Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Osteoporotic Pain in Mice: Cutaneous and Deep Musculoskeletal Pain Are Partially Independent of Bone Resorption and Differentially Sensitive to Pharmacological Interventions

Although the pathological changes in osteoporotic bones are well established, the characterization of the osteoporotic pain and its appropriate treatment are not fully elucidated. We investigated the behavioral signs of cutaneous and deep musculoskeletal pain and physical function; time-dependent changes in bone mineral density (BMD) and the emergence of the behavioral phenotype; and the effects of pharmacological interventions having different mechanisms of action (chronic intraperitoneal administration of pamidronate [0.25 mg/kg, 5x/week for 5 weeks] versus acute treatment with intraperitoneal morphine [10 mg/kg] and pregabalin [100 mg/kg]) in a mouse model of ovariectomized or sham-operated mice 6 months following surgery. We observed reduced BMD associated with weight gain, referred cutaneous hypersensitivity, and deep musculoskeletal pain that persisted for 6 months. Chronic bisphosphonate treatment, 6 months after ovariectomy, reversed bone loss and hypersensitivity to cold, but other behavioral indices of osteoporotic pain were unchanged. While the efficacy of acute morphine on cutaneous pain was weak, pregabalin was highly effective; deep musculoskeletal pain was intractable. In conclusion, the reversal of bone loss alone is insufficient to manage pain in chronic osteoporosis. Additional treatments, both pharmacological and nonpharmacological, should be implemented to improve quality of life for osteoporosis patients.
Error Modeling And Compensation Of 3D Scanning Robot System Based On Pso-Rbfnn

abstract In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct input and output data pairs of neural network, and the specific process of error modeling using PSO-RBFNN is given. Finally, through the actual experiment we test and verify the correctness and effectiveness of the proposed error modeling and compensation method by measuring the distance between the centers of two standard balls. Experimental results show: the proposed error model and the compensation method can effectively compensate the measurement errors and improve the accuracy of the 3D scanning robot system.
Tangerine: a large vocabulary Mandarin dictation system

The text input for non-alphabetic languages, such as Chinese, has been a decades-long problem. Chinese dictation using large vocabulary speech recognition provides a convenient mode of text entry. In contrast to a character based dictation system, a word-based Mandarin dictation system has been designed (based on Apple's PlainTalk speech recognition technology for efficient entry of Chinese characters into a computer. New features and improvements to the dictation system are presented. The new features and improvements have produced an overall reduction in recognition error of 50-80%. The vocabulary has also been increased from 5000 words to over 11000 words.
Assessment and impact analysis for aligning business processes and software systems

Business processes and existing software systems must be aligned so that software systems can adequately support the business processes in order to be effectively used within them. The alignment characteristic needs to be considered even during the execution of an evolution process. In particular, a strict relationship exists between the evolution of a legacy system and that of the supported business process. Therefore, the requirements for evolving a software system embedded in a business process are to be defined on the basis of the change needing to be performed on the process activities. In fact, any modification performed in the business process activities and/or supporting software system may impact the process activities in terms of input/output and/or purpose of the software system and, therefore, cause misalignment. A coarse grained strategy is proposed for detecting misalignment between software systems and supported business processes when a change is executed. In addition, the strategy supports the identification of all the objects, either software system components or process activities, affected by a change and needing to be considered during the evolution process, for keeping the alignment and ensuring the technological support to the business process. The strategy proposes the exploitation of quality parameters, for codifying the alignment concept, and impact analysis techniques, for propagating the change and identifying all the objects affected by a change and requiring new evolution interventions.
Photocatalytic Water Splitting Using ZnWO4 and NiOx Catalysts

Photocatalytic materials are a promising method for producing clean hydrogen. However, current photocatalytic systems involve expensive materials, cannot be scaled up to an industrial level, or suffer from low efficiencies. This study explores a novel combination of ZnWO 4 and NiO x catalysts, which are both inexpensive and also require nothing more than basic fabrication procedures. Our team designed a photocatalytic system with 400-500 nm ZnWO 4 nanoparticles with 1-2 wt% of NiO x co-catalysts to achieve maximum productivity. Design factors such as size, crystallinity, surface area, and composition were investigated to improve photocatalytic performance. Preparation of the samples involved wet chemistry and calcination in a box furnace at temperatures not exceeding 500 C. Samples were successfully fabricated and characterized with X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), particle size analysis and other characterization techniques. The results of the characterization were used to modify the fabrication procedure with the aim of getting our parameters to match our design targets. We also applied density functional theory (DFT) calculations to conduct preliminary relaxations of NiO x and ZnWO 4 crystals as a precursor to calculating bulk band structures for both materials. While the performance of the photocatalytic system is inconclusive due to time constraints, a strong foundation of research and a fully designed system was achieved. We also used kinetic Monte Carlo (KMC) simulations to study the formation of the NiO x co-catalyst with the goal of adjusting our fabrication procedure based on the simulation results. This project demonstrated the successful fabrication of a low cost photocatalytic system, and designed an optimized prototype. To our knowledge, this project was the first to show deposition of NiO x on a ZnWO 4 substrate.
AMBISONIC BASED BINAURAL SOUND REPRODUCTION SYSTEM

A computationally efficient 3D real time rendering engine for binaural sound reproduction via headphones is presented. Binaural sound reproduction requires to filter the virtual sound source signals with head related transfer functions (HRTFs). To improve humans localization capabilities head tracking as well as room simulation have to be incorporated. This yields the problem of high-quality, time-varying interpolation between different HRTFs. To overcome this problem a virtual ambisonic approach is used that results in a bank of time-invariant HRTF filter.
Residential Space Heating Determinants and Supply-Side Restrictions: Discrete Choice Approach

This paper provides an empirical analysis of the supply-side constraints that impact household choices of space heating systems in French dwellings. Based on data from the 2006 National Housing Survey and the 2013 Household Survey, we estimate discrete choice models using socio-demographic, dwelling and spatial characteristics as determinants of choice. In order to capture the supply-side constraints, we perform a post-estimation clustering based on the Expectation-Maximization algorithm, which allows us to determine the groups of households that are most likely to choose each considered space heating system. The results suggest that for 2006, households living in individual houses preferred to heat space using an individual boiler, whereas those living in apartments opted for direct electric heating. In 2013 all households preferred direct electric heating, and wood heating became their second choice. The discrete choice models do not show a significant change in behavior from 2006 to 2013. The post-estimation clustering indicates that many households were strongly constrained by the conditions and characteristics of their living space in 2006. The mean probability of choosing direct electric heating or a boiler was more than 0.8 for 21% of households living in houses and for 36% living in apartments. Thus, these households did not seem to have a choice. In 2013 the supply-side constraints were weaker. The highest mean probability (0.755) is observed in the group opting for an individual boiler. We also observe an increase in the popularity of heat pump and wood heating systems.
α-Bayesian Collaboration of Multiple Predictors and its Applications to Hybrid Recommendation and User Modeling

In recent widespread areas of machine learning or data mining applications, one often should deal with multiple statistical prediction tasks simultaneously, each of which cannot be successful by itself due to limitation of data amount. Among several approaches to utilizing task relationship, a naive but still important approach is to separately train taskspecific predictors in advance and then integrate them at the time of prediction. In this study, we propose a general framework of realizing such a “collaborative prediction” mechanism, specifically based on an existing generalization of Bayesian predictive distribution using the α-divergence. We also propose a novel hybrid method of collaborative and content-based recommendations, under the proposed framework. We demonstrate the effectiveness of the proposed method by using two kinds of real datasets; one is the Movielens benchmark collection related to recommendation, and the other is a real log dataset of shared electronic devices related to a particular problem of user modeling.
Research Statement 1 Dynamic Software Updating

My research interests span the areas of programming languages, operating systems, and software engineering. Broadly, my aim is safe, available, secure and scalable software. My current research focuses on dynamic software updating (DSU)—an approach to updating programs while they run. In my dissertation, I have developed techniques to make DSU practical: safe, easy to use, and applicable to realistic programs. In addition to my thesis research, I have also worked on finding errors in concurrent programs using systematic exploration and remote healing (an operating system and architectural approach to self-healing clusters). In my future work, I am interested in updating operating systems on the fly, allowing software to make use of multi-core CPUs, and ensuring safe and secure execution using static and dynamic techniques. I believe my set of skills and interests puts me in a favorable position to address current software challenges. My background in programming language theory allows me to take software problems, cast them in a formal framework, and produce solutions backed by rigorous proofs. My research experience with writing static analyzes and a compiler helps me apply these solutions to mainstream programming languages. My operating systems background and experience with large programs allows me to build effective implementations and evaluate them on realistic applications and workloads. The remainder of this research statement illustrates how my work combines techniques from programming languages, operating systems, and software engineering to achieve safe, available, secure and scalable software. My past and future research can be grouped into three categories: dynamic software updating, safe concurrent programming, and reliability and security monitoring.
Vector Finite Difference Modesolver for Anisotropic Dielectric Waveguides

We describe a new full-vector finite difference discretization, based upon the transverse magnetic field components, for calculating the electromagnetic modes of optical waveguides with transverse, nondiagonal anisotropy. Unlike earlier finite difference approaches, our method allows for the material axes to be arbitrarily oriented, as long as one of the principal axes coincides with the direction of propagation. We demonstrate the capabilities of the method by computing the circularly-polarized modes of a magnetooptical waveguide and the modes of an off-axis poled anisotropic polymer waveguide.
A decentralized approach to cooperative situation assessment in multi-robot systems

To act effectively under uncertainty, multi-robot teams need to accurately estimate the state of the environment. Although individual robots, with uncertain sensors, may not be able to accurately determine the current situation, the team as a whole should have the capability to perform situation assessment. However, sharing all information with all other team mates is not scalable nor is centralization of all information possible. This paper presents a decentralized approach to cooperative situation assessment that balances use of communication bandwidth with the need for good situation assessment. When a robot believes locally that a particular plan should be executed, it sends a proposal for that plan, to one of its team mates. The robot receiving the plan proposal, can either agree with the plan and forward it on, or it can provide sensor information to suggest that an alternative plan might have higher expected utility. Once sufficient robots agree with the proposal, the plan is initiated. The algorithm successfully balances the value of cooperative sensing against the cost of sharing large volumes of information. Experiments verify the utility of the approach, showing that the algorithm dramatically out-performs individual decision-making and obtains performance similar to a centralized approach.
AlGaN/GaN HFET: Operating Principle And Noise Performance

The operating principle and noise performance in AlGaN/GaN HFETs has been studied. In particular, it has been demonstrated that substrate material selection plays a great role in device performance for application point of view. Polarization induced surface states plays key role in formation of 2 DEG. Performance parameters study of device is related to noise study in devices because low frequency noise affects the device performance. Different types of noise and their possible reasons has been studied.
Age Estimation Based on AAM and 2D-DCT Features of Facial Images

This paper proposes a novel age estimation method - Global and Local feAture based Age estiMation (GLAAM) - relying on global and local features of facial images. Global features are obtained with Active Appearance Models (AAM). Local features are extracted with regional 2D-DCT (2- dimensional Discrete Cosine Transform) of normalized facial images. GLAAM consists of the following modules: face normalization, global feature extraction with AAM, local feature extraction with 2D-DCT, dimensionality reduction by means of Principal Component Analysis (PCA) and age estimation with multiple linear regression. Experiments have shown that GLAAM outperforms many methods previously applied to the FG-NET database.
A large scale, homogeneous, fully distributed parallel machine, I

The preliminary hardware description of CHOPP (Columbia Homogeneous Parallel Processor), a MIMD machine supporting a fully distributed host-less operating system is presented. The architecture is intended to permit implementation of machines with 10<supscrpt>5</supscrpt> to 10<supscrpt>6</supscrpt> processors. Issues of interconnection networks, throughput, and memory structure are treated.
CRRA UTILITY MAXIMIZATION UNDER DYNAMIC RISK CONSTRAINTS

The problem of optimal investment with CRRA (constant, relative risk aversion) preferences, subject to dynamic risk constraints on trading strategies, is the main focus of this paper. Several works in the literature, which deal either with optimal trading under static risk constraints or with VaR{based dynamic risk constraints, are extended. The market model considered is continuous in time and incomplete, and the prices of nancial assets are modeled by It^o processes. The dynamic risk constraints, which are time and state dependent, are generated by a general class of risk measures. Optimal trading strategies are characterized by a quadratic BSDE. Within the class of time consistent distortion risk measures, a three{fund separation result is established. Numerical results emphasize the eects of imposing risk constraints on trading.
Environmental Independent ASR Model Adaptation/Compensation by Bayesian Parametric Representation

The mismatch between system training and operating conditions can seriously deteriorate the performance of automatic speech recognition (ASR) systems. Various techniques have been proposed to solve this problem in a specified speech environment. Employment of these techniques often involves modification on the ASR system structure. In this paper, we propose an environment-independent (EI) ASR model parameter adaptation approach based on Bayesian parametric representation (BPR), which is able to adapt ASR models to new environments without changing the structure of an ASR system. The parameter set of BPR is optimized by a maximum joint likelihood criterion which is consistent with that of the hidden Markov model (HMM)-based ASR model through an independent expectation-maximization (EM) procedure. Variations of the proposed approach are investigated in the experiments designed in two different speech environments: one is the noisy environment provided by the AURORA 2 database, and the other is the network environment provided by the NTIMIT database. Performances of the proposed EI ASR model compensation approach are compared to those of the cepstral mean normalization (CMN) approach, which is one of the standard techniques for additive noise compensation. The experimental results show that performances of ASR models in different speech environments are significantly improved after being adapted by the proposed BPR model compensation approach
B-Cyclin/CDKs Regulate Mitotic Spindle Assembly by Phosphorylating Kinesins-5 in Budding Yeast

Although it has been known for many years that B-cyclin/CDK complexes regulate the assembly of the mitotic spindle and entry into mitosis, the full complement of relevant CDK targets has not been identified. It has previously been shown in a variety of model systems that B-type cyclin/CDK complexes, kinesin-5 motors, and the SCFCdc4 ubiquitin ligase are required for the separation of spindle poles and assembly of a bipolar spindle. It has been suggested that, in budding yeast, B-type cyclin/CDK (Clb/Cdc28) complexes promote spindle pole separation by inhibiting the degradation of the kinesins-5 Kip1 and Cin8 by the anaphase-promoting complex (APCCdh1). We have determined, however, that the Kip1 and Cin8 proteins are present at wild-type levels in the absence of Clb/Cdc28 kinase activity. Here, we show that Kip1 and Cin8 are in vitro targets of Clb2/Cdc28 and that the mutation of conserved CDK phosphorylation sites on Kip1 inhibits spindle pole separation without affecting the protein's in vivo localization or abundance. Mass spectrometry analysis confirms that two CDK sites in the tail domain of Kip1 are phosphorylated in vivo. In addition, we have determined that Sic1, a Clb/Cdc28-specific inhibitor, is the SCFCdc4 target that inhibits spindle pole separation in cells lacking functional Cdc4. Based on these findings, we propose that Clb/Cdc28 drives spindle pole separation by direct phosphorylation of kinesin-5 motors.
Protein Flexibility (3): ROCK and FRODA

This lecture talked about two examples of using FIRST to explore protein flexibility: ROCK [1, 2] and FRODA [3]. 1 The ROCK Algorithm 1.1 Terminology Ring: A closed loop of bonds in which any two atoms are connected by two distinct paths. Interlocked rings: Two rings that share common bond(s). Ring cluster: A collection of rings that are interlocked with one another. Side branches: All atoms that do not belong to any ring cluster. Figure 1 shows examples of ring, interlocked ring, ring cluster and side branch. Figure 1: Examples of ring, interlocked ring, ring cluster and side branch.
Efficient Driving on Multilane Roads Under a Connected Vehicle Environment

Traffic anticipation enhances driving intelligence and strengthens the ability to take early vehicle control action, e.g., lane change and speed adjustment, in a dynamically varying traffic environment. This paper presents an efficient vehicle driving system, based on detailed anticipation of surrounding traffic, that aims at optimizing the driving performance of individual vehicles and smoothening traffic flows on multilane roads. More elaborately, under a connected vehicle environment, the system receives the states of all vehicles that exist within its communication range. Based on their predicted states in a look forward horizon, the system generates the optimal acceleration and makes lane change decision simultaneously in the model predictive control framework. A fast hierarchical optimization scheme is used in the framework for its onboard implementation. The proposed efficient driving system is applied to a fraction of traffic, and both the individual and overall traffic performances are evaluated using a microscopic traffic simulator. It is revealed that the vehicles under the proposed efficient driving system improve their fuel economy and travel efficiency, significantly. In the mixed traffic, by the influence of the vehicle with the proposed driving system, the other traditionally driven vehicles also improve their performance.
Superblock: An Application-Aware Dynamic Partition Strategy for Large-Scale Graph

The emergence of large-scale graph data has posed essential challenges for processing them efficiently. The fundamental step for effectively processing the graph is to partition the graph and distribute the relevant parts on multiple workers for parallel computing. The existing partition strategies may suffer from the following problems: 1) They ignore the certain application features, making the partition not satisfy the application needs, which may cause performance degradation, 2) Because of the ignorance of the applications features, current partition strategies are not dynamic to meet the needs from different applications. In this paper, the Superblock partition strategy, an application-aware dynamic partition strategy for large-scale data is proposed to solve the above problems. It pre-partitions the graph into blocks and then extracts the application features and combines the blocks into Superblocks. The Superblock will be re-constructed when new application arrives as well. Experiments are performed using some common graph algorithms to confirm that the Superblock partition strategy can boost the performance of various data processing application on large-scale graph data and be dynamic enough to alter the partitions for different applications.