc. The establishment of the networks must be in a distributed and decentralized manner. Therefore, the complexity of the networks is in the nodes self. The nodes must be able to solve network’s problem, such as routing and security. Despite the technical challenges, the interests of the ad hoc networks increase rapidly in recent years, because they support mobility and are very well suited for many difficult situations, such rescue mission, military, vehicular communications, etc. In this overview article, I introduce the concept of wireless ad hoc networks and specially mobile ad hoc network (MANET), their architecture, purposes, applications, advantages, disadvantages, and comparison with infrastructure networks.
Ziggurat-based hardware Gaussian random number generator

An architecture and implementation of a high performance Gaussian random number generator (GRNG) is described. The GRNG uses the Ziggurat algorithm which divides the area under the probability density function into three regions (rectangular, wedge and tail). The rejection method is then used and this amounts to determining whether a random point falls into one of the three regions. The vast majority of points lie in the rectangular region and are accepted to directly produce a random variate. For the nonrectangular regions, which occur 1.5% of the time, the exponential or logarithm functions must be computed and an iterative fixed point operation unit is used. Computation of the rectangular region is heavily pipelined and a buffering scheme is used to allow the processing of rectangular regions to continue to operate in parallel with evaluation of the wedge and tail computation. The resulting system can generate 169 million normally distributed random numbers per second on a Xilinx XC2VP3O-6 device.
$(1+2u)$-constacyclic codes over $\mathbb{Z}_4+u\mathbb{Z}_4$

Let $R=\mathbb{Z}_4+u\mathbb{Z}_4,$ where $\mathbb{Z}_4$ denotes the ring of integers modulo $4$ and $u^2=0$. In the present paper, we introduce a new Gray map from $R^n$ to $\mathbb{Z}_{4}^{2n}. $ We study $(1+2u)$-constacyclic codes over $R$ of odd lengths with the help of cyclic codes over $R$. It is proved that the Gray image of $(1+2u)$-constacyclic codes of length $n$ over $R$ are cyclic codes of length $2n$ over $\mathbb{Z}_4$. Further, a number of linear codes over $\mathbb{Z}_4$ as the images of $(1+2u)$-constacyclic codes over $R$ are obtained.
MAD2: A scalable high-throughput exact deduplication approach for network backup services

Deduplication has been widely used in disk-based secondary storage systems to improve space efficiency. However, there are two challenges facing scalable high-throughput deduplication storage. The first is the duplicate-lookup disk bottleneck due to the large size of data index that usually exceeds the available RAM space, which limits the deduplication throughput. The second is the storage node island effect resulting from duplicate data among multiple storage nodes that are difficult to eliminate. Existing approaches fail to completely eliminate the duplicates while simultaneously addressing the challenges. This paper proposes MAD2, a scalable high-throughput exact deduplication approach for network backup services. MAD2 eliminates duplicate data both at the file level and at the chunk level by employing four techniques to accelerate the deduplication process and evenly distribute data. First, MAD2 organizes fingerprints into a Hash Bucket Matrix (HBM), whose rows can be used to preserve the data locality in backups. Second, MAD2 uses Bloom Filter Array (BFA) as a quick index to quickly identify non-duplicate incoming data objects or indicate where to find a possible duplicate. Third, Dual Cache is integrated in MAD2 to effectively capture and exploit data locality. Finally, MAD2 employs a DHT-based Load-Balance technique to evenly distribute data objects among multiple storage nodes in their backup sequences to further enhance performance with a well-balanced load. We evaluate our MAD2 approach on the backend storage of B-Cloud, a research-oriented distributed system that provides network backup services. Experimental results show that MAD2 significantly outperforms the state-of-the-art approximate deduplication approaches in terms of deduplication efficiency, supporting a deduplication throughput of at least 100MB/s for each storage component.
Decision Making through Polarized Summarization of User Reviews

When buying a mobile phone, booking an hotel, or watching a movie, many people rely on the reviews available on the Web. However, this huge amount of opinions make it difficult for users to have a comprehensive vision of the crowd judgments and to make an optimal decision. In this work we provide evidence that automatic text summarization of reviews can be used to design Web applications able to effectively reduce the decision making effort in domains where decisions are based upon the opinion of the crowd.
Subaru Weak-Lensing Survey II: Multi-Object Spectroscopy and Cluster Masses

We present the first results of a multi-object spectroscopic campaign to follow up cluster candidates located via weak lensing. Our main goals are to search for spatial concentrations of galaxies that are plausible optical counterparts of the weak-lensing signals, and to determine the cluster redshifts from those of member galaxies. Around each of 36 targeted cluster candidates, we obtained 15–32 galaxy redshifts. For 28 of these targets, we confirmed a secure cluster identification, with more than five spectroscopic galaxies within a velocity of ±3000km s^(-1). This includes three cases where two clusters at different redshifts are projected along the same line-of-sight. In 6 of the 8 unconfirmed targets, we found multiple small galaxy concentrations at different redshifts, each containing at least three spectroscopic galaxies. The weak-lensing signal around those systems was thus probably created by the projection of groups or small clusters along the same line-of-sight. In both of the remaining two targets, a single small galaxy concentration was found. In some candidate super-cluster systems, we found additional evidence of filaments connecting the main density peak to an additional nearby structure. For a subsample of our most cleanly measured clusters, we investigated the statistical relation between their weak-lensing mass (M_(NFW), σ_(SIS)) and the velocity dispersion of their member galaxies (σ_ν), comparing our sample with optically and X-ray selected samples from the literature. Our lensing-selected clusters are consistent with σ_ν = σ_(SIS), with a similar scatter to that of optically and X-ray selected clusters. We also derived an empirical relation between the cluster mass and the galaxy velocity dispersion, M_(200)E(z) = 11.0 x 10^(14) x (σ_ν/1000km s^(-1))^(3.0) h^(-1)M_⊙, which is in reasonable agreement with predictions of N-body simulations in the ΛCDM cosmology.
A Human CTL Recognizes a Caspase-8-Derived Peptide on Autologous HLA-B*3503 Molecules and Two Unrelated Peptides on Allogeneic HLA-B*3501 Molecules1

A CTL clone that recognizes autologous tumor cells was previously isolated from the blood of a head-and-neck cancer patient. The Ag was identified as peptide FPSDSWCYF presented by autologous HLA-B*3503 molecules. This peptide was encoded by a mutated CASP-8 gene, which is implicated in the triggering of apoptosis. Here, we show that this CTL clone, which expresses a single TCR, also recognizes two unrelated peptides on allogeneic HLA-B*3501 molecules. One peptide, HIPDVITY, is encoded by squalene synthase, and the other one, QFADVIVLF, is encoded by 2-hydroxyphytanoyl-CoA lyase. Both genes are expressed ubiquitously. These antigenic peptides are processed and presented by HLA-B*3501 cells. The two HLA-B35 alleles are closely related. Our results might reinforce the notion that the recognition of allogeneic HLA molecules depends on the presence in their groove of a limited number of peptides processed from ubiquitous proteins.
Improvements to fMPE for discriminative training of features

fMPE is a previously introduced form of discriminative training, in which offsets to the features are obtained by training a projection from a high-dimensional feature space based on posteriors of Gaussians. This paper presents recent improvements to fMPE, including improved high-dimensional features which are easier to compute, and improvements to the training procedure. Other issues investigated include cross-testing of fMPE transforms (i.e. using acoustic models other than those with which the fMPE was trained) and the best way to train the Gaussians used to obtain the vector of posteriors.
Haptic deformation modelling through cellular neural network

This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by nonlinear CNN activities. The novelty of the methodology is that soft object deformation is carried out from the perspective of energy propagation, and nonlinear material properties are modelled with nonlinear CNNs, rather than geometric nonlinearity as in most of the existing deformation methods. Integration with a haptic device has been achieved to simulate soft object deformation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but also easily accommodates isotropic, anisotropic and inhomogeneous materials, and local and large-range deformation.
Non-subsampled contourlet based deformable model for medical image segmentation

Artifacts involved in medical images like noise and intensity inhomogeneity makes an automated approach of segmentation failure. This paper proposed deformable model based automatic image segmentation model with above said constraints. The deformable models are sensitive to the noise and image intensities. Therefore use of non-subsampled contourlet transform is proposed to tackle these artifacts and to enhance images. These enhanced images are utilized along with region based deformable model for successful segmentation. The illustrated results on various types of medical images show the advantages of proposed model.
Poshida, a protocol for private information retrieval

Web Search Engines are the easy source to get data from Internet. WSE retrieve the information from the ocean of data according to the user generated queries. In return the WSE records the queries to build the user profile and retrieves the personalize search results. WSE sells the query log to marketing companies to generate revenue. This poses a threat to user privacy. In 2006 AOL released query log for research purpose but those logs were not properly anonymized, lead to the discovery of users. Performing private web search and achieving privacy is the active area of research. Many technique has been proposed to hide the identity of users from the WSE, However state of the art scheme is yet to device to privately retrieve information from WSE. This paper proposes a new protocol to obfuscate the user profile which is maintained by WSE. Profile Exposure level is used to measure the level of privacy a user achieve from WSE. Results show the protocol hides 50 percent of user profile at first degree 70% at second, 77% at third and more than 85% at fourth degree.
Intelligent Method of Identifying Degraded State for Electronic Devices Based on Neural Network and Fuzzy Integral

Hot Carrier Degraded(HCD), Electro-Static discharge(ESD) and Time-Dependent Dielectric Breakdown (TDDB) are the main failure modes of electronic devices. Identifying degraded state of electronic devices needs considering the combined effects of various factors. In this paper, On the basis of information such as real-time HCD damage, ESD damage, etc were obtained, with the help of fuzzy integral, which has the unique ability of information fusion, a new intelligent method of identifying degraded state of electronic devices was proposed based on neural network and fuzzy integral. Finally, an experiment based on a certain type of electronic device was introduced in detail, and the effectiveness of the intelligent method proposed in this paper was analyzed and validated by the experiment.
Viral and Bacterial Infections Induce Expression of Multiple NK Cell Receptors in Responding CD8+ T Cells1

NK cells express several families of receptors that play central roles in target cell recognition. These NK cell receptors are also expressed by certain memory phenotype CD8+ T cells, and in some cases are up-regulated in T cells responding to viral infection. To determine how the profile of NK receptor expression changes in murine CD8+ T cells as they respond to intracellular pathogens, we used class I tetramer reagents to directly examine Ag-specific T cells during lymphocytic choriomeningitis virus and Listeria monocytogenes infections. We found that the majority of pathogen-specific CD8+ T cells initiated expression of the inhibitory CD94/NKG2A heterodimer, the KLRG1 receptor, and a novel murine NK cell marker (10D7); conversely, very few Ag-specific T cells expressed Ly49 family members. The up-regulation of these receptors was independent of IL-15 and persisted long after clearance of the pathogen. The expression of CD94/NKG2A was rapidly initiated in naive CD8+ T cells responding to peptide Ags in vitro and on many of the naive T cells that proliferate when transferred into lymphopenic (Rag-1−/−) hosts. Thus, CD94/NKG2A expression is a common consequence of CD8+ T cell activation. Binding of the CD94/NKG2A receptor by its ligand (Qa-1b) did not significantly inhibit CD8+ T cell effector functions. However, expression of CD94 and NKG2A transgenes partially inhibited early events of T cell activation. These subtle effects suggest that CD94/NKG2A-mediated inhibition of T cells may be limited to particular circumstances or may synergize with other receptors that are similarly up-regulated.
Surveys of paua ( Haliotis iris ) off Nugget Point , southeast New Zealand

A survey of paua (Haliotis iris) conducted in the proposed marine reserve off Nugget Point showed that paua were common only in the southern part of the proposed reserve, probably because of the greater availability of habitat compared with the northern part. The abundance and size composition of paua from the southern part of the proposed reserve were similar to adjacent populations (Catlins north and south Otago). The abundance of juvenile paua from the southern part of the propcsed reserve was significantly greater than that of adjacent populations. The results did not permit an assessment of likely sustainable harvests for paua.
Annexin A1 restores Aβ1‐42‐induced blood–brain barrier disruption through the inhibition of RhoA‐ROCK signaling pathway

The blood–brain barrier (BBB) is composed of brain capillary endothelial cells and has an important role in maintaining homeostasis of the brain separating the blood from the parenchyma of the central nervous system (CNS). It is widely known that disruption of the BBB occurs in various neurodegenerative diseases, including Alzheimer's disease (AD). Annexin A1 (ANXA1), an anti‐inflammatory messenger, is expressed in brain endothelial cells and regulates the BBB integrity. However, its role and mechanism for protecting BBB in AD have not been identified. We found that β‐Amyloid 1‐42 (Aβ42)‐induced BBB disruption was rescued by human recombinant ANXA1 (hrANXA1) in the murine brain endothelial cell line bEnd.3. Also, ANXA1 was decreased in the bEnd.3 cells, the capillaries of 5XFAD mice, and the human serum of patients with AD. To find out the mechanism by which ANXA1 recovers the BBB integrity in AD, the RhoA‐ROCK signaling pathway was examined in both Aβ42‐treated bEnd.3 cells and the capillaries of 5XFAD mice as RhoA was activated in both cases. RhoA inhibitors alleviated Aβ42‐induced BBB disruption and constitutively overexpressed RhoA‐GTP (active form of RhoA) attenuated the protective effect of ANXA1. When pericytes were cocultured with bEnd.3 cells, Aβ42‐induced RhoA activation of bEnd.3 cells was inhibited by the secretion of ANXA1 from pericytes. Taken together, our results suggest that ANXA1 restores Aβ42‐induced BBB disruption through inhibition of RhoA‐ROCK signaling pathway and we propose ANXA1 as a therapeutic reagent, protecting against the breakdown of the BBB in AD.