Miracle-dz/UniFAR-FTUs huggingface dataset:
A heterogeneous CPU–GPU parallel approach to a multigrid Poisson solver for incompressible fluid simulation

One of the major obstacles in incompressible fluid simulations is the projection step that enforces zero divergence of the velocity field. We propose a novel heterogeneous CPU–GPU parallel multigrid Poisson solver that decomposes the high‐frequency components of the residual field using a wavelet decomposition and conducts an additional smoothing process on them, using the CPU, while the GPU is performing projection at the coarsest level. In example animations of smoke and turbulent flow with thermal buoyancy, this additional smoothing improves the accuracy of the parallel multigrid Poisson solver in a single multigrid cycle and reduces the number of multigrid cycles required to reach a specified accuracy. Copyright © 2013 John Wiley & Sons, Ltd.
Unsupervised Feature Selection on Data Streams

Massive data streams are continuously being generated from sources such as social media, broadcast news, etc., and typically these datapoints lie in high-dimensional spaces (such as the vocabulary space of a language). Timely and accurate feature subset selection in these massive data streams has important applications in model interpretation, computational/storage cost reduction, and generalization enhancement. In this paper, we introduce a novel unsupervised feature selection approach on data streams that selects important features by making only one pass over the data while utilizing limited storage. The proposed algorithm uses ideas from matrix sketching to efficiently maintain a low-rank approximation of the observed data and applies regularized regression on this approximation to identify the important features. We theoretically prove that our algorithm is close to an expensive offline approach based on global singular value decompositions. The experimental results on a variety of text and image datasets demonstrate the excellent ability of our approach to identify important features even in presence of concept drifts and also its efficiency over other popular scalable feature selection algorithms.
Using frequency distance filteration for reducing database search workload on GPU-based cloud service

The Smith-Waterman algorithm is the most widely used algorithm to analyze the similarity between protein and DNA sequences and suitable for the database search due to its high sensitivity. However, Smith-Waterman still is a very time-consuming method. CUDA programming can efficiently improve the computations by using the computing power of the massive computing hardware as GPUs. In this paper, we proposed an efficient frequency based filter method instead of just speed up the Smith-Waterman comparison but waste computing resource to deal with those unnecessary comparisons. We implemented the Smith-Waterman algorithm by introduction of the techniques from earlier researches and add in our real-time filter method on Graphic Processing Units to filter unnecessary comparisons. We also design a user friendly interface to provide the service in the potential clouding computing environment. In our research we choose two data sets, H1N1 VH protein database and Human protein database then compare CUDA-SW and CUDA-SW with filter, we called CUDA-SWf we can obtain up to 41% performance improve from reduce unnecessary sequence alignments.
Scene recognition using quantitative measurement of intensity flicker

In this paper, we propose a new scheme to recognize old-film scene and modern scene. The proposed method automatically divides a video into several scenes. An adaptive histogram transform is performed on frames to yield flicker-free frames, and intensity flicker is measured and defined as the difference between original frame and flicker-free frame. A quantitative measurement of intensity flicker is computed as feature. Moreover, using color feature and the results of scene recognition, the proposed method identifies the characteristic of video and estimates the era of film in three periods, including, early period, middle period, and modern period. The experiment results will demonstrate that the proposed method not only recognizes old-film scene and modern scene, but it can handle video identification and era estimation of film and video.
An algorithm based on LBPV and MIL for left atrial thrombi detection using transesophageal echocardiography

Transesophageal echocardiography (TEE) is widely used to detect left atrium (LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary pattern variance (LBPV) features are extracted from region of interest (ROI). And the dynamic features are formed by using the information of its neighbor frames in the sequence. The sequence is viewed as a bag, and the ROIs in the sequence are considered as the instances. Multiple-instance learning (MIL) method is employed to solve the LAA thrombi detection. The experimental results show that the proposed method can achieve better performance than that by using other methods.
To Know a Bāthā: Family Language Socialization among Buddhist Immigrants from Myanmar in New York City

While there is a rising number of research studies on heritage language maintenance among East Asian immigrants, there have been very few studies on Southeast Asian minority heritage language maintenance. The purpose of this paper is to shed light on the practices and ideologies towards heritage language maintenance in two Buddhist immigrant families from Myanmar residing in New York City. This paper approaches the topic of heritage language maintenance by employing Elinor Ochs and Bambi Schieffelin’s (1984) theoretical framework on language socialization—socializing U.S. born Burmese children to become competent members of both their heritage and American societies. The findings suggest that the socio-semantically ambiguous concept bāthā in Burmese referencing both language and religion leads to the prioritization of religious socialization over language socialization in the process of heritage language maintenance. Although the former could compliment the latter, both families seemed to be satisfied by the achievement of the former. By doing so, the participants in this study however achieve in successfully compromising between full assimilation to mainstream American society as a minority immigrant community and maintaining their heritage roots, even if not linguistically.
MOTION PRIMITIVES FOR AN AUTOROTATING HELICOPTER

In this paper, we introduce a design criterion for motion primitives of a helicopter when engine failure occurs. A flight plan followed by such a helicopter after it has established autorotation consists of concatenation of such well-defined motion primitives. A library of primitives act as an useful finite action space for a motion planner to search over. Subject to the constrained flight dynamics, a set of useful trim or steady state motions are identified which either maximize glide slope or minimize descent speed. Incorporating these trim motions allows the system to coast along a trajectory for an arbitrary amount of time. To transition from one trim to another, a set of maneuvers or finite time trajectories are defined and solved for using optimal control.
Securing ZigBee smart energy profile 1.x with OpenECC library

ZigBee Smart Energy is the world's leading standard for interoperable products that monitor, control, inform and automate the delivery and use of energy and water. The ZigBee Smart Energy Profile (SEP) specification uses elliptic curve cryptography (ECC) for implementing certificate-based key establishment (CBKE) between communicating devices. This paper presents the design, implementation, and evaluation of OpenECC, the first open-source library supporting ECC-based protocols for ZigBee SEP version 1.x. The experimental results demonstrate the high performance and small memory footprint of the OpenECC library when deployed on the current generation of Zigbee Smart Energy platform. Our work is the first step towards securing ZigBee Smart Energy applications using open-source libraries.
Extraction of a Multi-Dimensional Polynomial Device Model for an Improved Distortion Contribution Analysis Technique

This paper presents a method to characterize a multi-dimensional polynomial current and charge source models needed for a distortion contribution analysis technique that can identify the dominant nonlinearities and different mixing mechanisms. A separate model characterization step with carefully chosen test signals is used to break the inherent input-output correlation that otherwise complicates the model fitting. The proposed Volterra-on-top-of-harmonic-balance technique and improved polynomial fitting are then verified through the analysis of a 100-W LDMOS RF Doherty amplifier, which showed that, in this circuit, the cubic nonlinearity of the gate capacitance is the main culprit of AM-PM distortion.
Incremental Calculation for Fully Adaptive Algorithms

The step complexity of fully adaptive (FA) algorithms depends only on the contention during an operation, when counting both local computation and accesses to shared registers. This paper contributes to the design of efficient fully adaptive algorithms by specifying and implementing two generic objects, Gather&f and Collect&f . A Gather&f object returns the value of applying a function f on the information previously stored in the object. In order to reduce the local step complexity, our implementation of a Gather&f calculates f incrementally, as values are stored in the object. This implementation uses only read and write operations on the shared memory. It has O(k) local and shared step complexity for calculating f and O(k) for storing information, where k is the point contention during the operation’s execution interval. A Collect&f further guarantees that the value of f returned by a later collect&f operation does not reflect fewer store operations than a (strictly) earlier collect&f operation. A simple implementation has O(k) local and shared step complexity for calculating f and for storing information. For common applications where collect&f is repeatedly invoked, the paper presents an efficient object called ECollect&f . In this object, storing information takes a single step and calculating f has O(k) local and shared step complexity. To demonstrate the applicability of Gather&f and ECollect&f , we use them to improve the local and shared step complexity of atomic snapshot and immediate snapshot to O(k) and O(k), respectively.
Soft Dependency Constraints for Reordering in Hierarchical Phrase-Based Translation

Long-distance reordering remains one of the biggest challenges facing machine translation. We derive soft constraints from the source dependency parsing to directly address the reordering problem for the hierarchical phrase-based model. Our approach significantly improves Chinese--English machine translation on a large-scale task by 0.84 BLEU points on average. Moreover, when we switch the tuning function from BLEU to the LRscore which promotes reordering, we observe total improvements of 1.21 BLEU, 1.30 LRscore and 3.36 TER over the baseline. On average our approach improves reordering precision and recall by 6.9 and 0.3 absolute points, respectively, and is found to be especially effective for long-distance reodering.
MGR: Multi-level global router

Global routing faces an increasing problem size and urgent demand on improvement in solution quality. Despite of the recent developments of global routers, there exist only two types of choices: slow 3D routers with good solution quality or efficient 2D routers with relatively poor solution quality. We propose a multi-level 3D global router called MGR to fill the gap. MGR resorts to an efficient multi-level framework to reroute nets in the congested region on the 3D grid graph. Routing on the coarsened grid graph speeds up the global router while 3D routing introduces less vias. The powerful multi-level rerouting framework wraps three innovative routing techniques together: adaptive resource reservation in coarsening process, a new 3-terminal maze routing algorithm and network flow based solution propagation in uncoarsening process. As a result, MGR can achieve the solution quality close to 3D routers with comparable runtime of 2D routers.
User-aided footprint extraction for appliance modelling in Non-Intrusive Load Monitoring

In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of the appliance. Since the NILM system has only the aggregated power consumption available, the recorded footprint might be corrupted by other appliances, which can not be turned off during this period, i.e., the fridge and freezer in the household. Furthermore, the user needs a facilitated procedure, in order to obtain a clean footprint from the aggregated power signal in real scenario. Therefore, a user-aided footprint extraction procedure is needed. In this work, this procedure is defined as a NILM problem with two sources, i.e., the desired appliance and the fridge-freezer combination. One of the resulting disaggregated profiles of the algorithm corresponds to the extracted footprint. Then, this is used for the appliance modelling stage to create te corresponding Hidden Markov Model (HMM), suitable for the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The effectiveness of the footprint extraction procedure is evaluated through the confidence of the disaggregation output of a real problem, using a span of 30 days data taken from two different datasets (AMPds, ECO). The experiments are conducted using the HMM from the extracted footprint, compared to the confidence of the same problem using the HMM from the true footprint, as appliance level consumption. The results show that the performance are comparable, with the worst relative F1 loss of 3.83%, demonstrating the effectiveness of the footprint extraction procedure.
Trajectory tracking and consensus of SISO linear multi-agent systems with formation changes

In this paper, a decentralized control scheme for group formation and trajectory tracking of multi-agent systems is designed. In this scheme, each agent dynamics is represented by a single-input single-output linear systemand the information that each controller requires is only the output of the others agents. With the proposed decentralized control law, multiagent system tracks asymptotically the trajectory and formation of the agents, even in the presence of smooth changes of formations. Moreover, the agent dynamics (structure and dimension) are not restricted to be the same, as usually assumed in the literature. A simulation example is provided in order to illustrate the application of the proposed control.
Genomewide Identification of Sko1 Target Promoters Reveals a Regulatory Network That Operates in Response to Osmotic Stress in Saccharomyces cerevisiae

ABSTRACT In Saccharomyces cerevisiae, the ATF/CREB transcription factor Sko1 (Acr1) regulates the expression of genes induced by osmotic stress under the control of the high osmolarity glycerol (HOG) mitogen-activated protein kinase pathway. By combining chromatin immunoprecipitation and microarrays containing essentially all intergenic regions, we estimate that yeast cells contain approximately 40 Sko1 target promoters in vivo; 20 Sko1 target promoters were validated by direct analysis of individual loci. The ATF/CREB consensus sequence is not statistically overrepresented in confirmed Sko1 target promoters, although some sites are evolutionarily conserved among related yeast species, suggesting that they are functionally important in vivo. These observations suggest that Sko1 association in vivo is affected by factors beyond the protein-DNA interaction defined in vitro. Sko1 binds a number of promoters for genes directly involved in defense functions that relieve osmotic stress. In addition, Sko1 binds to the promoters of genes encoding transcription factors, including Msn2, Mot3, Rox1, Mga1, and Gat2. Stress-induced expression of MSN2, MOT3, and MGA1 is diminished in sko1 mutant cells, while transcriptional regulation of ROX1 seems to be unaffected. Lastly, Sko1 targets PTP3, which encodes a phosphatase that negatively regulates Hog1 kinase activity, and Sko1 is required for osmotic induction of PTP3 expression. Taken together our results suggest that Sko1 operates a transcriptional network upon osmotic stress, which involves other specific transcription factors and a phosphatase that regulates the key component of the signal transduction pathway.
Damage Accumulation in Cyclically-Loaded Glass-Ceramic Matrix Composites Monitored by Acoustic Emission

Barium osumilite (BMAS) ceramic matrix composites reinforced with SiC-Tyranno fibers are tested in a cyclic loading protocol. Broadband acoustic emission (AE) sensors are used for monitoring the occurrence of different possible damage mechanisms. Improved use of AE indices is proposed by excluding low-severity signals based on waveform parameters, rather than only threshold criteria. The application of such improvements enhances the accuracy of the indices as accumulated damage descriptors. RA-value, duration, and signal energy follow the extension cycles indicating moments of maximum or minimum strain, while the frequency content of the AE signals proves very sensitive to the pull-out mechanism.
Carbon Monoxide Induced PPARγ SUMOylation and UCP2 Block Inflammatory Gene Expression in Macrophages

Carbon monoxide (CO) dampens pro-inflammatory responses in a peroxisome proliferator-activated receptor-γ (PPARγ) and p38 mitogen-activated protein kinase (MAPK) dependent manner. Previously, we demonstrated that CO inhibits lipopolysaccharide (LPS)-induced expression of the proinflammatory early growth response-1 (Egr-1) transcription factor in macrophages via activation of PPARγ. Here, we further characterize the molecular mechanisms by which CO modulates the activity of PPARγ and Egr-1 repression. We demonstrate that CO enhances SUMOylation of PPARγ which we find was attributed to mitochondrial ROS generation. Ectopic expression of a SUMOylation-defective PPARγ-K365R mutant partially abolished CO-mediated suppression of LPS-induced Egr-1 promoter activity. Expression of a PPARγ-K77R mutant did not impair the effect of CO. In addition to PPARγ SUMOylation, CO-activated p38 MAPK was responsible for Egr-1 repression. Blocking both CO-induced PPARγ SUMOylation and p38 activation, completely reversed the effects of CO on inflammatory gene expression. In primary macrophages isolated form C57/BL6 male mice, we identify mitochondrial ROS formation by CO as the upstream trigger for the observed effects on Egr-1 in part through uncoupling protein 2 (UCP2). Macrophages derived from bone marrow isolated from Ucp2 gene Knock-Out C57/BL6 mice (Ucp2 −/−), produced significantly less ROS with CO exposure versus wild-type macrophages. Moreover, absence of UCP2 resulted in a complete loss of CO mediated Egr-1 repression. Collectively, these results indentify p38 activation, PPARγ-SUMOylation and ROS formation via UCP2 as a cooperative system by which CO impacts the inflammatory response.
Octave-tunable miniature RF resonators

The development and testing of a miniaturized, high-Q, broadly tunable resonator is described. An exemplary device, with a center frequency that is continuously tunable from 1.2 to 2.6GHz, was tested in detail. Experimental results demonstrated a resonator Q of up to 380, and typical insertion loss of -1.9dB for a 25MHz 3-dB bandwidth. These resonators have been used to stabilize a broadly-tunable oscillator with phase noise of -132dBc/Hz at 100-kHz offset, with a center frequency tunable from 1.2-2.6GHz, and a tuning speed of 1GHz/ms.
An algebra for signal processing

Our paper presents an attempt to axiomatise signal processing. Our long-term goal is to formulate signal processing algorithms for an ideal world of exact computation and prove properties about them, then interpret these ideal formulations and apply them without change to real world discrete data. We give models of the axioms that are based on Gaussian functions, that allow for exact computations and automated tests of signal algorithm properties.
Fast object detection and segmentation in MPEG compressed domain

We present a fast algorithm for object detection and segmentation in the MPEG compressed domain using color clustering, region merging based on spatiotemporal similarities, background/foreground classification, and pixel edge extraction. The features extracted from the blocks of the segmented object in the compressed domain can be used for fast object tracking and indexing at low level. Moreover, these blocks can be decompressed to obtain details of a specific object in the pixel domain and can be used for high level indexing. By using the proposed algorithm, we can reduce the amount of the information needed to be processed, and therefore, save the computational time, and increase the processing speed. Also we need to perform an inverse DCT on only some parts of the image.
Modelling, simulating and visualising the Cahn-Hilliard-Cook field equation

The Cahn-Hilliard-Cook equation continues to be a useful model describing binary phase separation in systems such as alloys and other physical and chemical applications. We describe our investigation of this field equation and report on the various discretisation schemes we used to integrate the system in one-, two- and three-dimensions. We also discuss how the equation can be visualised effectively in these different dimensions and consider how these techniques can usefully be applied to other partial differential equations.
The far‐infrared vibration–rotation–tunneling spectrum of the water tetramer‐d8

The far‐infrared vibration–rotation–tunneling spectrum of (D2O)4 has been measured in the spectral region near 2.04 THz. Observation of additional transition doublets with a constant 5.6 MHz spacing in a parallel (c‐type) spectrum extends the first detailed study of this cluster [Science 271, 59 (1996)]. Three possibilities are explored for the origin of this small splitting: tunneling between degenerate equilibrium structures via facile torsional motions analogous to those observed in the water trimer, tunneling between nondegenerate structural frameworks, and tunneling made feasible only through excitation of a specific vibrational coordinate. The degenerate tunneling scheme best accounts for the spectral features, although the precise dynamics responsible for the observed spectral features cannot be uniquely established from the present data. A further doubling of spectral features, observed only in the K=2 manifold of transitions for J≥3, is symmetric about the unperturbed symmetric top energy levels .
Distance Adaptive Dynamic Routing and Spectrum Allocation in Elastic Optical Networks With Shared Backup Path Protection

This paper considers distance adaptive dynamic routing and spectrum assignment (RSA) for elastic optical networks with shared backup path protection (SBPP). Efficient heuristic algorithms based on spectrum window planes are proposed for implementing distance and modulation format adaptive RSA so as to maximize spare capacity sharing among multiple protection lightpaths. A differentiated sharable frequency slot cost was also defined for the first time to more efficiently share protection resource. Network performance is evaluated in terms of bandwidth blocking probability (BBP) through simulations. The proposed SBPP technique is effective in reducing BBP and improving spectral efficiency compared to conventional SBPP schemes and 1+1 path protection. The impact of transponder tunability on bandwidth blocking performance is also evaluated to show that a limited tuning range is sufficient to achieve a BBP performance close to that with full tunability.