Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting genetic regulatory response using classification

MOTIVATION Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions. RESULTS We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences ('motifs') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment ('parents'). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S.cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. AVAILABILITY The MLJava package is available upon request to the authors. Supplementary: Additional results are available from http://www.cs. columbia.edu/compbio/geneclass
Improved Model of Soil for Environment-Robot Excavator Interaction

Planning of automated digging processes meets difficulties, because is difficult to describe operating environment (soil) - digging robot interaction, here a double-wedge model is proposed as improvement of traditional wedge model generally used. The double-wedge model doesn't introduce sham variables, preserves real geometric dimensions, puts in evidence the influence of control parameters. allows to show the possibility to predict soil cutting effort by both numerical way or measuring and processing the soil cutting effort itself. The prediction can be used to control robot by force and/or planning on line tool trajectories, for example valuing the presence of buried objects.
An Efficient Algorithm for Privacy Preserving Maximal Frequent Itemsets Mining

This paper addressed the insecurity and the inefficiency of privacy preserving association rule mining in vertically partitioned data. We presented a privacy preserving maximal frequent itemsets mining algorithm in vertically partitioned data. The algorithm adopted a more secure vector dot protocol which used an inverse matrix to hide the original input vector, and without any site revealing privacy vector. The mining strategy was based on depth-first search for the maximal frequent itemsets. Performance analysis and experimental analysis showed that the algorithm possessed higher security and efficiency.
Human and Computer Personality Prediction From Digital Footprints

Is it possible to judge someone accurately from his or her online activity? The Internet provides vast opportunities for individuals to present themselves in different ways, from simple self-enhancement to malicious identity fraud. We often rely on our Internet-based judgments of others to make decisions, such as whom to socialize with, date, or employ. Recently, personality-perception researchers have turned to studying social media and digital devices in order to ask whether a person’s digital traces can reveal aspects of his or her identity. Simultaneously, advances in “big data” analytics have demonstrated that computer algorithms can predict individuals’ traits from their digital traces. In this article, we address three questions: What do we currently know about human- and computer-based personality assessments? How accurate are these assessments? Where are these fields heading? We discuss trends in the current findings, provide an overview of methodological approaches, and recommend directions for future research.
State-Based Synchronization Protocol in Sensor Networks

Some applications of the sensor network require precise time synchronization among local clocks of the sensors. Due to the limited resource in the sensor network, it is not trivial to maintain the said synchronization. In this paper, we propose a novel method, referred to as the state-based synchronization protocol (SBSP) to solve this issue. We create a finite state machine in the SBSP to describe the synchronization actions at each sensor node. Specifically, the SBSP makes use of the ADV, REQ and SYNC packets and the finite state machine to achieve synchronization. We will compare the performance of the proposed protocol against the popular timing-sync protocol for sensor networks (TPSN) and lightweight tree-based synchronization (LTS) through simulations. The SBSP reduces the redundant message exchanges in the TPSN. It is shown that the SBSP saves power with less message exchanges while maintaining better synchronization error.
Caenorhabditis elegans Maintains Highly Compartmentalized Cellular Distribution of Metals and Steep Concentration Gradients of Manganese

Bioinorganic chemistry is critical to cellular function. Homeostasis of manganese (Mn), for example, is essential for life. A lack of methods for direct in situ visualization of Mn and other biological metals within intact multicellular eukaryotes limits our understanding of management of these metals. We provide the first quantitative subcellular visualization of endogenous Mn concentrations (spanning two orders of magnitude) associated with individual cells of the nematode, Caenorhabditis elegans.
Advanced container transportation equipment using transfer robot and alignment system

In this study, a horizontal-transfer robot system was designed using a hydraulic system and some electronic sensors, and a synchronized control method was applied to control each robot. An alignment system was also designed to align between the rail and a truck. The proposed system will be useful as an intermodal transportation system that is significantly considered an enhanced technique or future railroad logistics. The system can simplify the complicated job process in railroad-based transportation and, from an economic viewpoint, can reduce relevant logistic costs.
SOS Boosting of Image Denoising Algorithms

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following ”SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restored signal-strengthened outcome. The convergence of this process is studied for the K-SVD image denoising and related algorithms. Furthermore, still in the context of K-SVD image denoising, we introduce an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance. We demonstrate the SOS boosting algorithm for several leading denoising methods (K-SVD, NLM, BM3D, and EPLL), showing tendency to further improve denoising performance.
Making stochastic source coding e cient byrecovering informationBrendan

In this paper, we introduce a new algorithm called \bits-back coding" that makes stochastic source codes eecient. For a given one-to-many source code, we show that this algorithm can actually be more eecient than the algorithm that always picks the shortest codeword. Optimal eeciency is achieved when codewords are chosen according to the Boltzmann distribution based on the codeword lengths. After presenting a binary Bayesian network model that assigns exponentially many codewords to each symbol, we show how a tractable approximation to the Boltzmann distribution can be used for bits-back coding. It turns out that a commonly used technique for determining parameters | maximum likelihood estimation | actually minimizes the optimal bits-back coding cost. A tractable approximation to maximum likelihood estimation | incremental expectation maximization | minimizes the bits-back coding cost as well. We illustrate the performance of bits-back coding rst on a toy problem and then using real data with a binary Bayesian network that produces 2 60 possible codewords for each symbol. For both tasks, the rate for bits-back coding is nearly one half of that obtained by picking the shortest codeword for each symbol.
Cardboard modeling studio: a designerly exploration tool for rich and embodied interaction

Central to this studio is the question of how to design for rich and embodied (meaningful) interaction. We approach this question from a designerly perspective and find inspiration in the theory of ecological perception and in the domain of tangible and embodied interaction. As we aim for a meaningful interaction style that is firmly rooted in human experience and the diverse human skills, we present cardboard modeling as a designerly exploration tool that offers experiential insight into the solution domain of a given interaction design challenge. The studio has two distinct parts: part one aims at familiarizing the participants with the cardboard modeling technique and part two emphasizes the use of the cardboard modeling technique as an instrument to explore meaningful interaction. During the second part of the studio also the quality of the interaction solutions are discussed through presentations. The studio runs from 9.30h to approximately 17.00h.
Recent Explosive Human Population Growth Has Resulted in an Excess of Rare Genetic Variants

Exponential Growth Effects Humans are an extraordinarily successful species, as measured by our large population size—approximately 7 billion—much of which can be put down to recent explosive growth. Leveraging human genomic data, Keinan and Clark (p. 740) examined the effects of population growth on our ability to detect rare genetic variants, those hypothesized to be most likely associated with disease. It appears that rapid recent growth increases the load of rare variants and is likely to play an important role in the individual genetic burden of complex disease risk. Genetic models that incorporate recent human population growth can better identify mutations in large samples. Human populations have experienced recent explosive growth, expanding by at least three orders of magnitude over the past 400 generations. This departure from equilibrium skews patterns of genetic variation and distorts basic principles of population genetics. We characterized the empirical signatures of explosive growth on the site frequency spectrum and found that the discrepancy in rare variant abundance across demographic modeling studies is mostly due to differences in sample size. Rapid recent growth increases the load of rare variants and is likely to play a role in the individual genetic burden of complex disease risk. Hence, the extreme recent human population growth needs to be taken into consideration in studying the genetics of complex diseases and traits.
Inhibition of Carcinogen-Activating Cytochrome P450 Enzymes by Xenobiotic Chemicals in Relation to Antimutagenicity and Anticarcinogenicity

A variety of xenobiotic chemicals, such as polycyclic aromatic hydrocarbons (PAHs), aryl- and heterocyclic amines and tobacco related nitrosamines, are ubiquitous environmental carcinogens and are required to be activated to chemically reactive metabolites by xenobiotic-metabolizing enzymes, including cytochrome P450 (P450 or CYP), in order to initiate cell transformation. Of various human P450 enzymes determined to date, CYP1A1, 1A2, 1B1, 2A13, 2A6, 2E1, and 3A4 are reported to play critical roles in the bioactivation of these carcinogenic chemicals. In vivo studies have shown that disruption of Cyp1b1 and Cyp2a5 genes in mice resulted in suppression of tumor formation caused by 7,12-dimethylbenz[a]anthracene and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, respectively. In addition, specific inhibitors for CYP1 and 2A enzymes are able to suppress tumor formation caused by several carcinogens in experimental animals in vivo , when these inhibitors are applied before or just after the administration of carcinogens. In this review, we describe recent progress, including our own studies done during past decade, on the nature of inhibitors of human CYP1 and CYP2A enzymes that have been shown to activate carcinogenic PAHs and tobacco-related nitrosamines, respectively, in humans. The inhibitors considered here include a variety of carcinogenic and/or non-carcinogenic PAHs and acethylenic PAHs, many flavonoid derivatives, derivatives of naphthalene, phenanthrene, biphenyl, and pyrene and chemopreventive organoselenium compounds, such as benzyl selenocyanate and benzyl selenocyanate; o-XSC, 1,2-, 1,3-, and 1,4-phenylenebis(methylene)selenocyanate.
History-sensitive heuristics for recovery of features in code of evolving program families

A program family might degenerate due to unplanned changes in its implementation, thus hindering the maintenance of family members. This degeneration is often induced by feature code that is changed individually in each member without considering other family members. Hence, as a program family evolves over time, it might no longer be possible to distinguish between common and variable features. One of the imminent activities to address this problem is the history-sensitive recovery of program family's features in the code. This recovery process encompasses the analysis of the evolution history of each family member in order to classify the implementation elements according to their variability nature. In this context, this paper proposes history-sensitive heuristics for the recovery of features in code of degenerate program families. Once the analysis of the family history is carried out, the feature elements are structured as Java project packages; they are intended to separate those elements in terms of their variability degree. The proposed heuristics are supported by a prototype tool called RecFeat. We evaluated the accuracy of the heuristics in the context of 33 versions of 2 industry program families. They presented encouraging results regarding recall measures that ranged from 85% to 100%; whereas the precision measures ranged from 71% to 99%.
TIMIPLAN : An Application to Solve Multimodal Transportation Problems

Intermodal transportation is a very challenging task for AI planning. It is a logistics problem in which a set of goods have to be transported to different places, with the combination of at least two modes of transport in a single transport chain, without a change of container for the goods. The main goal of this paper is to introduce TIMIPLAN, a new application to solve multimodal transportation problems. TIMIPLAN has been developed in the context of a research project involving one of the biggest spanish companies in intermodal transportation, Acciona Transmediterránea Cargo. The main challenge of this project is the size of the planning problems: more than 300 containers, trucks, locations and services have to be dealt with everyday. Thus, internally, TIMIPLAN combines Operational Research (OR) techniques with Artificial Intelligence (AI) planning in order to obtain good quality plans, by exploiting the benefits of both kinds of techniques. These plans can be also graphically visualized.
The variability of inner ear orientation in saurischian dinosaurs: testing the use of semicircular canals as a reference system for comparative anatomy

The vestibular system of the inner ear houses three semicircular canals—oriented on three nearly-orthogonal planes—that respond to angular acceleration stimuli. In recent years, the orientation of the lateral semicircular canal (LSC) has been regularly used to determine skull orientations for comparative purposes in studies of non-avian dinosaurs. Such orientations have been inferred based on fixing the LSC to a common set of coordinates (parallel to the Earth’s horizon), given that the orientation to gravity of this sensory system is assumed constant among taxa. Under this assumption, the LSC is used as a baseline (a reference system) both to estimate how the animals held their heads and to describe craniofacial variation among dinosaurs. However, the available data in living birds (extant saurischian dinosaurs) suggests that the orientation of the LSC in non-avian saurischian dinosaurs could have been very variable and taxon-specific. If such were the case, using the LSC as a comparative reference system would cause inappropriate visual perceptions of craniofacial organization, leading to significant descriptive inconsistencies among taxa. Here, we used Procrustes methods (Geometric Morphometrics), a suite of analytical tools that compares morphology on the basis of shared landmark homology, to show that the variability of LSC relative to skull landmarks is large (ca. 50°) and likely unpredictable, thus making it an inconsistent reference system for comparing and describing the skulls of saurischian (sauropodomorph and theropod) dinosaurs. In light of our results, the lateral semicircular canal is an inconsistent baseline for comparative studies of craniofacial morphology in dinosaurs.
Preliminary study of stress/neutral detection on recordings of children in the natural home environment

Emotion and stress/neutral detection based on an input audio stream has been a topic of interest in the literature with various applications. This paper reports on a preliminary study of stress/neutral detection based on naturalistic home environment recordings of children. One major motivation of the work is to add stress/neutral detection functionality into the LENA™ System [10]. The study started with an acted emotion database, and tested the acoustic feature of Mel-frequency cepstral coefficients and the Gaussian Mixture Model (GMM) for stress/neutral detection on this relatively simple database. The method was then applied to the adult speech segments automatically extracted from home recordings of children with the LENA System, achieving 72% accuracy for adult stress/neutral detection. The application of this new functionality to a large number of naturalistic home environment recordings of children reveals interesting and meaningful statistical differences among the families of typically developing children, language-delayed children, and children with Autism Spectrum Disorders (ASD). The result suggests the potential for stress/neutral detection, along with the LENA System, as an integrated solution for (i) quality assessment of the child language environment, (ii) monitoring language interventions for disordered children, or (iii) general psychological and behavioral research.
Additive Nearest Neighbor Feature Maps

In this paper, we present a concise framework to approximately construct feature maps for nonlinear additive kernels such as the Intersection, Hellinger's, and X2 kernels. The core idea is to construct for each individual feature a set of anchor points and assign to every query the feature map of its nearest neighbor or the weighted combination of those of its k-nearest neighbors in the anchors. The resultant feature maps can be compactly stored by a group of nearest neighbor (binary) indication vectors along with the anchor feature maps. The approximation error of such an anchored feature mapping approach is analyzed. We evaluate the performance of our approach on large-scale nonlinear support vector machines~(SVMs) learning tasks in the context of visual object classification. Experimental results on several benchmark data sets show the superiority of our method over existing feature mapping methods in achieving reasonable trade-off between training time and testing accuracy.
Towards Adaptive Occlusion Culling Using Camera Coherence

Occlusion culling proves to be useful for the interactive visualization of environments that are not densely occluded. Those which are built up by dense geometric sets like aerospace engines composed of thousands of components and millions of polygons. In first place the convenience of using occlusion culling is studied with a simple scheme. Then improvements are analyzed. The key points to obtain frame rate speed-ups are: a convenient occlusion query scheduling provides the performance required; depth sorting is performed only when camera orientation changes more than a given threshold; coherence reduces the number of occlusion queries posted per frame. It is possible to select the percentage of occlusion queries that will be performed in each frame, from non-conservative schemes up to a conservative one. Furthermore, we propose a small addition to the GPU occlusion queries to perform faster renderings
On the MAC/Network/Energy Performance Evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR Routing Protocols

Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15. 4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.