Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty analysis of Learning-from-Examples algorithms

Learning-from-Examples (LfE) algorithms are becoming popular building blocks for some type of measurement systems, like smart sensors. They enhance and extend the measurement capabilities of sensors allowing the use of sophisticated algorithms for sensor compensation, or for the automatic classification of physical phenomena. Machine learning systems differ quite a bit from components more commonly found in measurement systems, both in the way they introduce uncertainty and in the way that uncertainty is usually estimated. In this work we provide an analysis of uncertainty of such kind of systems, focusing on the peculiarities resulting from the presence of the LfE module in the measurement chain. The analysis is at a theoretical level, with support of numerical simulations.
Turán problems for integer‐weighted graphs

A multigraph is (k,r)‐dense if every k‐set spans at most r edges. What is the maximum number of edges exℕ(n,k,r) in a (k,r)‐dense multigraph on n vertices? We determine the maximum possible weight of such graphs for almost all k and r (e.g., for all r>k3) by determining a constant m=m(k,r) and showing that exℕ(n,k,r)=m$\left ( n\atop 2\right )$+O(n), thus giving a generalization of Turán's theorem. We find exact answers in many cases, even when negative integer weights are also allowed. In fact, our main result is to determine the maximum weight of (k,r)‐dense n‐vertex multigraphs with arbitrary integer weights with an O(n) error term. © 2002 Wiley Periodicals, Inc. J Graph Theory 40: 195–225, 2002
Oral bioavailability of glyphosate: Studies using two intestinal cell lines

Gyphosate is a commonly used nonselective herbicide that inhibits plant growth through interference with the production of essential aromatic amino acids. In vivo studies in mammals with radiolabeled glyphosate have shown that 34% of radioactivity was associated with intestinal tissue 2 h after oral administration. The aim of our research was to investigate the transport, binding, and toxicity of glyphosate to the cultured human intestinal epithelial cell line, Caco‐2, and the rat small intestinal crypt‐derived cell line, ileum epithelial cells‐18 (IEC‐18). An in vitro analysis of the transport kinetics of [14C]‐glyphosate showed that 4 h after exposure, approximately 8% of radiolabeled glyphosate moved through the Caco‐2 monolayer in a dose‐dependent manner. Binding of glyphosate to cells was saturable and approximately 4 × 1011 binding sites/cell were estimated from bound [14C]. Exposure of Caco‐2 cells to ≥10 mg/ml glyphosate reduced transmembrane electrical resistance (TEER) by 82 to 96% and increased permeability to [3H]‐mannitol, indicating that paracellular permeability increased in glyphosate‐treated cells. At 10‐mg/ml glyphosate, both IEC‐18 and Caco‐2 cells showed disruption in the actin cytoskeleton. In Caco‐2 cells, significant lactate dehydrogenase leakage was observed when cells were exposed to 15 mg/ml of glyphosate. These data indicate that at doses >10 mg/ml, glyphosate significantly disrupts the barrier properties of cultured intestinal cells.
Ecoinformatics Reveals Effects of Crop Rotational Histories on Cotton Yield

Crop rotation has been practiced for centuries in an effort to improve agricultural yield. However, the directions, magnitudes, and mechanisms of the yield effects of various crop rotations remain poorly understood in many systems. In order to better understand how crop rotation influences cotton yield, we used hierarchical Bayesian models to analyze a large ecoinformatics database consisting of records of commercial cotton crops grown in California's San Joaquin Valley. We identified several crops that, when grown in a field the year before a cotton crop, were associated with increased or decreased cotton yield. Furthermore, there was a negative association between the effect of the prior year's crop on June densities of the pest Lygus hesperus and the effect of the prior year's crop on cotton yield. This suggested that some crops may enhance L. hesperus densities in the surrounding agricultural landscape, because residual L. hesperus populations from the previous year cannot continuously inhabit a focal field and attack a subsequent cotton crop. In addition, we found that cotton yield declined approximately 2.4% for each additional year in which cotton was grown consecutively in a field prior to the focal cotton crop. Because L. hesperus is quite mobile, the effects of crop rotation on L. hesperus would likely not be revealed by small plot experimentation. These results provide an example of how ecoinformatics datasets, which capture the true spatial scale of commercial agriculture, can be used to enhance agricultural productivity.
Policies to reduce influenza in the workplace: impact assessments using an agent-based model.

OBJECTIVES We examined the impact of access to paid sick days (PSDs) and stay-at-home behavior on the influenza attack rate in workplaces. METHODS We used an agent-based model of Allegheny County, Pennsylvania, with PSD data from the US Bureau of Labor Statistics, standard influenza epidemic parameters, and the probability of staying home when ill. We compared the influenza attack rate among employees resulting from workplace transmission, focusing on the effects of presenteeism (going to work when ill). RESULTS In a simulated influenza epidemic (R0 = 1.4), the attack rate among employees owing to workplace transmission was 11.54%. A large proportion (72.00%) of this attack rate resulted from exposure to employees engaging in presenteeism. Universal PSDs reduced workplace infections by 5.86%. Providing 1 or 2 "flu days"-allowing employees with influenza to stay home-reduced workplace infections by 25.33% and 39.22%, respectively. CONCLUSIONS PSDs reduce influenza transmission owing to presenteeism and, hence, the burden of influenza illness in workplaces.
Compression and Explanation Using Hierarchical Grammars

This paper describes an algorithm, called SEQUITUR, that identi"es hierarchical structure in sequences of discrete symbols and uses that information for compression. On many practical sequences it performs well at both compression and structural inference, producing comprehensible descriptions of sequence structure in the form of grammar rules. The algorithm can be stated concisely in the form of two constraints on a context-free grammar. Inference is performed incrementally, the structure faithfully representing the input at all times. It can be implemented ef"ciently and operates in a time that is approximately linear in sequence length. Despite its simplicity and ef"ciency, SEQUITUR succeeds in inferring a range of interesting hierarchical structures from naturally occurring sequences.
The papillomavirus E2 proteins: structure, function, and biology.

Nearly twenty years after the first high-resolution crystal structures of specific protein-DNA complexes were determined, the stereo-chemical basis for protein-DNA recognition remains an active area of investigation. One outstanding question is, how are proteins able to detect noncontacted sequences in their binding sites? The papillomavirus E2 proteins represent a particularly suitable group of proteins in which to examine the mechanisms of "indirect readout. " Coordinated structural and thermodynamic studies of the E2-DNA interaction conducted over the past five years are summarized in this review. The data support a model in which the electrostatic properties of the individual E2 proteins correlate with their affinities for intrinsically flexible or rigidly prebent DNA targets.
Linear Learning with Sparse Data

Linear predictors are especially useful when the data is high-dimensional and sparse. One of the standard techniques used to train a linear predictor is the Averaged Stochastic Gradient Descent (ASGD) algorithm. We present an efficient implementation of ASGD that avoids dense vector operations. We also describe a translation invariant extension called Centered Averaged Stochastic Gradient Descent (CASGD).
Open Access Research Article the Biological Context of Hiv-1 Host Interactions Reveals Subtle Insights into a System Hijack

Background: In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions. Results: After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system. Conclusions: Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties. Background Human immunodeficiency virus type 1 (HIV-1) and its associated illnesses have major health and socio-economic impacts, particularly in developing countries [1]. Concomitant with the progression of the HIV pandemic there has, thus, been a major international research effort, leading to a detailed understanding of HIV biology. One of the most important aspects of this knowledge is the set of known contacts between viral proteins and the host system[2-4], fundamental to HIV's life cycle. HIV, like all viruses, subjugates and exploits host cells in order to propagate. To achieve this, the HIV virion must first bind to a host cell, primarily CD4+ T cells, macrophages and dendritic cells, and then 'hijack' their cellular
The Neural Crest: Understanding Stem Cell Function in Development and Disease

Complex organs like the nervous system are composed of different cell types which are all derived from multipotent stem cells. In vertebrates, a transient population of stem cells, the neural crest, generates the entire peripheral nervous system as well as non-neural progeny. The developmental processes of cellular differentiation and proliferation require precise coordination and control. Errors in the programs that regulate stem cell function can lead to defects that manifest in developmental disorders, in some cases they might even induce cancer. It is therefore of fundamental interest to understand the mechanisms of stem cell maintenance and differentiation. Using the neural crest as a model system helps us not only to understand the role of stem cells in development but might also lead to new aspects for the cure of stem cell-related diseases.
Xplus

The need to improve a suboptimal execution plan picked by the query optimizer for a repeatedly run SQL query arises routinely. Complex expressions, skewed or correlated data, and changing conditions can cause the optimizer to make mistakes. For example, the optimizer may pick a poor join order, overlook an important index, use a nested-loop join when a hash join would have done better, or cause an expensive, but avoidable, sort to happen. SQL tuning is also needed while tuning multi-tier services to meet service-level objectives. The difficulty of SQL tuning can be lessened considerably if users and higher-level tuning tools can tell the optimizer: "I am not satisfied with the performance of the plan p being used for the query Q that runs repeatedly. Can you generate a (δ%) better plan? " This paper designs, implements, and evaluates Xplus which, to our knowledge, is the first query optimizer to provide this feature. Xplus goes beyond the traditional plan-first-execute-next approach: Xplus runs some (sub)plans proactively, collects monitoring data from the runs, and iterates. A nontrivial challenge is in choosing a small set of plans to run. Xplus guides this process efficiently using an extensible architecture comprising SQL-tuning experts with different goals, and a policy to arbitrate among the experts. We show the effectiveness of Xplus on real-life tuning scenarios created using TPC-H queries on a PostgreSQL database.
Fitness Based Identification of a Robot Structure

Embodiment theory suggests that recurrent processes of sensorimotor activity give rise to cognitive structures. In the case of robots, internal sensorimotor activity generated with physics simulators can be exploited to expand the historical domain of action, however pre-engineered simulations are limited by the reality gap problem. Alternatively simulation might be inferred and self-constructed out of data collected during robot functioning. Fundamental to this line of research is dening a distance function to asses the potential of candidate robot simulations to reproduce real world activity. In this paper we study the characteristics of a distance function based on behavioral tness measurements. We show how this function can be applied for the generation of behaviors using an algorithm that co-evolves a robot and its simulation. The experiments show how the monotonicity of the function increases with the number of behaviors being tested in reality and with the genotypic diversity of corresponding robot controllers. Moreover it allows for the accurate identication of behavior-relevant parameters contained in the simulation. The metric shows an advantage, when compared to other metrics, for assessing the quality of simulators over long time scales of robot behavioral evaluation.
Analysis of absorption mechanism in a planar radome absorber

The analysis of absorption mechanism in a planar radome absorber is presented. The absorber is composed of an array of cylindrically shaped absorber, made from materials having both magnetic and electric losses. By analyzing the field distribution and power loss density within the structure, we show that the dielectric resonance mode is responsible for the absorption. It is further investigated that by mapping the possible modes versus the radius-to-height ratio of the cylindrical absorber, a broadband absorber over 40% bandwidth with lower reflection performance (less than −30 dB) can be designed. This study demonstrates that the multimode dielectric resonator method using high losses materials can be used as a potential candidate for the design of microwave absorbers having thin profile, broadband and lower reflection requirements.
Sales Buy-In of Marketing Strategies: Exploration of its Nuances, Antecedents, and Contextual Conditions

This study uses a qualitative research design with the grounded theory method to explore the multifaceted nature of sales buy-in—that is, the sales function’s belief that marketers’ proposed strategy is appropriate and has merit. Based on 49 in-depth interviews with sales and marketing professionals, the findings indicate that obtaining sales buy-in consists of four key components: (1) objectivity and rational persuasion, (2) sensitivity and responsiveness to reality, (3) involvement in strategy creation, and (4) positioning for success. The findings also show that three organizational-level factors play an important role in determining sales buy-in: (1) eliminating interfunctional walls, (2) bridging the cultural divide between sales and marketing, and (3) developing interfunctional relationships. Last, sales buy-in depends on two contextual conditions—hierarchy and strategy absorption time.
A fault tolerant peer-to-peer distributed EM algorithm

In this paper, a distributed Expectation Maximization (EM) algorithm is proposed for estimating parameters of a Gaussian mixture model in a peer-to-peer network. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A peer-to-peer algorithm is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the Gaussian mixture model using the estimated global sufficient statistics. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm.
Nonlinearity compensation in digital radio systems

Digital radio links with bandlimited pulses exhibit a severe performance degradation when the transmitter high power amplifier operates near saturation. To cope with the increase of nonlinear intersymbol interference due to the amplifier nonlinearities, a discrete-time Volterra system can be used to process the transmitted data. We present an efficient technique for implementing adaptive data predistorters with memory based on a discrete-time Volterra system composed of digital linear filters and memoryless nonlinear devices working at the symbol rate. Third- and fifth-order structures are proposed and a system performance evaluation is presented for several realistic situations. >
Study on the Relation of Local Railroad Freight Transportation and Region Economic Growth

In order to impel local railroad freight transportation to adapt the development of market economy, and help local railroad freight transportation to distinguish circumstances and correct market localization, with the aid of the measure economic model, the paper analyzes relations between local railroad freight transportation and region economy, from the macro- environment lay, adopting HeNan Province's historical data, the article carries Granger causality method to examination the relation between local railroad freight transportation and region economy, and through variance decomposition method to test this kind of causal relation intensity. The main conclusions of this study are that there is evidence of bi-directional causality between local railroad freight transportation and region economy in HeNan Province; in the short-term, the region economy to freight transportation of local railroad's influence is bigger than freight transportation of local railroad to region economy's influence; Looked for a long time, freight transportation of local railroad to region economy's influence is bigger than region economy to freight transportation of local railroad's influence.
Antiproliferative, Ultrastructural, and Physiological Effects of Amiodarone on Promastigote and Amastigote Forms of Leishmania amazonensis

Amiodarone (AMIO), the most frequently antiarrhythmic drug used for the symptomatic treatment of chronic Chagas' disease patients with cardiac compromise, has recently been shown to have also specific activity against fungi, Trypanosoma cruzi and Leishmania. In this work, we characterized the effects of AMIO on proliferation, mitochondrial physiology, and ultrastructure of Leishmania amazonensis promastigotes and intracellular amastigotes. The IC50 values were 4.21 and 0.46 μM against promastigotes and intracellular amastigotes, respectively, indicating high selectivity for the clinically relevant stage. We also found that treatment with AMIO leads to a collapse of the mitochondrial membrane potential (ΔΨm) and to an increase in the production of reactive oxygen species, in a dose-dependent manner. Fluorescence microscopy of cells labeled with JC-1, a marker for mitochondrial energization, and transmission electron microscopy confirmed severe alterations of the mitochondrion, including intense swelling and modification of its membranes. Other ultrastructural alterations included (1) presence of numerous lipid-storage bodies, (2) presence of large autophagosomes containing part of the cytoplasm and membrane profiles, sometimes in close association with the mitochondrion and endoplasmic reticulum, and (3) alterations in the chromatin condensation and plasma membrane integrity. Taken together, our results indicate that AMIO is a potent inhibitor of L. amazonensis growth, acting through irreversible alterations in the mitochondrial structure and function, which lead to cell death by necrosis, apoptosis and/or autophagy.
Semi-supervised training in low-resource ASR and KWS

In particular for “low resource” Keyword Search (KWS) and Speech-to-Text (STT) tasks, more untranscribed test data may be available than training data. Several approaches have been proposed to make this data useful during system development, even when initial systems have Word Error Rates (WER) above 70%. In this paper, we present a set of experiments on low-resource languages in telephony speech quality in Assamese, Bengali, Lao, Haitian, Zulu, and Tamil, demonstrating the impact that such techniques can have, in particular learning robust bottle-neck features on the test data. In the case of Tamil, when significantly more test data than training data is available, we integrated semi-supervised training and speaker adaptation on the test data, and achieved significant additional improvements in STT and KWS.
Tectono-sedimentary evolution around the Jurassic-Cretaceous transition in Galve (Aguilar del Alfambra Formation, Teruel, Iberian Chain)

: Extensive mapping and facies analysis in a 2 km continuous outcrop located west of Galve (Teruel) resulted in the identification of a set of synsedimentary normal faults. Fault activity after the sedimentation of the shallow marine to coastal deposits of the Tithonian Villar del Arzobispo Formation resulted in block tilting and the formation of a low-angle erosive unconformity with an associated gap probably ranging from the late Tithonian to the early Berriasian. The lower beds of the overlying mid-Berriasian Aguilar del Alfambra Formation locally onlap this erosive surface. Synsedimentary fault activity explains the sharp thickness changes observed within this formation. The Aguilar del Alfambra Formation was deposited in mixed terrigenous-carbonate tidal flat to restricted lagoon environments. The boundary between this formation and the overlying alluvial to fluvial terrigenous deposits of the Galve Formation is another erosive unconformity. This mid-late Berriasian unconformity is associated to successive conglomeratic beds. Synsedimentary fault activity at the onset of the sedimentation of the Galve Formation is indicated by the local incision of fluvial channels on the hanging walls of the faults. The reported data provides further understanding of the unconformities previously described at the lower and upper boundaries of the Aguilar del Alfambra Formation.
The Three-Headed Monster : An Evolutionary Program

In this paper we present our experiments with developing an intelligent agent using genetic programming, with the goal of exploring an environment with limited information in search for resources. The challenge consisted of evolving an agent browsing a world knowing only the altitude in a local neighborhood, looking for a balance food diet of game and grain. We used genetic programming to evolve our agent, using both a simple exploratory approach and more sophisticated search techniques. Our results show that in this particular context, the simple approaches seem to work the best.