Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Algorithms under Asymmetric Read and Write Costs

In several emerging technologies for computer memory (main memory) the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fundamentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We consider both the case in which all but O(1) memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the (M, ω)-ARAM, in which there is a small (symmetric) memory of size M , a large unbounded (asymmetric) memory, both random access, and the cost of reading from the large memory is unit, but the cost of writing is ω ≥ 1. For FFT and sorting networks we show a lower bound cost of Ω(ω(n log ωM n)). For the FFT we show a matching upper bound, which indicates it is only possible to achieve asymptotic improvements with cheaper reads when ω > M. For sorting networks we show an asymptotic gap between the cost of sorting networks and comparison sorting in the model. This is in contrast with the RAM, and most other models, in which the asymptotic costs are the same. We also show a lower bound for computations on an n × n diamond DAG of Ω(ωn 2 /M) cost, which indicates no asymptotic improvement is achievable with fast reads. However, we show that for the minimum edit distance problem (and related problems), which would seem to be a diamond DAG, we can improve on this lower bound getting O(ωn 2 /(M min(ω 1/3 , M 1/2))) cost. To achieve this we make use of a " path sketch " technique that is forbidden in a strict DAG computation. Finally we show several interesting upper bounds for shortest path problems, minimum-spanning trees, and other problems. A common theme in many of the upper bounds is that they require redundant computation and a tradeoff between reads and writes.
Ethernet Based Remote Monitoring And Control Of Temperature By Using Rabbit Processor

Networking is a major component of the processes and control instrumentation systems as the network’s architecture solves many of the Industrial automation problems. There is a great deal of benefits in the process of industrial parameters to adopt the Ethernet control system. Hence an attempt has been made to develop an Ethernet based remote monitoring and control of temperature. In the present work the experimental result shows that remote monitoring and control system (RMACS) over the Ethernet.
On Connectivity and Robustness in Random Intersection Graphs

Random intersection graphs have received much attention recently and been used in a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks. For these graphs, each node is equipped with a set of objects in a random manner, and two nodes have an undirected edge in between if they have at least one object in common. In this paper, we investigate connectivity and robustness in a <italic>general random intersection graph</italic> model. Specifically, we establish sharp asymptotic zero-one laws for <inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula>-connectivity and <inline-formula><tex-math notation="LaTeX"> $k$</tex-math></inline-formula>-robustness, as well as the asymptotically exact probability of <inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula>-connectivity, for any positive integer <inline-formula> <tex-math notation="LaTeX">$k$</tex-math></inline-formula>. The <inline-formula><tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-connectivity property quantifies how resilient is the connectivity of a graph against node or edge failures, while <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-robustness measures the effectiveness of <italic>local-information-based</italic> consensus algorithms (which do not use global graph topology information) in the presence of adversarial nodes. In addition to presenting the results under the general random intersection graph model, we consider two special cases of the general model, a <italic>binomial</italic> random intersection graph and a <italic>uniform</italic> random intersection graph, which both have numerous applications as well. For these two specialized graphs, we present asymptotically exact probabilities of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-connectivity and asymptotic zero-one laws for <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-robustness.
E cient Bulk Operations on Dynamic R-trees

In recent years, there has been an upsurge of interest in spatial databases. A major issue is how to e ciently manipulate massive amounts of spatial data stored on disk in multidimensional spatial indexes (data structures). Construction of spatial indexes (bulk loading) has been studied intensively in the database community. The continuous arrival of massive amounts of new data makes it important to e ciently update existing indexes (bulk updating). In this paper, we present a simple, yet e cient technique for performing bulk update and query operations on multidimensional indexes. We present our technique in terms of the so-called R-tree and its variants, as they have emerged as practically e cient indexing methods for spatial data. Our method uses ideas from the bu er tree lazy bu ering technique and fully utilizes the available internal memory and the page size of the operating system. We give a theoretical analysis of our technique, showing that it is e cient both in terms of I/O communication, disk storage, and internal computation time. We also present the results of an extensive set of experiments showing that in practice our approach performs better than the previously best known bulk update methods with respect to update time, and that it produces a better quality index in terms of query performance. One important novel feature of our technique is that in most cases it allows us to perform a batch of updates and queries simultaneously. To be able to do so is essential in environments where queries have to be answered even while the index is being updated and reorganized. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC 27708, USA. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04{96{1{0013 and by the National Science Foundation through ESS grant EIA{9870734 and CAREER grant EIA{9984099. Email: large@cs.duke. edu. Westfalische Wilhelms-Universit at, Institut f ur Informatik, 48149 M unster, Germany. Email: khh@math.uni-muenster.de. Westfalische Wilhelms-Universit at, Institut f ur Informatik, 48149 M unster, Germany. Part of this work was done while a visiting scholar at Duke University. Email: jan@math.uni-muenster.de. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC 27708, USA. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04{96{1{0013 and by the National Science Foundation through grants CCR{9522047, EIA{9870734, and CCR{9877133. Email: jsv@cs.duke. edu.
Deep learning tracker via SVM ranking vector

The deep learning based trackers can always achieve high tracking precision and strong adaptability in different scenarios. However, due to the fact that the number of the parameter is large and the fine-tuning is challenging, the time complexity is high. In order to improve the efficiency, we proposed a tracker based on fast deep learning through constructing a new network with less redundancy. Based on the theory of deep learning, we proposed a deep neural network to describe essential features of images. Furthermore, fast deep learning can be achieved by restricting the size of network. With the help of GPU, the time complexity of the network training is released to a large extent. Under the framework of particle filter, the proposed method combined the deep learning extractor with an SVM scoring professor to distinguish the target from the background. The condensed network structure reduced the complexity of the model. Compared with some other deep learning based tracker, the proposed method can achieve higher efficiency. The frame rate keeps at 22 frames per second on average. Experiments on an open tracking benchmark demonstrate that both the robustness and the timeliness of the proposed tracker are promising when the appearance of the target changes containing translation, rotation and scale or the interference containing illumination, occlusion and cluttered background. Unfortunately, it is not robust enough when the target moves fast or the motion blur and some similar objects exist.
ENHANCING SONIC BROWSING USING AUDIO INFORMATION RETRIEVAL

Collections of sound and music of increasing size and diversity are used both by typical computer users and multimedia designers. Browsing audio collections poses several challenges to the design of effective user interfaces. Recent techniques in audio information retrieval allow the automatic extraction of audio content information. This information can be used to inform and enhance audio browsing tools. In this paper we describe how audio information retrieval can be utilized to create novel user interfaces for browsing of audio collections. More specifically we report on recent work on two system prototypes: the Sonic Browser and Marsyas and our current work on merging the two systems in a common flexible system.
Image interpolation using wavelet-based contour estimation

Successful image interpolation requires proper enhancement of high frequency content of image pixels around edges. We introduce a simple edge model to estimate high resolution edge profiles from lower resolution values. Pixels around edges are viewed as samples taken from one dimensional (1D) continuous edge profiles according to 1D smooth edge contours defining the sampling instants. The image is highpass filtered by wavelets and subpixel edge locations are estimated by minimizing the modeling error in the wavelet domain. Interpolation is carried out by applying the model, wherever applicable, together with a baseline interpolator (here, bilinear) in order to make edges look sharper without introducing artifacts. The results are compared to bilinear interpolation, and significant improvement in terms of SNR, edge sharpness and contour smoothness is observed.
Literature Characterization and Similarity Retrieval Based on Hierarchical Clustering

The growing number of literature in journals database raises a new and challenging search problem: locating desired literature. Traditional keyword search is insufficient: the specific literature users require is possibly not captured. We introduce a new algorithm of hierarchical clustering. With this algorithm, we cluster the keywords into a concept tree, then we turn every literature into an induced tree. We propose a new method for theses retrieval, which based on concept similarity. This method improves in recall and precision.
Determining the effect of change in income on self-rated health using regression models - ordered, linear, fixed or random?

Many observational studies have shown an association between income and health. However, much less research has assessed how changes in income influence health over time. This could help illuminate the issue of whether income is causally related to health, if longitudinal survey data were analysed with methods to account for bias from both measured and unmeasured time-invariant confounders. The aims of this paper are to discuss econometric and biostatistical regression approaches to such analyses using panel data (i.e., fixed and random effects, and ordinal versus continuous specification of the five-level self-rated health (outcome) variable). We used data from 16,365 adults from the first four waves (2002-2005) of the New Zealand fixed household panel Survey of Family, Income and Employment (SoFIE). The outcome was annual self-rated health (SRH; five responses: excellent, very good, good, fair, poor),and the main exposure variable was annual household equivalised income. Potential confounding variables included age, sex, ethnicity, education, employment status, marital status, family structure and area deprivation level. Random and fixed effects linear and proportional odds models were fitted using SAS 8.2. Theoretically, we argue that the best analytical approach is to treat the self-rated heath outcome as ordinal (not continuous), and with a fixed effects methodology. A fixed effects proportional odds logistic regression model in SAS is possible using the NLMIXED procedure to compute a hybrid model. This model produces fixed effects estimates for time-varying covariates. The NLMIXED procedure can also be used to estimate the random effects proportional odds model, allowing the inclusion of timeinvariant covariates. Empirically, the random effects proportional odds models found a very small but significant positive effect of increasing income on the odds of a higher/better level of SRH. This diminished and became non-significant in the (theoretically preferred) fixed effects/hybrid proportional odds models. Qualitatively similar results were found in the linear random and fixed effects models.
Development and quality characteristics of shelf‐stable soy‐agushie: a residual by‐product of soymilk production

Abstract A process was developed for the production of a high‐protein food ingredient, soy‐agushie, from the residual by‐product of soymilk production. The product, with a moisture content of about 6%, was evaluated for its quality characteristics and performance in traditional dishes. The protein content was about 26% with similar amino acids content as that of the whole soybean. Lysine remained high in the dehydrated product (6.57 g/16 g N). While over 60% of the original B vitamins content in the beans was extracted with the milk, high proportions of the minerals were found to be retained in the residual by‐product. The process adequately reduced the trypsin inhibitor levels in the beans from 25 to 1.5 mg/g. High sensory scores were obtained for recipes developed with soy‐agushie in traditional dishes. The scope of utilization of the soy‐agushie could be widened to include several traditional foods and bakery products for maximum nutritional benefits.
Clinical Potential of the Acyclic Nucleoside Phosphonates Cidofovir, Adefovir, and Tenofovir in Treatment of DNA Virus and Retrovirus Infections

SUMMARY The acyclic nucleoside phosphonates HPMPC (cidofovir), PMEA (adefovir), and PMPA (tenofovir) have proved to be effective in vitro (cell culture systems) and in vivo (animal models and clinical studies) against a wide variety of DNA virus and retrovirus infections: cidofovir against herpesvirus (herpes simplex virus types 1 and 2 varicella-zoster virus, cytomegalovirus [CMV], Epstein-Barr virus, and human herpesviruses 6, 7, and 8), polyomavirus, papillomavirus, adenovirus, and poxvirus (variola virus, cowpox virus, vaccinia virus, molluscum contagiosum virus, and orf virus) infections; adefovir against herpesvirus, hepadnavirus (human hepatitis B virus), and retrovirus (human immunodeficiency virus types 1 [HIV-1] and 2 [HIV-2], simian immunodeficiency virus, and feline immunodeficiency virus) infections; and tenofovir against both hepadnavirus and retrovirus infections. Cidofovir (Vistide) has been officially approved for the treatment of CMV retinitis in AIDS patients, tenofovir disoproxil fumarate (Viread) has been approved for the treatment of HIV infections (i.e., AIDS), and adefovir dipivoxil (Hepsera) has been approved for the treatment of chronic hepatitis B. Nephrotoxicity is the dose-limiting side effect for cidofovir (Vistide) when used intravenously (5 mg/kg); no toxic side effects have been described for adefovir dipivoxil and tenofovir disoproxil fumarate, at the approved doses (Hepsera at 10 mg orally daily and Viread at 300 mg orally daily).
Non-stationary continuous dynamic Bayesian networks

Dynamic Bayesian networks have been applied widely to reconstruct the structure of regulatory processes from time series data. The standard approach is based on the assumption of a homogeneous Markov chain, which is not valid in many real-world scenarios. Recent research efforts addressing this shortcoming have considered undirected graphs, directed graphs for discretized data, or over-flexible models that lack any information sharing among time series segments. In the present article, we propose a non-stationary dynamic Bayesian network for continuous data, in which parameters are allowed to vary among segments, and in which a common network structure provides essential information sharing across segments. Our model is based on a Bayesian multiple change-point process, where the number and location of the change-points is sampled from the posterior distribution.
Indexing evolving events from tweet streams

Tweet streams provide a variety of real-time information on dynamic social events. Although event detection has been actively studied, most of the existing approaches do not address the issue of efficient event monitoring in the presence of a large number of events detected from continuous tweet streams. In this paper, we capture the dynamics of events using four event operations: creation, absorption, split and merge.We also propose a novel event indexing structure, named Multi-layer Inverted List (MIL), for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search using MIL based on upper bound pruning. Extensive experiments have been conducted on a large-scale tweet dataset. The results demonstrate the promising performance of our method in terms of both efficiency and effectiveness.
Circulating Cell Free Tumor DNA Detection as a Routine Tool for Lung Cancer Patient Management

Circulating tumoral DNA (ctDNA), commonly named “liquid biopsy”, has emerged as a new promising noninvasive tool to detect biomarker in several cancers including lung cancer. Applications involving molecular analysis of ctDNA in lung cancer have increased and encompass diagnosis, response to treatment, acquired resistance and prognosis prediction, while bypassing the problem of tumor heterogeneity. ctDNA may then help perform dynamic genetic surveillance in the era of precision medicine through indirect tumoral genomic information determination. The aims of this review were to examine the recent technical developments that allowed the detection of genetic alterations of ctDNA in lung cancer. Furthermore, we explored clinical applications in patients with lung cancer including treatment efficiency monitoring, acquired therapy resistance mechanisms and prognosis value.
Quantitative phosphoproteomics reveals genistein as a modulator of cell cycle and DNA damage response pathways in triple-negative breast cancer cells

Around one sixth of breast cancer cases are classified as triple-negative breast cancer (TNBC), named after the absence of the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2); however, patients with TNBC suffer from poor clinical outcome and shortage of targeted therapy. Genistein, an estrogenic soy isoflavone, shows anticancer effects in TNBC cells such as inducing G2/M cell cycle arrest and apoptosis. However, the underlying mechanism of its anticancer effects is poorly understood and its elucidation can help the development of novel therapeutic strategies for TNBC. In this study, by combining isobaric tag-based TMT labeling with titanium dioxide-based phosphopeptide enrichment, we quantitated 5,445 phosphorylation sites on 2,008 phosphoproteins in the TNBC cell line MDA-MB-231, upon genistein treatment. Our analysis revealed 332 genistein-regulated phosphorylation sites on 226 proteins. Our data show that genistein can regulate several biological processes during the cell cycle, including DNA replication, cohesin complex cleavage, and kinetochore formation. Furthermore, genistein can also activate DNA damage response, including activation of ATR and BRCA1 complex. Overall, our study presents evidence at a phosphoproteomic level that genistein is able to inhibit TNBC cell growth by regulating the cell cycle and DNA damage response in a more complex manner. Our findings help elucidate the mechanisms through which genistein exerts its anticancer effects in TNBC cells.
Direct adaptation of hybrid DNN/HMM model for fast speaker adaptation in LVCSR based on speaker code

Recently an effective fast speaker adaptation method using discriminative speaker code (SC) has been proposed for the hybrid DNN-HMM models in speech recognition [1]. This adaptation method depends on a joint learning of a large generic adaptation neural network for all speakers as well as multiple small speaker codes using the standard back-propagation algorithm. In this paper, we propose an alternative direct adaptation in model space, where speaker codes are directly connected to the original DNN models through a set of new connection weights, which can be estimated very efficiently from all or part of training data. As a result, the proposed method is more suitable for large scale speech recognition tasks since it eliminates the time-consuming training process to estimate another adaptation neural networks. In this work, we have evaluated the proposed direct SC-based adaptation method in the large scale 320-hr Switchboard task. Experimental results have shown that the proposed SC-based rapid adaptation method is very effective not only for small recognition tasks but also for very large scale tasks. For example, it has shown that the proposed method leads to up to 8% relative reduction in word error rate in Switchboard by using only a very small number of adaptation utterances per speaker (from 10 to a few dozens). Moreover, the extra training time required for adaptation is also significantly reduced from the method in [1].
Novel Design Strategy for Checkpoint Kinase 2 Inhibitors Using Pharmacophore Modeling, Combinatorial Fusion, and Virtual Screening

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the BesttrainBesttest and FasttrainFasttest prediction results. The potential inhibitors were selected from NCI database by screening according to BesttrainBesttest + FasttrainFasttest prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.
Use of an hourglass model in neuronal coding

We study a system of interacting renewal processes which is a model for neuronal activity. We show that the system possesses an exponentially large number (with respect to the number of neurons in the network) of limiting configurations of the ‘firing neurons’. These we call patterns. Furthermore, under certain conditions of symmetry we find an algorithm to control limiting patterns by means of the connection parameters.