Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Temperature Characteristics of Temperature Analogous Material in Geomechanical Model Test

In the geomechanical model test where the temperature analogous material is applied,mechanical parameters and thermal physical properties are changed by changing temperature.And it's influence degree will determine the reliability of the test result.In this paper model test and numerical analysis are used to study the temperature characteristics of the material.The analysis shows that the influence degree of the test displacement made by the change of thermal physical property is 6%,and the influence degree of stress is-6%～9%. The adaptation of temperature range is less than 75 ℃.
Axenic mass cultivation of the free-living soil amoeba , Acanthamoeba castellanii in a laboratory fermentor

Axenic mass cultivation ofAcanthamoeba castellanii in laboratory fermentors (141) yielded after 20 days approximately 3 g cells (wet weight). After a short lag phase amoebal cell numbers increased exponentially to a maximum of 3.5 x 105 cells per ml until cell death occurred after 20 days. Optical density and protein concentrations revealed identical patterns. During amoebal growth only 12-19% of the initially added glucose (100 mM) as sole carbon source was used. Large amounts of ammonia (1 g in 10.5 1 culture volume) were excreted into the medium which subsequently raised the pH from 6.6 to 7.7, and from 6.6 to 6.8 in 2 and 20 mM buffered media, respectively. Growth inhibition and cell death could not be explained by a depletion of glucose or oxygen limitations during growth. The production of ammonia had a growth inhibitory effect, however, the sudden termination of the exponential growth phase and cell death could not be explained by the toxic influence of ammonia only.
Towards Deterministic Decomposable Circuits for Safe Queries

There exist two approaches for exact probabilistic inference of UCQs on tuple-independent databases. In the extensional approach, query evaluation is performed within a DBMS by exploiting the structure of the query. In the intensional approach, one first builds a representation of the lineage of the query on the database, then computes the probability of the lineage. In this paper we propose a new technique to construct lineage representations as deterministic decomposable circuits in PTIME. The technique can apply to a class of UCQs that has been conjectured to separate the complexity of the two approaches. We test our technique experimentally, and show that it succeeds on all the queries of this class up to a certain size parameter, i.e., over $20$ million queries.
Booking limits and bid price based revenue management policies in rail freight transportation

In this paper, the possibility and potential benefits of implementing discriminatory policies in rail freight transportation are analyzed, with the aim of revenue maximization. A regular, cyclic, single train service with fixed composition and capacity is studied. The problem is decomposed into discrete time periods. Transportation requests arise randomly over time, and the decision of either accept or reject a certain request has to be made. The problem is formulated via dynamic programming, and the deterministic approximations of the problem are used in order to formulate booking limits and bid price policies. Results obtained are compared with those of standard first come – first served policy, which is implemented by the Serbian railways. Although not acceptable in all contexts, the proposed aggressive policies demonstrated promising benefits.
Terahertz spectroscopy of quantum 2D electron systems

Terahertz time-domain spectroscopy permits the coherent motion of charges to be examined in a diverse range of two-dimensional semiconductor heterostructures. Studies of the THz conductivity and magnetoconductivity of two-dimensional quantum systems are reviewed, including cyclotron resonance spectroscopy and the transverse conductivity in the Hall and quantum Hall regimes. Experiments are described that demonstrate quantum phenomena at THz frequencies, principally coherent control and enhanced light–matter coupling in electromagnetic cavities.
Match-up & conquer: a two-step technique for recognizing unconstrained bimanual and multi-finger touch input

We present a simple, two-step technique for recognizing multi-touch gesture input that is invariant to how users articulate gestures, i.e., by using one or two hands, one or multiple fingers, one or multiple strokes, synchronous or asynchronous stroke input. We introduce, for the first time in the gesture literature, a preprocessing step that is specific to multi-touch gestures (Match-Up) that clusters together similar strokes produced by different fingers, before running a gesture recognizer (Conquer). We report gains in recognition accuracy up to 10% leveraged by our new preprocessing step, which manages to construct a more adequate representation of multi-touch gestures in terms of key strokes. It is our hope that the Match-Up technique will add to the practitioners' toolkit of gesture preprocessing techniques, as a first step toward filling today's lack of algorithmic knowledge to process multi-touch input and leading toward the design of more efficient and accurate recognizers for touch surfaces.
On Tensors, Sparsity, and Nonnegative Factorizations

Tensors have found application in a variety of fields, ranging from chemometrics to signal processing and beyond. In this paper, we consider the problem of multilinear modeling of sparse count data. Our goal is to develop a descriptive tensor factorization model of such data, along with appropriate algorithms and theory. To do so, we propose that the random variation is best described via a Poisson distribution, which better describes the zeros observed in the data as compared to the typical assumption of a Gaussian distribution. Under a Poisson assumption, we fit a model to observed data using the negative log-likelihood score. We present a new algorithm for Poisson tensor factorization called CANDECOMP--PARAFAC alternating Poisson regression (CP-APR) that is based on a majorization-minimization approach. It can be shown that CP-APR is a generalization of the Lee--Seung multiplicative updates. We show how to prevent the algorithm from converging to non-KKT points and prove convergence of CP-APR under mil.
of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Small mammal community structure and composition in the Small mammal community structure and composition in the Cerrado Province of central Brazil Cerrado Province of central Brazil

Community structure, macrohabitat selection, and patterns of species co-occur- rence were examined during a 14-month study of small mammals in the Cerrado Province of central Brazil. Data were collected from mark-recapture grids in brejo and gallery forest, and from live-trapping and specimen collection in all habitat types within cerrado (campo limpo, campo sujo, cerrado [s.s.], cerradao, brejo, valley-side wet campo, and gallery forest). Gallery forest supported the highest species richness, most complex vertical distribution of species, highest level of trophic diversity, and highest macroniche diversity. Degree of habitat selection varied widely. All habitat types supported both rodents and marsupials, although marsupials tended to be much less common in the grasslands (campos) than in woodlands (cerrado) and forests (cerradao, gallery forest). Some species, such as Didelphis albiventris, occurred in all habitat types, while others were much more restricted. Oryzomys bicolor, for example, ocur- red only in gallery forest. No habitat type had a completely distinct fauna: overlap in species composition always occurred with at least one other habitat type. Because of the great varia- bility of habitats, and the fact that subsets of the mammal fauna were frequently habitat specific, the overall species richness of any portion of mixed cerrado vegetation is remarkably high.
, a ¡ ecting denaturing gradientgel electrophoresis pro ¢ les

Fingerprinting methods such as denaturing gradient gel electrophoresis (DGGE) of 16S rRNA gene pools have become a popular tool for comparisons between microbial communities. The GC-clamp portion of primers for DGGE amplicon preparation provides a key component in resolving fragments of similar size but different sequence. We hypothesized that repeat syntheses of identical 40-base GCclamp primers lead to different DGGE profiles. Three repeat syntheses of the same GC-clamp primer and two different GC-clamp primers directed at the V3–5 region of the 16S rRNA gene were compared. Genomic DNA of two separate soil bacterial communities and three bacterial species was amplified and resolved by DGGE. The DGGE profiles obtained with repeat-synthesized primers differed among each other as much as with alternate primers, for both soil DNA and pure single species. The GC-clamp portion of members of amplicon pools varied among each other, deviating from the design sequence, and was the likely cause for multiple bands derived from a single 16S rRNA gene sequence. We recommend procuring an oligonucleotide batch large enough to conduct an entire project. This should help to avoid any DGGE profile variations due to performance differences between repeat syntheses of GC-clamp oligonucleotide primers.
HyperNeat Plus the Connection Cost Technique

One of humanity’s grand scientific challenges is to create artificially intelligent robots that rival natural animals in intelligence and agility. A key enabler of such animal complexity is the fact that animal brains are structurally organized in that they exhibit modularity and regularity, amongst other attributes. Modularity is the localization of function within an encapsulated unit. Regularity refers to the compressibility of the information describing a structure, and typically involves symmetries and repetition. These properties improve evolvability, but they rarely emerge in evolutionary algorithms without specific techniques to encourage them. It has been shown that (1) modularity can be evolved in neural networks by adding a cost for neural connections and, separately, (2) that the HyperNEAT algorithm produces neural networks with complex, functional regularities. In this paper we show that adding the connection cost technique to HyperNEAT produces neural networks that are significantly more modular, regular, and higher performing than HyperNEAT without a connection cost, even when compared to a variant of HyperNEAT that was specifically designed to encourage modularity. Our results represent a stepping stone towards the goal of producing artificial neural networks that share key organizational properties with the brains of natural animals.
Cellular and population plasticity of helper CD4+ T cell responses

Vertebrates are constantly exposed to pathogens, and the adaptive immunity has most likely evolved to control and clear such infectious agents. CD4+ T cells are the major players in the adaptive immune response to pathogens. Following recognition of pathogen-derived antigens naïve CD4+ T cells differentiate into effectors which then control pathogen replication either directly by killing pathogen-infected cells or by assisting with generation of cytotoxic T lymphocytes (CTLs) or pathogen-specific antibodies. Pathogen-specific effector CD4+ T cells are highly heterogeneous in terms of cytokines they produce. Three major subtypes of effector CD4+ T cells have been identified: T-helper 1 (Th1) cells producing IFN-γ and TNF-α, Th2 cells producing IL-4 and IL-10, and Th17 cells producing IL-17. How this heterogeneity is maintained and what regulates changes in effector T cell composition during chronic infections remains poorly understood. In this review we discuss recent advances in our understanding of CD4+ T cell differentiation in response to microbial infections. We propose that a change in the phenotype of pathogen-specific effector CD4+ T cells during chronic infections, for example, from Th1 to Th2 response as observed in Mycobactrium avium ssp. paratuberculosis (MAP) infection of ruminants, can be achieved by conversion of T cells from one effector subset to another (cellular plasticity) or due to differences in kinetics (differentiation, proliferation, death) of different effector T cell subsets (population plasticity). We also shortly review mathematical models aimed at describing CD4+ T cell differentiation and outline areas for future experimental and theoretical research.
Estimation and Direct Equalization of Doubly Selective Channels

We propose channel estimation and direct equalization techniques for transmission over doubly selective channels. The doubly selective channel is approximated using the basis expansion model (BEM). Linear and decision feedback equalizers implemented by time-varying finite impulse response (FIR) filters may then be used to equalize the doubly selective channel, where the time-varying FIR filters are designed according to the BEM. In this sense, the equalizer BEM coefficients are obtained either based on channel estimation or directly. The proposed channel estimation and direct equalization techniques range from pilot-symbol-assisted-modulation- (PSAM-) based techniques to blind and semiblind techniques. In PSAM techniques, pilot symbols are utilized to estimate the channel or directly obtain the equalizer coefficients. The training overhead can be completely eliminated by using blind techniques or reduced by combining training-based techniques with blind techniques resulting in semiblind techniques. Numerical results are conducted to verify the different proposed channel estimation and direct equalization techniques.
Customized Online Aggregation & Summarization Tool for Environmental Rasters (COASTER)

The Customized Online Aggregation & Summarization Tool for Environmental Rasters (COASTER) system (www.COASTERdata.net) was developed by Yellowstone Ecological Research Center (YERC) (www.yellowstoneresearch.org) in response to the information needs of end-user communities interested in decision-support for natural resource management. The purpose of COASTER is to greatly simplify the process of creating predictor datasets for research exploring environmental impacts driven by climate change, land-use activities, disturbance, and invasive spread. COASTER achieves this goal by providing users with a web-based system for processing environmental (gridded, raster) datasets, using a set of standardized functions, to create output customized to meet their analytical needs. In doing so, COASTER effectively translates large and cumbersome datasets into user-specified information useful for parameterizing statistical models and for visualizing spatial and temporal patterns within environmental datasets. The COASTER system currently contains over 10 terabytes of climate data from several sources. These datasets have daily temporal resolutions, spatial resolutions ranging from 1km to 330km, and temporal extents ranging from 30 to 64 years (1948-2011). COASTER datasets are primarily limited to North America, but gridded datasets from other regions can easily be added to the system. Variables within the climatic datasets available on COASTER include metrics quantifying temperature, precipitation, shortwave radiation, vapor pressure deficit, humidity, and wind conditions. Notable features of COASTER include a conceptually simple yet flexible set of functions capable of producing a wide range of outputs, a design applicable to many types of raster datasets, and results formatted for seamless integration within most GIS and remote sensing software packages.
Motion Segmentation by Exploiting Complementary Geometric Models

Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty. Even when we are confronted with a general scene-motion, the fundamental matrix approach as a model for motion segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together. We show that the performance can be substantially improved in this way. We perform extensive testing on existing motion segmentation datasets, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.
Chemical genetic strategy identifies histone deacetylase 1 (HDAC1) and HDAC2 as therapeutic targets in sickle cell disease

The worldwide burden of sickle cell disease is enormous, with over 200,000 infants born with the disease each year in Africa alone. Induction of fetal hemoglobin is a validated strategy to improve symptoms and complications of this disease. The development of targeted therapies has been limited by the absence of discrete druggable targets. We developed a unique bead-based strategy for the identification of inducers of fetal hemoglobin transcripts in primary human erythroid cells. A small-molecule screen of bioactive compounds identified remarkable class-associated activity among histone deacetylase (HDAC) inhibitors. Using a chemical genetic strategy combining focused libraries of biased chemical probes and reverse genetics by RNA interference, we have identified HDAC1 and HDAC2 as molecular targets mediating fetal hemoglobin induction. Our findings suggest the potential of isoform-selective inhibitors of HDAC1 and HDAC2 for the treatment of sickle cell disease.
Bmi-1-targeting suppresses osteosarcoma aggressiveness through the NF-κB signaling pathway

Bone cancer is one of the most lethal malignancies and the specific causes of tumor initiation are not well understood. B-cell-specific Moloney murine leukemia virus integration site 1 protein (Bmi-1) has been reported to be associated with the initiation and progression of osteosarcoma, and as a prognostic indicator in the clinic. In the current study, a full-length antibody targeting Bmi-1 (AbBmi-1) was produced and the preclinical value of Bmi-1-targeted therapy was evaluated in bone carcinoma cells and tumor xenograft mice. The results indicated that the Bmi-1 expression level was markedly upregulated in bone cancer cell lines, and inhibition of Bmi-1 by AbBmi-1 reduced the invasiveness and migration of osteosarcoma cells. Overexpression of Bmi-1 promoted proliferation and angiogenesis, and increased apoptosis resistance induced by cisplatin via the nuclear factor-κB (NF-κB) signal pathway. In addition, AbBmi-1 treatment inhibited the tumorigenicity of osteosarcoma cells in vivo. Furthermore, AbBmi-1 blocked NF-κB signaling and reduced MMP-9 expression. Furthermore, Bmi-1 promoted osteosarcoma tumor growth, whereas AbBmi-1 significantly inhibited osteosarcoma tumor growth in vitro and in vivo. Notably, AbBmi-1 decreased the percentages of Ki67-positive cells and terminal deoxynucleotidyl transferase dUTP nick end labeling-positive cells in tumors compared with Bmi-1-treated and PBS controls. Notably, MMP-9 and NF-κB expression were downregulated by treatment with AbBmi-1 in MG-63 osteosarcoma cells. In conclusion, the data provides evidence that AbBmi-1 inhibited the progression of osteosarcoma, suggesting that AbBmi-1 may be a novel anti-cancer agent through the inhibition of Bmi-1 via activating the NF-κB pathway in osteosarcoma.
Analysis of energy deposition and damage mechanisms in single layer optical thin films irradiated by IR and UV femtosecond pulses

We report on the sub-picosecond laser-induced damage of optical thin films of different thickness made by Magnetron sputtering, Ion assisted deposition and Ion plating, and submitted to single irradiation of the first and the third harmonics of an Ytterbium laser (1030 and 343nm). Using a single rate equation approach for free electron excitation coupled with calculation of the spatial and temporal distribution of the electric field, we investigate numerically the spatial density distribution of the absorbed energy and evaluate its capacity to describe damage phenomena especially damage threshold and morphologies (damage diameter, ablation deepness). Laser-induced damage thresholds are compared for different film thicknesses and different irradiation conditions.
Mediterranean diet and CHD: the Greek European Prospective Investigation into Cancer and Nutrition cohort

Adherence to the Mediterranean diet (MD) has been reported to improve CHD prognosis and to be inversely associated with CHD mortality. The aim of the present study was to investigate the association of adherence to the MD with CHD incidence and mortality in the Greek European Prospective Investigation into Cancer and Nutrition cohort, a population with traditional Mediterranean roots. In a general population sample of 23 929 adult men and women with no CVD or cancer at enrolment, a validated FFQ was interviewer-administered, sociodemographic, physical activity and other characteristics were recorded, and arterial blood pressure and anthropometric characteristics were measured. In a median period of 10 years, 636 incident CHD cases and 240 CHD deaths were recorded. Associations of adherence to the MD, operationalised through a nine-component score (0, poor; 9, excellent), with CHD incidence and mortality were evaluated through Cox regression controlling for potentially confounding variables. A two-point increase in the MD score was associated with lower CHD mortality by 25 % (95 % CI 0·57, 0·98) among women and 19 % (95 % CI 0·67, 0·99) among men. The association of adherence to the MD with CHD incidence was again inverse, but weaker (hazard ratios 0·85 (95 % CI 0·71, 1·02) among women and 0·98 (95 % CI 0·87, 1·10) among men). With respect to score components, only meat among men (positively) and fruits and nuts among women (inversely) were associated with both the incidence of and mortality from CHD. The MD, as an integral entity, is inversely associated with CHD incidence and, particularly, mortality.
A Modelling Ecosystem for Prognostics

This paper evaluates data-driven asset prognostic models from a modelling ecosystem perspective, which includes data description, uncertainty quantification, model selection justification and validation, and application limitations. An easily accessible and comprehensive ecosystem enables efficient reproducibility of previous work to facilitate both the adoption of the models by industry and the development of future scientific methods. The results of this study enable the development of a list of ecosystem elements to accompany the publication of new models. By describing the ecosystem in the communication of new models, researchers can ensure the reproducibility of their models in the wider prognostic community.
An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system re-configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.