Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cantilever-Type Uncooled Infrared Detector With High Fill-Factor and Low-Noise Characteristic

A capacitive microcantilever-type infrared (IR) detector having a unique structure that has high immunity to thermomechanical noise (TM-noise) is proposed. The device has a capacitive readout scheme and is compared with a conventional design using the same readout method by finite element model simulation. The total cantilever length was halved, compared with the conventional device structure, in order to increase the device's spring constant, and the IR absorber area was consequently increased as the portion of the leg in the given pixel area is decreased. Large spring constant and increased absorber area are the main causes of the TM-noise reduction. The feasibility of the device was shown by fabrication, and measured parameters demonstrated the structure's superiority. It was shown that the proposed structure potentially has low TM-noise and an overall noise-equivalent temperature difference (NETD) value that is lower than that of the conventional designed device. The NETD of the proposed device was found to be 5.7 mK.
Temporal-Needle: A view and appearance invariant video descriptor

The ability to detect similar actions across videos can be very useful for real-world applications in many fields. However, this task is still challenging for existing systems, since videos that present the same action, can be taken from significantly different viewing directions, performed by different actors and backgrounds and under various video qualities. Video descriptors play a significant role in these systems. In this work we propose the "temporal-needle" descriptor which captures the dynamic behavior, while being invariant to viewpoint and appearance. The descriptor is computed using multi temporal scales of the video and by computing self-similarity for every patch through time in every temporal scale. The descriptor is computed for every pixel in the video. However, to find similar actions across videos, we consider only a small subset of the descriptors - the statistical significant descriptors. This allow us to find good correspondences across videos more efficiently. Using the descriptor, we were able to detect the same behavior across videos in a variety of scenarios. We demonstrate the use of the descriptor in tasks such as temporal and spatial alignment, action detection and even show its potential in unsupervised video clustering into categories. In this work we handled only videos taken with stationary cameras, but the descriptor can be extended to handle moving camera as well.
Content based image retrieval with bin of color histogram

In this paper, we present a method to compare image similarity. This method uses a graph to represent a image. As a result, the technique is able to take advantage of smaller space overhead and is not sensitive to rotation and translation. We carried out an experiment on an image database containing 9,160 images. Experimental results show that our method is more effective than the local color histogram and cell/color histograms.
Executive function in children with externalizing and comorbid internalizing behavior problems.

BACKGROUND The goal of this study is to investigate differences in executive function (EF) in children with different levels of disruptive behavior problems (DBP). METHODS Ninety-three children between 7 and 12 years old with DBP were compared to 63 normally developing peers on a battery of EF tasks that varied in the amount of required emotion regulation ('hot' EF). RESULTS Differences in EF were found between DBP and comparison groups as indexed by hot EF tasks. Self-reported emotion scales, in conjunction with physiological recordings of heart rate, confirmed that emotions were elicited during hot EF. CONCLUSIONS Results suggest that difficulties in hot EF underlie externalizing problem behaviors in middle childhood.
An opportunistic-non orthogonal multiple access based cooperative relaying system over Rician fading channels

Non-orthogonal Multiple Access (NOMA) has become a salient technology for improving the spectral efficiency of the next generation 5G wireless communication networks. In this paper, the achievable average rate of an Opportunistic Non-Orthogonal Multiple Access (O-NOMA) based Cooperative Relaying System (CRS) is studied under Rician fading channels with Channel State Information (CSI) available at the source terminal. Based on CSI, for opportunistic transmission, the source immediately chooses either the direct transmission or the cooperative NOMA transmission using the relay, which can provide better achievable average rate performance than the existing Conventional-NOMA (C-NOMA) based CRS with no CSI at the source node. Furthermore, a mathematical expression is also derived for the achievable average rate and the results are compared with C-NOMA based CRS with no CSI at the transmitter end, over a range of increasing power allocation coefficients, transmit Signal-to-Noise Ratios (SNRs) and average channel powers. Numerical results show that the CRS using O-NOMA with CSI achieves better spectral efficiency in terms of the achievable average rate than the Conventional-NOMA based CRS without CSI. To check the consistency of the derived analytical results, Monte Carlo simulations are performed which verify that the results are consistent and matched well with the simulation results.
Conditional outage performance analysis framework for OFDM channels

The channel state information at the transmitter side (CSIT) is playing a more and more important role in the design of OFDM/OFDMA communication systems. Unfortunately, it is not trivial to conduct a performance analysis of OFDM systems with partial CSIT. Using the saddle-point approximation method, this paper will develop an analytical design and performance analysis framework for OFDM channels with 1 bit CSIT, which we shall refer to as the conditional outage exponent. The proposed framework will then allow us to present the fundamental relationship among the outage probability, transmission rate, SNR, outage capacity, delay-limited capacity, ergodic capacity, diversity-multiplexing tradeoff (DMT), finite-SNR DMT, and the number of diversity branches. It is surprising that the outage performance with 1 bit CSIT (conditional outage exponent) is worse than the corresponding one without CSIT (non-conditional outage exponent) in most cases. This counter-intuitive phenomenon occurs because the observation of the channel at the transmitter side will result in a state space collapse of the channel gains, i.e., from the prior probability space to the posterior probability space. As a result, the conditional outage exponent based framework can be easily used to design and evaluate the performance of existing and upcoming OFDM/OFDMA multichannel systems with 1 bit CSIT.
Research of Image Pre-processing Algorithm Based on FPGA

Abstract How to design a low-cost , reliable and real-time target recognition system with large amount of data has become a hot topic in the area of image processing . However, Edge detection has played an important role in target recognition system. The threshold of traditional canny edge detection algorithm must be setting by human, and has a large number of calculations. In order to overcome the shortcomings of the traditional Canny algorithm, proposing an adaptive threshold edge detection algorithm, and realizing it by hardware. This paper will introduce the implementation of the common low-level image processing algorithm in the FPGA, including color space convert module , edge extraction algorithms module , Hough transform module . The results of the experiment indicate that to realize the large amount of calculation of image processing by FPGA hardware logic, not only improves the effect of image processing, but also has high real-time!
Shape based 3D model retrieval without query

We describe our shape based 3D model retrieval method that is based on a browsing technique. With this method, users can retrieve the desired 3D model efficiently without a query model. In previous retrieval systems, users should provide a 3D model as a query to the system. Then, the system retrieves similar 3D models and returns them to the user. However, the problem of how to obtain the query model remains. With our method, users can retrieve the desired 3D model by walking through a virtual 3D space without the query model. At first, 3D shape features are extracted from all the 3D models, and 3D models are arranged and classified in the virtual 3D space so that similar 3D models are placed near each other. This allows the user to easily grasp where the 3D models similar to the desired one are located. After approaching the 3D model that is similar to the desired one, users can focus on all the nearby models, which are usually similar to the desired one. So users can find the desired one efficiently. We also developed two functions to make our method more efficient. Firstly, our method needs to render a large number of 3D models at one time quickly, so we developed a high-speed rendering method. Secondly, to make it easier for the user to choose the desired one from many 3D models, we developed a method to make 3D models face the direction from which users can recognize the shape of the 3D models easily. In addition, we present the results of experiments to evaluate the retrieval efficiency, which shows that our method is four times as fast as a retrieval method using a query model.
An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins

MOTIVATION All-alpha membrane proteins constitute a functionally relevant subset of the whole proteome. Their content ranges from about 10 to 30% of the cell proteins, based on sequence comparison and specific predictive methods. Due to the paucity of membrane proteins solved with atomic resolution, the training/testing sets of predictive methods for protein topography and topology routinely include very few well-solved structures mixed with a hundred proteins known with low resolution. Moreover, available predictors fail in predicting recently crystallised membrane proteins (Chen et al., 2002). Presently the number of well-solved membrane proteins comprises some 59 chains of low sequence homology. It is therefore possible to train/test predictors only with the set of proteins known with atomic resolution and evaluate more thoroughly the performance of different methods. RESULTS We implement a cascade-neural network (NN), two different hidden Markov models (HMM), and their ensemble (ENSEMBLE) as a new method. We train and test in cross validation the three methods and ENSEMBLE on the 59 well resolved membrane proteins. ENSEMBLE scores with a per-protein accuracy of 90% for topography and 71% for topology, outperforming the best single method of 7 and 5 percentage points, respectively. When tested on a low resolution set of 151 proteins, with no homology with the 59 proteins, the per-protein accuracy of ENSEMBLE is 76% for topography and 68% for topology. Our results also indicate that the performance of ENSEMBLE is higher than that of the best predictors presently available on the Web.
A comparison of antenna placement algorithms

Co-location of multiple antenna systems on a single fixed or mobile platform can be challenging due to a variety of factors, such as mutual coupling, individual antenna constraints, multipath, obstructions, and parasitic effects due to the platform. The situation frequently arises where a new communication capability, and hence antenna system, is needed on an existing platform. The problem of placing new antennas requires a long, manual effort in order to complete an antenna placement study. An automated procedure for determining such placements would not only save time, but would be able to optimize the performance of all co-located antenna systems. In this paper we examine a set of stochastic algorithms to determine their effectiveness at finding optimal placements for multiple antennas on a platform. To our knowledge, this is the first study to investigate optimizing multiple antenna placement on a single platform using multiple stochastic algorithms. Of the four algorithms studied, simulated annealing and evolutionary strategy were found to be most effective in finding optimal placements.
Software release management

A software release is a fully constructed version of a software product; it often consists of a hierarchy of files arranged in a directory structure. This hierarchy may contain many different types of files. Because of the World Wide Web, there is an increasing tendency for software to consist of a system of systems (i.e., to be assembled from pre-existing, independently produced, or independently released systems). It is not easy to manage software releases of such systems with simple file utilities, because, in these systems, tasks such as determining which files are in a release, which file permissions have changed from one release to another, and which files have changed in all the releases are very difficult. This paper discusses the problems of software release management (SRM) and outlines a prototype tool — the software release inventory manager (SRIM) — that addresses these problems. The SRIM automatically identifies the various files required for a software release, builds a system to track files and releases, validates information, and produces repeatable and controlled releases.
Patterns of Food Parenting Practices and Children’s Intake of Energy-Dense Snack Foods

Most previous studies of parental influences on children’s diets included just a single or a few types of food parenting practices, while parents actually employ multiple types of practices. Our objective was to investigate the clustering of parents regarding food parenting practices and to characterize the clusters in terms of background characteristics and children’s intake of energy-dense snack foods. A sample of Dutch parents of children aged 4–12 was recruited by a research agency to fill out an online questionnaire. A hierarchical cluster analysis (n = 888) was performed, followed by k-means clustering. ANOVAs, ANCOVAs and chi-square tests were used to investigate associations between cluster membership, parental and child background characteristics, as well as children’s intake of energy-dense snack foods. Four distinct patterns were discovered: “high covert control and rewarding”, “low covert control and non-rewarding”, “high involvement and supportive” and “low involvement and indulgent”. The “high involvement and supportive” cluster was found to be most favorable in terms of children’s intake. Several background factors characterized cluster membership. This study expands the current knowledge about parental influences on children’s diets. Interventions should focus on increasing parental involvement in food parenting.
Future Impact of Various Interventions on the Burden of COPD in Canada: A Dynamic Population Model

Background Chronic obstructive pulmonary disease (COPD) is a growing economic burden worldwide. Smoking cessation is thought to be the single most effective way of reducing the economic burden of COPD. The impact of other strategies such as interventions that predict risk of disease, reduce progression of disease, or reduce exacerbations has not been systematically studied. Objectives We estimated the economic and clinical burden of COPD over the next 25 years in Canada and the impact of three potential interventions (screening test for predisposition to COPD, new drugs to avoid progression into more severe disease stages, and predictive test for exacerbations) on COPD burden. Methods Using a dynamic simulation model, we projected the total burden of COPD (cost, morbidity, and mortality) from 2011 to 2035 using the population of Canada as a case study. The model stratified population based on sex, age, smoking status, respiratory symptoms, and their COPD stage. The cost and quality adjusted life years (QALYs) associated with each intervention were estimated. Results The model indicates that annual societal cost of COPD is $4.52 billion (B) Canadian dollars in 2011 and will reach $3.61B ($7.33B undiscounted) per year in 2035. Over the next 25 years, COPD will be responsible for approximately $101.4B in societal costs ($147.5B undiscounted) and 12.9 million QALYs lost (19.0 million undiscounted). Our results suggested that the best strategy to reduce the financial burden of COPD is by reducing exacerbations. Smoking cessation, while it is the cornerstone of COPD prevention, has only a modest effect in attenuating the financial burden of COPD over the next 25 years in Western countries such as Canada. Conclusion Our data suggest that any intervention that can reduce the number of exacerbations has a substantial impact on morbidity and costs of COPD and should be considered in conjunction with the ongoing efforts to reduce smoking rates.
Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.
An amplified promoter system for targeted expression of calcium indicator proteins in the cerebellar cortex

Recording of identified neuronal network activity using genetically encoded calcium indicators (GECIs) requires labeling that is cell type-specific and bright enough for the detection of functional signals. However, specificity and strong expression are often not achievable using the same promoter. Here we present a combinatorial approach for targeted expression and single-cell-level quantification in which a weak promoter is used to drive trans-amplification under a strong general promoter. We demonstrated this approach using recombinant adeno-associated viruses (rAAVs) to deliver the sequence of the GECI D3cpv in the mouse cerebellar cortex. Direct expression under the human synapsin promoter (hSYN) led to high levels of expression (50–100 μM) in five interneuron types of the cerebellar cortex but not in Purkinje cells (PCs) (≤10 μM), yielding sufficient contrast to allow functional signals to be recorded from somata and processes in awake animals using two-photon microscopy. When the hSYN promoter was used to drive expression of the tetracycline transactivator (tTA), a second rAAV containing the bidirectional TET promoter (Ptetbi) could drive strong D3cpv expression in PCs (10–300 μM), enough to allow reliable complex spike detection in the dendritic arbor. An amplified approach should be of use in monitoring neural processing in selected cell types and boosting expression of optogenetic probes. Additionally, we overcome cell toxicity associated with rAAV injection and/or local GECI overexpression by combining the virus injection with systemic pre-injection of hyperosmotic D-mannitol, and by this double the time window for functional imaging.
The switchboard optimization problem and heuristics for cut-through networking

With software defined networking rapidly becoming industry default, the cut-through mode of switching is becoming increasingly harder to achieve in practice. On the other hand, cut-through networking remains firmly in focus in several fields where top network performance is required, where supercomputing and datacenter interconnect are two popular examples. In earlier work, cut-through mode on end-to-end paths was achieved by emulating circuit switching over traditional packet networks. This paper focuses on communication nodes themselves and attempts to solve the practical problem that datacenters encounter when implementing end-to-end circuits. This paper models the problem as an N-by-M switchboard, where N is the number of physical ports and M is the number of ports that compete for the bandwidth on each port in N. The optimization problem is of the bin-packing variety with unique aspects related to the cut-through mode. This paper solves the problem using Genetic Algorithm and proposes an effective heuristic to speed up the optimization.
Cleavage of Bid May Amplify Caspase-8-Induced Neuronal Death Following Focally Evoked Limbic Seizures

The mechanism by which seizures induce neuronal death is not completely understood. Caspase-8 is a key initiator of apoptosis via extrinsic, death receptor-mediated pathways; we therefore investigated its role in mediating seizure-induced neuronal death evoked by unilateral kainic acid injection into the amygdala of the rat, terminated after 40 min by diazepam. We demonstrate that cleaved (p18) caspase-8 was detectable immediately following seizure termination coincident with an increase in cleavage of the substrate Ile-Glu-Thr-Asp (IETD)-p-nitroanilide and the appearance of cleaved (p15) Bid. Expression of Fas and FADD, components of death receptor signaling, was increased following seizures. In vivo intracerebroventricular z-IETD-fluoromethyl ketone administration significantly reduced seizure-induced activities of caspases 8, 9, and 3 as well as reducing Bid and caspase-9 cleavage, cytochrome c release, DNA fragmentation, and neuronal death. These data suggest that intervention in caspase-8 and/or death receptor signaling may confer protection on the brain from the injurious effects of seizures.
Lovastatin inhibits brain endothelial cell Rho‐mediated lymphocyte migration and attenuates experimental autoimmune encephalomyelitis

Neuroinflammatory diseases, such as multiple sclerosis (MS), result from aberrant leukocyte traffic into the central nervous system (CNS). To breach the specialized blood‐brain barrier, activated leukocytes interact with CNS endothelial cells (EC) and activate a CD54‐mediated signaling pathway controlling the Rho GTPase. To function correctly Rho requires posttranslational prenylation, and this can be inhibited by depleting the supply of isoprenoids through inhibition of the cholesterol synthesis pathway with 3‐hydroxy‐3‐methylglutaryl CoA reductase (HMG‐CoA reductase) inhibitors (statins). Here we show that treatment of brain EC in vitro with lovastatin inhibits Rho‐mediated transendothelial T cell migration. This effect can be reversed by supplementation with mevalonolactone, the downstream product of HMG‐CoA reductase, or by ectopic expression of myristoylated Rho, which remains active in the absence of prenylation. In a relapsing‐remitting mouse model of MS, lovastatin treatment inhibited leukocyte migration into the CNS and significantly attenuated the development of both acute and relapsing clinical disease. These studies demonstrate that the indirect pharmacological inhibition of Rho proteins in brain EC by statins can inhibit a key stage in the pathogenesis of neuroinflammation, namely leukocyte migration across the blood‐brain barrier. These studies demonstrate a novel effect of statins in modulating the immune response in neuroinflammtory diseases and may provide additional rationale for their use in the treatment of MS.