Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Analytical Approach to Predicting Asphalt Pavement Temperature

AbstractThe temperature profile within the asphalt layer of a flexible pavement plays an important role in the design of hot-mix asphalt and in the performance models of asphalt concrete materials such as low-temperature cracking and asphalt binder oxidation. In this paper, the time-dependent temperature profile within an asphalt concrete layer is modeled as the heat conduction problem in a one-layer pavement system, where the surface boundary condition is simply the pavement surface-temperature history during the time period of interest. An infinite series solution for asphalt layer temperature prediction was derived using measured pavement surface temperatures. The main mathematical tools employed in deriving the analytical solution are separation of variables and Duhamel’s principle. Mathematically, the infinite series are proven to converge uniformly over the finite physical domain at a fixed time t. Field validation based on measured in situ temperature data demonstrated that the proposed closed-form.
OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar

This paper introduces our Chinese semantic dependency parsing system for Task 9 of Se-mEval 2016. Our system has two components: a parser trained using the Berkeley Grammar Trainer on the Penn Chinese Treebank rean-notated in a Generalized Categorial Grammar, and a multinomial logistic regression classi-ﬁer. We ﬁrst parse the data with the automatic parser to obtain predicate-argument dependencies and then we use the classiﬁer to predict the semantic dependency labels for the predicate-argument dependency relations extracted. Although our parser is not trained directly on the task training data, our system yields the best performance for the non-local dependency recovery for the news data and comparable overall results.
The Angular Interval between the Direction of Progression and Body Orientation in Normal, Alcohol- and Cocaine Treated Fruit Flies

In this study we characterize the coordination between the direction a fruit-fly walks and the direction it faces, as well as offer a methodology for isolating and validating key variables with which we phenotype fly locomotor behavior. Our fundamental finding is that the angular interval between the direction a fly walks and the direction it faces is actively managed in intact animals and modulated in a patterned way with drugs. This interval is small in intact flies, larger with alcohol and much larger with cocaine. The dynamics of this interval generates six coordinative modes that flow smoothly into each other. Under alcohol and much more so under cocaine, straight path modes dwindle and modes involving rotation proliferate. To obtain these results we perform high content analysis of video-tracked open field locomotor behavior. Presently there is a gap between the quality of descriptions of insect behaviors that unfold in circumscribed situations, and descriptions that unfold in extended time and space. While the first describe the coordination between low-level kinematic variables, the second quantify cumulative measures and subjectively defined behavior patterns. Here we reduce this gap by phenotyping extended locomotor behavior in terms of the coordination between low-level kinematic variables, which we quantify, combining into a single field two disparate fields, that of high content phenotyping and that of locomotor coordination. This will allow the study of the genes/brain/locomotor coordination interface in genetically engineered and pharmacologically manipulated animal models of human diseases.
A null model for cortical representations with grandmothers galore

ABSTRACT There has been extensive discussion in the literature about the extent to which cortical representations can be described as localist or distributed. Here, we discuss a simple null model that encompasses a family of related architectures describing the transformation of signals throughout the parts of the visual system involved in object recognition. This family of models constitutes a rigorous first approximation to explain the neurophysiological properties of ventral visual cortex. This null model contains both distributed and local representations throughout the entire hierarchy of computations and the responses of individual units are meaningful and interpretable when encoding is adequately defined for each computational stage.
DCD: Distributed charging and discharging scheme for EVs in microgrids

The increasing number of electric vehicles (EVs) will have a significant impact on the demand response (DR) in smart grids (SGs). How to encourage EV users to voluntarily join vehicle-to-grid (V2G) market has been one of the significant challenges to SGs engineers and scientists. On the other hand, dealing with the randomness of renewable distributed energy resources (DERs) output is critical for microgrids to maintain their efficiency and reliability. In this work, a distributed charging and discharging scheduling scheme (DCD) for EVs in microgrids is proposed to provide electric vehicle (EV) users with energy cost reduction while promoting the integration of renewable energy sources (RESs) by reducing the negative impact of highly intermittent characteristics of RESs. The comparison results based on the real measured renewable energy and load data show that the proposed scheme enables EVs to become promisingly important contributors to microgrids.
Graph and point cloud matching for image registration PhD thesis proposal

In this document, we discuss the registration of images using graph and point cloud matching, with a special focus on medical imaging applications. Several parts of the human body present tree-like structures such as neuronal or pulmonary trees, retinal fundus, or blood vessels. There is a high interest in registering these structures due to many issues or limitations – acquiring images of the same structure using diﬀerent modalities, discovering the correspondences between images taken at diﬀerent time points, or building a complete image when the acquisition technique only allows the partial capture. Often a solution to this problem is to evaluate the intensity or texture, maximizing the similarity between these descriptors. However, this is not the optimal approach when registering images acquired from diﬀerent modalities, which can look very diﬀerent in terms of these properties. A broader approach is to ﬁnd the position of the underlying structures, and match their geometrical appearance in order to register the images. These structures can be represented with geometrical positions in 2D and 3D, and they can be matched using point cloud matching, or graph matching methods if the physical links between the points are also extracted. In this document, we focus on point cloud and graph matching methods, assuming that the segmentation of the structures is given. We will study in particular the registration of neuronal trees in electron and light microscopy images, where the images look very diﬀerent to use feature based registration techniques. Moreover, current state of the art point and graph matching methods are not able to register robustly these types of structures due to the misalignment and deformation between the sets. Several point cloud and graph matching approaches have been presented over the years, most with emphasis on applications other than medical imaging, such as character recognition, object registration. In this document, we go over the state of the art methods, which can be used for the problem, and we also present a framework of synthetic and real data used to compare them. We show that there is a clear demand for solving this problem – some methods are not able to match the structures, and/or show an impractical time complexity for our problem, where we have a high number of points. We present a technique for graph and point cloud matching, which quickly ﬁnds a coarse approximation of the solution by exploring a reduced set of partial matches using an approach
Exact Random Coding Exponents for Erasure Decoding

Random coding of channel decoding with an erasure option is studied. By analyzing the large deviations behavior of the code ensemble, we obtain exact single-letter formulas for the error exponents in lieu of Forney's lower bounds. The analysis technique we use is based on an enhancement and specialization of tools for assessing the statistical properties of certain distance enumerators. We specialize our results to the setup of the binary symmetric channel case with uniform random coding distribution and derive an explicit expression for the error exponent which, unlike Forney's bounds, does not involve optimization over two parameters. We also establish the fact that for this setup, the difference between the exact error exponent corresponding to the probability of undetected decoding error and the exponent corresponding to the erasure event is equal to the threshold parameter. Numerical calculations indicate that for this setup, as well as for a Z-channel, Forney's bound coincides with the exact random coding exponent.
Cumulative Path Summary for Structurally Dynamic XML Documents

In XML filtering systems, if the structure is not known, user may input structurally invalid queries. The filtering system wastes its precious time in evaluating these invalid queries before it eventually finds that the queries are structurally invalid. In this paper we propose a technique to guide the user in formulating queries, which are more likely to succeed.
Superhydrophobic, Reversibly Elastic, Moldable, and Electrospun (SupREME) Fibers with Multimodal Functions: From Oil Absorbents to Local Drug Delivery Adjuvants

Surface hydrophobicity has served as a core means for governing the spatial behaviors of numerous substances depending on their affinities to oil or aqueous phases. Exploiting systems that can maximize hydrophobic features contributes to the development of versatile supports capable of spatially separating, guiding, or protecting target materials in defined manners. Herein, superhydrophobic, reversibly elastic, moldable, and electrospun (SupREME) fibers, which exhibit multimodal functions for arranging spatial responses of substances with distinct affinities to oil phases, are fabricated by coaxially electrospinning polysulfone and poly(glycerol sebacate) (PGS), followed by a thermal process. The exterior PSF layers enable volumetric expansion of the fibers, further reinforcing the overall superhydrophobicity (contact angles >150°). The elastic core PGS networks confer reversibly compressible properties (>100 cycles) to the fibers, ultimately enhancing their hydrophobic performance and extending their durability. The SupREME fibers demonstrate superiorities as absorbents for selectively separating oil‐based substances, sealants for blocking the leakage of aqueous fluids, and adjuvants for temporarily enhancing the local residence of drugs by repelling ambient fluidic environments. The SupREME fibers can be versatile platforms in many applications that require the spatial regulation of specific substances with affinities to oil or water phases, ranging from environmental industries to medical fields.
The Impact of Using Regression Models to Build Defect Classifiers

It is common practice to discretize continuous defect counts into defective and non-defective classes and use them as a target variable when building defect classifiers (discretized classifiers). However, this discretization of continuous defect counts leads to information loss that might affect the performance and interpretation of defect classifiers. Another possible approach to build defect classifiers is through the use of regression models then discretizing the predicted defect counts into defective and non-defective classes (regression-based classifiers). In this paper, we compare the performance and interpretation of defect classifiers that are built using both approaches (i.e., discretized classifiers and regression-based classifiers) across six commonly used machine learning classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART, and neural networks) and 17 datasets. We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches, ii) In contrast to common practice, building a defect classifier using discretized defect counts (i.e., discretized classifiers) does not always lead to better performance. Hence we suggest that future defect classification studies should consider building regression-based classifiers (in particular when the defective ratio of the modeled dataset is low). Moreover, we suggest that both approaches for building defect classifiers should be explored, so the best-performing classifier can be used when determining the most influential features.
A model for full local image interpretation

We describe a computational model of humans' ability to provide a detailed interpretation of a scene’s components. Humans can identify in an image meaningful components almost everywhere, and identifying these components is an essential part of the visual process, and of understanding the surrounding scene and its potential meaning to the viewer. Detailed interpretation is beyond the scope of current models of visual recognition. Our model suggests that this is a fundamental limitation, related to the fact that existing models rely on feed-forward but limited top-down processing. In our model, a first recognition stage leads to the initial activation of class candidates, which is incomplete and with limited accuracy. This stage then triggers the application of class-specific interpretation and validation processes, which recover richer and more accurate interpretation of the visible scene. We discuss implications of the model for visual interpretation by humans and by computer vision models.
Additive Gaussian Processes

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.
Banshee: Bandwidth-Efficient DRAM Caching via Software/Hardware Cooperation

Placing the DRAM in the same package as a processor enables several times higher memory bandwidth than conventional off-package DRAM. Yet, the latency of in-package DRAM is not appreciably lower than that of off-package DRAM. A promising use of in-package DRAM is as a large cache. Unfortunately, most previous DRAM cache designs optimize mainly for cache hit latency and do not consider bandwidth efficiency as a first-class design constraint. Hence, as we show in this paper, these designs are suboptimal for use with in-package DRAM.We propose a new DRAM cache design, Banshee, that optimizes for both in-package and off-package DRAM bandwidth efficiency without degrading access latency. Banshee is based on two key ideas. First, it eliminates the tag lookup overhead by tracking the contents of the DRAM cache using TLBs and page table entries, which is efficiently enabled by a new lightweight TLB coherence protocol we introduce. Second, it reduces unnecessary DRAM cache replacement traffic with a new bandwidth-aware frequency-based replacement policy. Our evaluations show that Banshee significantly improves performance (15% on average) and reduces DRAM traffic (35.8% on average) over the best-previous latency-optimized DRAM cache design.CCS CONCEPTS•Computersystemsorganization → Multicore architectures; {\it Heterogeneous (hybrid) systems;
Common Ancestry and Novel Genetic Traits of Francisella novicida-Like Isolates from North America and Australia as Revealed by Comparative Genomic Analyses

ABSTRACT Francisella novicida is a close relative of Francisella tularensis, the causative agent of tularemia. The genomes of F. novicida-like clinical isolates 3523 (Australian strain) and Fx1 (Texas strain) were sequenced and compared to F. novicida strain U112 and F. tularensis strain Schu S4. The strain 3523 chromosome is 1,945,310 bp and contains 1,854 protein-coding genes. The strain Fx1 chromosome is 1,913,619 bp and contains 1,819 protein-coding genes. NUCmer analyses revealed that the genomes of strains Fx1 and U112 are mostly colinear, whereas the genome of strain 3523 has gaps, translocations, and/or inversions compared to genomes of strains Fx1 and U112. Using the genome sequence data and comparative analyses with other members of the genus Francisella, several strain-specific genes that encode putative proteins involved in RTX toxin production, polysaccharide biosynthesis/modification, thiamine biosynthesis, glucuronate utilization, and polyamine biosynthesis were identified. The RTX toxin synthesis and secretion operon of strain 3523 contains four open reading frames (ORFs) and was named rtxCABD. Based on the alignment of conserved sequences upstream of operons involved in thiamine biosynthesis from various bacteria, a putative THI box was identified in strain 3523. The glucuronate catabolism loci of strains 3523 and Fx1 contain a cluster of nine ORFs oriented in the same direction that appear to constitute an operon. Strains U112 and Schu S4 appeared to have lost the loci for RTX toxin production, thiamine biosynthesis, and glucuronate utilization as a consequence of host adaptation and reductive evolution. In conclusion, comparative analyses provided insights into the common ancestry and novel genetic traits of these strains.
Beamforming with quantized feedback to increase diversity of relay channels

In this paper, beamforming schemes that maximize diversity of the relay systems for amplify and forward (AF) and decode and forward (DF) methods are presented. For fading relay channels, these schemes are simulated with the relays having partial (quantized) information of the relay-destination channel, and compared to complete information case. Under utilized quantization process, phases and amplitudes of the channel fading coefficients are fed back to the relays in different number of bits. Phase information of the channel coefficients appear to be more crucial for both AF and DF methods. While the beamforming scheme for AF results in roughly full diversity order, the scheme for DF cannot achieve full diversity order because of its dependence to the decoding errors made at the relays.
Estrogenic and anti-androgenic endocrine disrupting chemicals and their impact on the male reproductive system

Endocrine disrupting chemicals (EDCs) are identified for their ability to perturb the homeostasis of endocrine system and hormonal balance. The male reproductive system is under close control of hormones and each change in their concentration and time of exposition and action can induce a deregulation of its physiology. In this review we summarize the most recent studies on two main categories of EDCs with different action: the estrogenic bisphenol A and alkylphenols and the anti-androgenic phthalates. This review describes the main effects of these substances on male reproductive system.
City Dweller Responses to Multiple Stressors Intruding into Their Homes: Noise, Light, Odour, and Vibration

Urban densification increases exposure to noise, light, odour, and vibration in urban dwellings. Exposure from combined environmental stressors intruding into the home could increase the risk of adverse effects on wellbeing, even when the exposure is at a relatively low level. This study assesses the prevalence of annoyance with a combination of potential environmental stressors common in urban areas and the association with wellbeing. A questionnaire was sent by mail to residents in five areas in Halmstad (Sweden) with similar socioeconomic and housing characteristics but different exposure (response rate 56%; n = 385). Of the respondents, 50% were annoyed to some degree by at least one of the suggested stressors, most commonly by noise and vibration from local traffic. Structural equation modelling showed that annoyance led to lowered quality of life via the mediating construct residential satisfaction, which in turn was influenced by place attachment and perceived restoration possibilities in the dwelling. Stress had a negative impact on quality of life, but was not directly correlated to annoyance. Stress was however correlated with sensitivity. The findings suggest that dose-response relationships for environmental stressors should be studied in a broader context of environmental and individual factors. Also relatively low levels of exposure should be mitigated, especially if several stressors are present.
Semantic Analysis and Implicit Target Extraction of Comments from E-Commerce Websites

In e-commerce websites, customers usually make comments, which include the properties of the product, the attitude to the vendor, express delivery information after buying the products. The information provides an important reference when others buy products in the website. In sentiment analysis, a finer-grained opinion mining approach focuses on not only the product itself as a whole but also product features, which can be a part or attribute of the product. Previous related research focuses on the explicit target mining but neglects the implicit ones. Whereas, the implicit features, which are implied by some words or phrases, are very significant and meaningful to express users' opinion. In this paper, we propose a new approach to uniformly extract explicit and implicit opinion features from the comments. Our experimental results prove the effectiveness of the approach. The recall is improved, suggesting that taking the implicit features into consideration can extract more useful information, meanwhile, the precision is stable, not decreases.
Effect of Antimicrobial Denture Base Resin on Multi-Species Biofilm Formation

Our aims of the research were to study the antimicrobial effect of dimethylaminododecyl methacrylate (DMADDM) modified denture base resin on multi-species biofilms and the biocompatibility of this modified dental material. Candida albicans (C. albicans), Streptococcus mutans (S. mutans), Streptococcus sanguinis (S. sanguinis), as well as Actinomyces naeslundii (A. naeslundii) were used for biofilm formation on denture base resin. Colony forming unit (CFU) counts, microbial viability staining, and 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) array were used to evaluate the antimicrobial effect of DMADDM. C. albicans staining and Real-time PCR were used to analyze the morphology and expression of virulence genes of C. albicans in biofilm. Lactate dehydrogenase (LDH) array and Real-time PCR were conducted to examine the results after biofilm co-cultured with epithelial cell. Hematoxylin and eosin (HE) staining followed by histological evaluation were used to study the biocompatibility of this modified material. We found that DMADDM containing groups reduced both biomass and metabolic activity of the biofilm significantly. DMADDM can also inhibit the virulence of C. albicans by means of inhibiting the hyphal development and downregulation of two virulence related genes. DMADDM significantly reduced the cell damage caused by multi-species biofilm according to the LDH activity and reduced the expression of IL-18 gene of the cells simultaneously. The in vivo histological evaluation proved that the addition of DMADDM less than 6.6% in denture material did not increase the inflammatory response (p > 0.05). Therefore, we proposed that the novel denture base resin containing DMADDM may be considered as a new promising therapeutic system against problems caused by microbes on denture base such as denture stomatitis.