Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostics in Battery Health Management

In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.
Combining similarities with regression based classifiers for Entity Linking at TAC 2010

The UC3M team has sent three runs for each Entity Linking task proposed in the Knowledge Base Population (KBP) track at TAC 2010. The skeleton system presented in 2009 has evolved in 2010 by incorporating some new tools, new algorithms for candidate retrieval and feature extraction, and two new stages that use regression based classifiers for candidate filtering. These improvements have allowed the UC3M team overall values to almost reach the median values of all participants in the Entity Linking tasks.
Early Gene Expression in Wounded Human Keratinocytes Revealed by DNA Microarray Analysis

Wound healing involves several steps: spreading of the cells, migration and proliferation. We have profiled gene expression during the early events of wound healing in normal human keratinocytes with a home-made DNA microarray containing about 1000 relevant human probes. An original wounding machine was used, that allows the wounding of up to 40% of the surface of a confluent monolayer of cultured cells grown on a Petri dish (compared with 5% with a classical ‘scratch’ method). The two aims of the present study were: (a) to validate a limited number of genes by comparing the expression levels obtained with this technique with those found in the literature; (b) to combine the use of the wounding machine with DNA microarray analysis for large-scale detection of the molecular events triggered during the early stages of the wound-healing process. The time-courses of RNA expression observed at 0.5, 1.5, 3, 6 and 15 h after wounding for genes such as c-Fos, c-Jun, Egr1, the plasminogen activator PLAU (uPA) and the signal transducer and transcription activator STAT3, were consistent with previously published data. This suggests that our methodologies are able to perform quantitative measurement of gene expression. Transcripts encoding two zinc finger proteins, ZFP36 and ZNF161, and the tumour necrosis factor α-induced protein TNFAIP3, were also overexpressed after wounding. The role of the p38 mitogen-activated protein kinase (p38MAPK) in wound healing was shown after the inhibition of p38 by SB203580, but our results also suggest the existence of surrogate activating pathways.
Coset Codes for Compound Multiple Access Channels With Common Information

Code construction is considered for arbitrary discrete memoryless compound multiple access channels (MACs) with common information. This class of channels includes MACs with/without common messages or with partially cooperating encoders. A construction method of code ensembles based on coset codes is proposed for these channels. Assuming joint maximum likelihood decoding, the performance of the proposed code ensemble is analyzed by deriving a lower bound on error exponents. A condition assuring that codes achieve the capacity region on average is given. It is seen that an elaborate combination of good linear codes gives capacity achieving codes for compound MACs with common messages or with partially cooperating encoders. The use of restricted ensembles with low-density parity check (LDPC) codes as component codes is also discussed. It is shown that a coset code ensemble based on nonbinary regular LDPC codes approaches the random coding exponent, and thus is capacity-achieving.
Need for speed – finding productive mutations using transcription factor-based biosensors, fluorescenceactivated cell sorting and recombineering

The establishment of a bioeconomy requires technologies enabling the rapid development of microbial production strains and enzymes as highly efficient biocatalysts for the conversion of substrates derived from renewable carbon sources into a multitude of chemicals. For strain development, two major strategies are available. The classical approach uses several rounds of random mutagenesis and screening for the desired producer clones and targets the entire genome irrespective of knowledge on function. The rational approach of metabolic and genetic engineering, on the other hand, is based on current knowledge and was strongly enhanced by the development of ‘omics’ technologies and novel tools for genetic engineering, leading to the establishment of systems biology and synthetic biology (Keasling, 2010). Despite the huge success of metabolic engineering, many of the industrial production strains are still based on the classical approach alone or a mixture of both approaches because these strains outperform the ones based exclusively on rational engineering. This is simply due to the fact that our knowledge of even the best studied microorganisms such as Escherichia coli or Saccharomyces cerevisiae is still far from complete, as obviously shown by the fact that the functions of hundreds of genes in these species are still unknown (because of the lack of a universal strategy to identify these functions). Furthermore, despite the enormous advances in our understanding of enzymes, it is still difficult to predict which mutations in an enzyme will be favourable for a certain purpose. Consequently, random mutagenesis and screening still offer great chances to identify novel mutations that enhance production. Because of the possibility of cheap re-sequencing of clones obtained from screening, the productive mutations can in principle be identified, thus providing the basis for a molecular understanding of the effect of the mutation.
Pattern generation for humanoid robot with natural ZMP trajectory

Walking pattern generation is one of the key problems in control of humanoid robots. Zero Moment Point (ZMP)-based approaches such as Linear Inverted Pendulum Mode (LIPM) and Gravity Compensated Inverted Pendulum Mode (GCIPM) were proposed to generate the walking pattern. In proposed methods, the ZMP is considered fixed in the middle of supporting foot sole during stepping. But, in the natural human walking, ZMP is not fixed and moves forward under the supporting foot during stepping. This paper proposes a pattern generation method using GCIPM with considering moving ZMP under the robot supporting foot. It is supposed that the ZMP follows a first-order function and moves forward from the heel in the direction of toe. Simulation results show that the reference trajectories which are generated considering the moving ZMP cause the smooth movement for COM.
A diagnostic marker for speech delay associated with otitis media with effusion: the intelligibility‐speech gap

The goal of this study was to determine if notably reduced intelligibility is a potential diagnostic marker for children with speech delay and histories of early recurrent otitis media with effusion (SD‐OME). Intelligibility was assessed in one 5–10 minute conversational speech sample from each of 281 speakers. The OME histories of 148 of these children with normal speech acquisition were described in two prior reports. OME histories of 85 additional children with speech delay were obtained from case history reports. For both groups, the children with positive OME (OME+) histories had significantly lower intelligibility scores but significantly higher speech production scores than children with negative OME (OME−) histories. Findings for a diagnostic marker to discriminate speech delayed children with OME+ versus OME− histories were promising, considering that the data were obtained retrospectively and did not include audiological information characterizing children's concurrent fluctuant hearing loss. The formula for the diagnostic marker, termed the Intelligibility‐Speech Gap, was identified by a machine learning routine. Diagnostic accuracy findings for the marker were as follows: positive predictive value=74%, negative predictive value=86%, sensitivity=79%, specificity=83%, positive likelihood ratio=4.6 and negative likelihood ratio=0.25. Discussion considers speech processing perspectives on the source of the intelligibility‐speech gap in children with suspected SD‐OME, and methodological perspectives on its development as a diagnostic marker of one etiological subtype of speech delay.
Multivariate analysis versus multiple univariate analyses.

The argument for preceding multiple analysis of variance (ANOVAS) with a multivariate analysis of variance (MANOVA) to control for Type I error is challenged. Several situations are discussed in which multiple ANOVAS might be conducted without the necessity of a preliminary MANOVA. Three reasons for considering a multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine variable relative worth. The analyses discussed in this article are those appropriate in research situations in which analysis of variance techniques are useful. These analyses are used to study the effects of treatment variables on outcome/response variables (in ex post facto as well as experimental studies). We speak of an univariate analysis of variance (ANOVA) when a single outcome variable is involved; when multiple outcome variables are involved, it is a multivariate analysis of variance (MANOVA). (Covariance analyses may also be included.) With multiple outcome variables, the typical analysis approach used in the group-comparison context, at least in the behavioral sciences, is to either (a) conduct multiple ANOVAs or (b) conduct a MANOVA followed by multiple ANOVAS. That these are two popular choices may be concluded from a survey of some prominent behavioral science journals. The 1986 issues
Pushing the envelope of modern methods for bundle adjustment

In this paper, we present results and experiments with several methods for bundle adjustment, producing the fastest bundle adjuster ever published. The fastest methods work with the well known reduced camera system and handle the block-sparse pattern arising in the reduced camera system in a natural way. Adapting to the naturally arising block-sparsity allows the use of BLAS3, efficient memory handling, fast variable ordering, and customized sparse solving all at the same time. We present two methods, one using exact minimum degree ordering and block-based LDL solving, and one using block-based preconditioned conjugate gradient, both on the reduced camera system. We show experimentally that the adaptation to the natural block sparsity allows both these methods to perform better than previous ones. Further speed improvements are achieved by the novel use of embedded point iterations. The embedded point iterations take place inside each camera update step, yielding a higher cost decrease from each camera update step. This is especially true for points projecting far out on the flatter region of the robustifier.
Association between human rhinovirus C and severity of acute asthma in children

A new and potentially more pathogenic group of human rhinovirus (HRV), group C (HRVC), has recently been discovered. We hypothesised that HRVC would be present in children with acute asthma and cause more severe attacks than other viruses or HRV groups. Children with acute asthma (n = 128; age 2–16 yrs) were recruited on presentation to an emergency department. Asthma exacerbation severity was assessed, and respiratory viruses and HRV strains were identified in a nasal aspirate. The majority of the children studied had moderate-to-severe asthma (85.2%) and 98.9% were admitted to hospital. HRV was detected in 87.5% and other respiratory viruses in 14.8% of children, most of whom also had HRV. HRVC was present in the majority of children with acute asthma (59.4%) and associated with more severe asthma. Children with HRVC (n = 76) had higher asthma severity scores than children whose HRV infection was HRVA or HRVB only (n = 34; p = 0.018), and all other children (n = 50; p = 0.016). Of the 19 children with a non-HRV virus, 13 had HRV co-infections, seven of these being HRVC. HRVC accounts for the majority of asthma attacks in children presenting to hospital and causes more severe attacks than previously known HRV groups and other viruses.
Inapproximability of Feedback Vertex Set for Bounded Length Cycles

The Feedback Vertex Set problem (FVS), where the goal is to find a small subset of ver- tices that intersects every cycle in an input directed graph, is among the fundamental prob- lems whose approximability is not well-understood. One can efficiently find an O(log n) factor approximation, but the best NP-hardness result is only a factor of ≈ 1.36 (via a sim- ple reduction from Vertex Cover). A constant-factor approximation is ruled out under the Unique Games Conjecture (UGC), and we give a simpler proof of this in the paper. Our main result concerns a natural variant of FVS, where the goal is to find a small subset of vertices that intersects every cycle of bounded length. For this variant, we prove strong NP-hardness of approximation results: For any integer constant k > 3 and > 0, it is hard to find a (k − 1 − )-approximate solution to the problem of intersecting every cycle of length at most k. The hardness result almost matches the trivial factor k approximation algorithm for the problem. In fact, the hardness holds also for the problem of hitting every cycle of length at most a parameter k′ > k where k′ can be taken to be Ω( logn log logn ). Taking k′ = ω(log n log logn) would be enough to prove a hardness for FVS (for arbitrary length cycles). Our work thus identifies the problem of hitting cycles of length ≈ log n as the key towards resolving the approximability of FVS. Our result is based on reductions from k-uniform Hypergraph Vertex Cover with ran- dom matching and labeling techniques. As byproducts of our techniques, we also prove a factor (k−1− ) hardness of approximation result for k-Clique Transversal, where one must hit every k-clique in the graph with fewest possible vertices, and a factor Ω(k) hardness re- sult for finding a minimum-sized set of edges to hit all k-cycles. We also obtain almost tight Ω(k) factor hardness results for the dual problem of packing vertex-disjoint k-cycles and k-cliques in a graph, albeit relying on the UGC for k-Cycle Packing (but we do get a weaker factor Ω( √ k) NP-hardness result). ∗Supported in part by NSF grant CCF-1115525. guruswami@cmu.edu †Supported by a Samsung Fellowship, US-Israel BSF grant 2008293, and NSF CCF-1115525. euiwoonl@cs.cmu.edu ISSN 1433-8092 Electronic Colloquium on Computational Complexity, Revision 1 of Report No. 6 (2014)
Leakage Cancellation in Coded Automotive Radar

This paper concerns the study of automotive anticollision radar. It especially describes the leakage problem observed as undesired peaks in the correlation function. To resolve it, we propose a solution called Adaptive Leakage Cancellation (ALC) based on using of adaptive lter in order to identify the unknown echo path caused by the leakage. The output of this lter estimates the leakage signal that is subtracted from the received signal to let only the echos provided from targets. For comparison porposes we have considered three algorithms LMS (Least Mean Square), RLS (Recursive Least Square) and APA (Ane Projection Algorithm) to update the adaptive lter coecient vector. The LMS algorithm is considered as an interesting algorithm due to its simplicity and the good leakage cancellation.
Research on automatic semantic classification of human-interaction instructions

Instructions extraction extracts structured information from unstructured natural language instruction text, is an application of information extraction in the field of human-computer interaction. For a natural language instruction text, if we want to extract structural information which can able to describe the text semantic completely, it is critical to position these words or phrases and mark one description which belongs to their own semantic description. This paper first try to a solution which is semantic classification based on dictionary. Because of some shortcomings of the dictionary itself, the semantic classification results are poor. Through the analysis of dictionary-based semantic classification results, this paper proposes a semantic classification method which combining CRF, self-training and Dictionary. Use this method to conduct experiments in the field of vehicle. The experiment results show that our method can be effective in semantic classification for the natural language instruction text; the overall correct rate is 92%. Semantic classification is prepared for the following work of structured information extraction.
Segmentation of the Left Ventricle from Ultrasound Using Random Forest with Active Shape Model

This paper presents a model-based learning segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound (US) images by combining a random forest classifier with an active shape model (ASM). Our method applies an ASM for initial detection of the LV landmarks. Each landmark is subsequently directed radially inward or outward as a result of the random forest classifier identifying the landmark as outside or inside the LV boundary, respectively. This is done while preserving the shape characteristics obtained from the ASM. Our objective is to evaluate the combined application of a random forest classifier with an ASM for detecting the LV boundary with US images. Accuracy of this method is evaluated by comparing both our method and ASM to LV contours traced by an expert. A dataset of 85 randomly selected patient studies was chosen. The method exhibits improved accuracy compared to the ASM, producing a global overlap coefficient of 90.09% compared to 83.8% obtained with an active shape model.
Cholesky Factorization With Symmetric Permutation Applied to Detecting and Precoding Spatially Multiplexed Data Streams

We study computationally efficient spatial multiplexing transmission techniques aiming at high spectral efficiency. Two nonlinear transmission schemes based on the minimum mean-squared error criterion are considered in this paper: a detection scheme also known as V-BLAST and a precoding scheme called Tomlinson-Harashima precoding. The nonlinear techniques are known to be more powerful than simple linear filters, however, a large complexity overhead results. Initial proposals for the nonlinear schemes require the complexity proportional to N4 if the number of data streams is denoted by N. We propose to apply Cholesky factorization with symmetric permutation for finding a very simple and efficient algorithm that reduces the complexity by a factor of N. We conclude that the large performance advantage of the nonlinear detection and precoding schemes against their simple linear alternatives can be obtained without complexity overhead
Phytoplankton community structure in the Lena Delta (Siberia, Russia) in relation to hydrography

The Lena Delta in Northern Siberia is one of the largest river deltas in the world. During peak discharge, after the ice melt in spring, it delivers between 60–8000 m 3 s −1 of water and sediment into the Arctic Ocean. The Lena Delta and the Laptev Sea coast also constitute a continuous permafrost region. Ongoing climate change, which is particularly pronounced in the Arctic, is leading to increased rates of permafrost thaw. This has already profoundly altered the discharge rates of the Lena River. But the chemistry of the river waters which are discharged into the coastal Laptev Sea have also been hypothesized to undergo considerable compositional changes, e.g. by increasing concentrations of inorganic nutrients such as dissolved organic carbon (DOC) and methane. These physical and chemical changes will also affect the composition of the phytoplankton communities. However, before potential consequences of climate change for coastal arctic phytoplankton communities can be judged, the inherent status of the diversity and food web interactions within the delta have to be established. In 2010, as part of the AWI Lena Delta programme, the phyto- and microzooplankton community in three river channels of the delta (Trofimov, Bykov and Olenek) as well as four coastal transects were investigated to capture the typical river phytoplankton communities and the transitional zone of brackish/marine conditions. Most CTD profiles from 23 coastal stations showed very strong stratification. The only exception to this was a small, shallow and mixed area running from the outflow of Bykov channel in a northerly direction parallel to the shore. Of the five stations in this area, three had a salinity of close to zero. Two further stations had salinities of around 2 and 5 throughout the water column. In the remaining transects, on the other hand, salinities varied between 5 and 30 with depth. Phytoplankton counts from the outflow from the Lena were dominated by diatoms ( Aulacoseira species) cyanobacteria ( Aphanizomenon , Pseudanabaena ) and chlorophytes. In contrast, in the stratified stations the plankton was mostly dominated by dinoflagellates, ciliates and nanoflagellates, with only an insignificant diatom component from the genera Chaetoceros and Thalassiosira (brackish as opposed to freshwater species). Ciliate abundance was significantly coupled with the abundance of total flagellates. A pronounced partitioning in the phytoplankton community was also discernible with depth, with a different community composition and abundance above and below the thermocline in the stratified sites. This work is a first analysis of the phytoplankton community structure in the region where Lena River discharge enters the Laptev Sea.
Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation

We study a spectral initialization method that serves a key role in recent work on estimating signals in non-convex settings. Previous analysis of this method focuses on the phase retrieval problem and provides only performance bounds. In this paper, we consider arbitrary generalized linear sensing models and present a precise asymptotic characterization of the performance of the method in the high-dimensional limit. Our analysis also reveals a phase transition phenomenon that depends on the ratio between the number of samples and the signal dimension. When the ratio is below a minimum threshold, the estimates given by the spectral method are no better than random guesses drawn from a uniform distribution on the hypersphere, thus carrying no information; above a maximum threshold, the estimates become increasingly aligned with the target signal. The computational complexity of the method, as measured by the spectral gap, is also markedly different in the two phases. Worked examples and numerical results are provided to illustrate and verify the analytical predictions. In particular, simulations show that our asymptotic formulas provide accurate predictions for the actual performance of the spectral method even at moderate signal dimensions.