Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Chemical Labeling of Proteins with Small Fluorescent Molecules Based on Metal-Chelation Methodology

Site-specific chemical labeling utilizing small fluorescent molecules is a powerful and attractive technique for in vivo and in vitro analysis of cellular proteins, which can circumvent some problems in genetic encoding labeling by large fluorescent proteins. In particular, affinity labeling based on metal-chelation, advantageous due to the high selectivity/simplicity and the small tag-size, is promising, as well as enzymatic covalent labeling, thereby a variety of novel methods have been studied in recent years. This review describes the advances in chemical labeling of proteins, especially highlighting the metal-chelation methodology.
Wear and Morphology of Ultra-High Molecular Weight Polyethylene Wear Particles from Total Hip Replacements

The wear rate of ultra-high molecular weight polyethylene total hip replacement components is known to be influenced by various factors such as material and design. However, it is not known if these factors affect the size or morphology of the wear particles. The aim of this study was to compare the polyethylene wear particles from hip replacements of differing bearing materials and designs. Tissues were obtained at the revision surgeries of patients with surface replacements with titanium alloy or cobalt-chrome alloy femoral components up to 51 mm in diameter, and stem-type hip replacements with cobalt-chrome or alumina ceramic femoral components that were 28 or 32 mm in diameter. The polyethylene particles were isolated following tissue digestion and density gradient separation, and then studied by scanning electron microscopy. A computerized image analysis system was used to measure the diameter and length of the particles. The majority of wear particles were submicron in diameter. No systematic differences in size and morphology were found between the groups in this study. The similarity in size and morphology of the wear particles suggested that the same basic wear mechanisms were occurring in these components.
Noise-tolerant learning, the parity problem, and the statistical query model

We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptographic and coding problems. Our algorithm runs in polynomial time for the case of parity functions that depend on only the first O(log n log log n) bits of input, which provides the first known instance of an efficient noise-tolerant algorithm for a concept class that is not learnable in the Statistical Query model of Kearns [1998]. Thus, we demonstrate that the set of problems learnable in the statistical query model is a strict subset of those problems learnable in the presence of noise in the PAC model.In coding-theory terms, what we give is a poly(n)-time algorithm for decoding linear k × n codes in the presence of random noise for the case of k = c log n log log n for some c > 0. (The case of k = O(log n) is trivial since one can just individually check each of the 2k possible messages and choose the one that yields the closest codeword. )A natural extension of the statistical query model is to allow queries about statistical properties that involve t-tuples of examples, as opposed to just single examples. The second result of this article is to show that any class of functions learnable (strongly or weakly) with t-wise queries for t = O(log n) is also weakly learnable with standard unary queries. Hence, this natural extension to the statistical query model does not increase the set of weakly learnable functions.
Lung volume estimation for thoracic respiration via chest size deviation measurement

Individual thermal stress of people in physical training has not been evaluated quantitatively in real time. The main limitations in evaluating individual thermal stress are the difficulties in measuring individual thermal parameters, clothing insulation, and metabolic rate. In this study, we aimed to overcome these limitations with metabolic rate measurement. For calculating metabolic rate in real time without breath gas analysis, we proposed a derivation method from oxygen consumption and lung volume. We found that the deviation of chest size is proportional to the change of lung volume in each person, and the coefficient of determination was greater than 0.90.
Sparsity and scale: Compact representations of deformation for diffeomorphic registration

In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation across scales. In contrast, the LDDKBM extension of LDDMM allows representations of deformation at multiple scales but it does not favour compactness and hence may represent deformation at more scales than necessary. In this paper, we combine a sparsity prior with the multi-scale framework resulting in an algorithm allowing compact representation of deformation across scales. We present a mathematical formulation of the algorithm and evaluate it on a dataset of annotated lung CT images.
CORPUS OF SPONTANEOUS JAPANESE : ITS DESIGN AND EVALUATION

Corpus of Spontaneous Japanese, or CSJ, is a large-scale database of spontaneous Japanese. It contains speech signal and transcription of about 7 million words along with various annotations like POS and phonetic labels. After describing its design issues, preliminary evaluation of the CSJ was presented. The results suggest strongly the usefulness of the CSJ as the resource for the study of spontaneous speech.
Lightly-Supervised Training for Hierarchical Phrase-Based Machine Translation

In this paper we apply lightly-supervised training to a hierarchical phrase-based statistical machine translation system. We employ bitexts that have been built by automatically translating large amounts of monolingual data as additional parallel training corpora. We explore different ways of using this additional data to improve our system. Our results show that integrating a second translation model with only non-hierarchical phrases extracted from the automatically generated bitexts is a reasonable approach. The translation performance matches the result we achieve with a joint extraction on all training bitexts while the system is kept smaller due to a considerably lower overall number of phrases.
Emulation of an ASIC power and temperature monitor system for FPGA prototyping

Monitoring information can be used during system runtime to increase system lifetime and reliability. Examples of such monitoring information are power or temperature values. They provide the system with relevant information about the current hardware health. In a resource-aware computing system, where resources are allocated according to current requirements, the current hardware status must be included in the decisions for resource allocation in order to select the best suitable application-resource pairs and at the same time reach system targets like limiting the system temperature.To test and optimize the system's resource allocation already in the design phase, FPGA prototyping is often required before its implementation in an ASIC. Since the behavior of measured power and temperature evolution in FPGA and ASIC differ, data from power and temperature sensors on both platforms would have significant differences. To efficiently test and optimize the resource allocation for an ASIC implementation, it is necessary to provide the FPGA prototype with modeled power and temperature values characterized from an ASIC implementation. This paper describes how to emulate the behavior of an emulated ASIC power and temperature monitor system (eTPMon) on FPGA. The approach for emulating the power monitor is based on an instruction-level energy model. For emulating the temperature monitor, a thermal RC model is used. It is shown that eTPMon can supply an invasive MPSoC with the hardware status information (power and temperature of the cores) needed for efficient resource-aware load distribution to develop a resource-aware computing system concept. As a proof of concept different operating strategies and control targets were evaluated for a 2-tile invasive MPSoC.
Multidisciplinary interobserver agreement in the diagnosis of idiopathic pulmonary fibrosis

The purpose of the present study was to evaluate the accuracy of the diagnosis of idiopathic pulmonary fibrosis (IPF) by respiratory physicians in six European countries, and to calculate the interobserver agreement between high-resolution computed tomography reviewers and histology reviewers in IPF diagnosis. The diagnosis of usual interstitial pneumonia (UIP) was assessed by a local investigator, following the American Thoracic Society/European Respiratory Society consensus statement, and confirmed when a minimum of two out of three expert reviewers from each expert panel agreed with the diagnosis. The level of agreement between readers within each expert panel was calculated by weighted kappa. The diagnosis of UIP was confirmed by the expert panels in 87.2% of cases. A total of 179 thoracic high-resolution computed tomography scans were independently reviewed, and an interobserver agreement of 0.40 was found. Open or thoracoscopic lung biopsy was performed in 97 patients, 82 of whom could be reviewed by the expert committee. The weighted kappa between histology readers was 0.30. It is concluded that, although the level of agreement between the readers within each panel was only fair to moderate, the overall accuracy of a clinical diagnosis of idiopathic pulmonary fibrosis in expert centres is good (87.2%).
The CUHK Spoken Web Search System for MediaEval 2013

This paper describes an audio keyword detection system developed at the Chinese University of Hong Kong (CUHK) for the spoken web search (SWS) task of MediaEval 2013. The system was built only on the provided unlabeled data, and each query term was represented by only one query example (from the basic set for required runs). This system was designed following the posteriorgram-based template matching framework, which used a tokenizer to convert the speech data into posteriorgrams, and then applied dynamic time warping (DTW) for keyword detection. The main features of the system are: 1) a new approach of tokenizer construction based on Gaussian component clustering (GCC) and 2) query expansion based on the technique called pitch synchronous overlap and add (PSOLA). The MTWV and ATWV of our system on the SWS2013 Evaluation set are 0.306 and 0.304.
Precomputed Radiance Transfer for Real-Time Rendering in Dynamic, Low-Frequency Lighting Environments

We present a new, real-time method for rendering diffuse and glossy objects in low-frequency lighting environments that captures soft shadows, interreflections, and caustics. As a preprocess, a novel global transport simulator creates functions over the object's surface representing transfer of arbitrary, low-frequency incident lighting into transferred radiance which includes global effects like shadows and interreflections from the object onto itself. At run-time, these transfer functions are applied to actual incident lighting. Dynamic, local lighting is handled by sampling it close to the object every frame; the object can also be rigidly rotated with respect to the lighting and vice versa. Lighting and transfer functions are represented using low-order spherical harmonics. This avoids aliasing and evaluates efficiently on graphics hardware by reducing the shading integral to a dot product of 9 to 25 element vectors for diffuse receivers. Glossy objects are handled using matrices rather than vectors. We further introduce functions for radiance transfer from a dynamic lighting environment through a preprocessed object to neighboring points in space. These allow soft shadows and caustics from rigidly moving objects to be cast onto arbitrary, dynamic receivers. We demonstrate real-time global lighting effects with this approach. Keywords: Graphics Hardware, Illumination, Monte Carlo Techniques, Rendering, Shadow Algorithms.
University of Delaware at TREC 2015: Combining Opinion Profile Modeling with Complex Context Filtering for Contextual Suggestion

Abstract : In this paper we describe our effort on TREC 2015 Contextual Suggestion Track. Using opinions from online resources to model both user profile and candidate profile has been proven to be effective on previous TREC. This year we also leverage the power of building profile based on opinions. Opinions from well known commercial online resources are collected in order to build the profiles. Two kinds of opinion representations are used for the two submitted runs. Linear interpolation is leveraged to rank the candidate suggestions. Additionally, an advanced context filter which considers all possible factors such as trip type and trip duration is applied to the ranking results so that unwanted venues are removed from the final ranking list. Official results of our submitted runs show the effectiveness of the proposed method.
Abnormal activity detection in video sequences using learnt probability densities

Video surveillance is concerned with identifying abnormal or unusual activity at a scene. In this paper, we develop stochastic models to characterize the normal activities in a scene. Given video sequences of normal activity, probabilistic models are learnt to describe the normal motion in the scene. For any new video sequences, motion trajectories are extracted and evaluated using these learnt probabilistic models to identify if they are abnormal or not. In this paper, we have employed the commonly used prototype based representation to describe the movement of individual objects. The model parameters are estimated in the maximum-likelihood framework.
A new approach to improving end-to-end TCP enhancement schemes over mixed wired/wireless networks

Various transmission control protocol (TCP) enhancement schemes have been proposed to improve TCP performance over mixed wired/wireless networks. Among the different kinds of TCP enhancement schemes, end-to-end TCP enhancement schemes have the lowest implementation complexity and do not violate the semantics of TCP. A general principle of these end-to-end TCP enhancement schemes is to distinguish wireless loss from congestion loss, and to take the cause of the loss into consideration when performing TCP congestion control. However, they do not consider the ratio of congestion losses to wireless losses which significantly affects their performance, mainly in terms of their throughputs. In this paper, a new approach has been developed to address this issue. To apply the approach into an end-to-end TCP enhancement scheme, the ratio of congestion losses to wireless losses are estimated and the parameters used in the scheme are adjusted to further improve its performance. The simulation experiments showed that Veno, a popular end-to-end TCP enhancement scheme, with the new approach always outperformed the original Veno in terms of throughput and fairness, and their differences could be more than two times better. Note that this new approach can generally be applied to many other existing end-to-end TCP enhancement schemes, with minimal implementation costs.
TAN trees for fMRI Brain Image Classification

Functional Magnetic Resonance Imaging, or fMRI, is a 3D brain imaging technique that can be used to observe which portions of the brain are activated by different types of physical or mental stimuli. The technology opens the door to new ways of studying human cognitive processes, in particular, the challenge of finding a predictable pattern in the brain images produced while the human subject is in a certain cognitive state. The historical approach to such a problem has been to keep the human subject in a particular cognitive state during some time interval by repeatedly stimulating them with the same activity (such as reading a variety of words), and then averaging the brain images collected over the interval. However, there is another, machine learning approach to the problem, that allows for the analysis of patterns in individual images without having to average over a time interval. It has been shown that trained machine learning classifiers can be employed to successfully find relevant features in fMRI brain images and link them to specific cognitive states [7]. Examples of classifiers that have already been tried include Gaussian Naive Bayes, Support Vector Machines, and k Nearest Neighbor approaches.
Erasing and Blurring Memories: The Differential Impact of Interference on Separate Aspects of Forgetting

Interference disrupts information processing across many timescales, from immediate perception to memory over short and long durations. The widely held similarity assumption states that as similarity between interfering information and memory contents increases, so too does the degree of impairment. However, information is lost from memory in different ways. For instance, studied content might be erased in an all-or-nothing manner. Alternatively, information may be retained but the precision might be degraded or blurred. Here, we asked whether the similarity of interfering information to memory contents might differentially impact these 2 aspects of forgetting. Observers studied colored images of real-world objects, each followed by a stream of interfering objects. Across 4 experiments, we manipulated the similarity between the studied object and the interfering objects in circular color space. After interference, memory for object color was tested continuously on a color wheel, which in combination with mixture modeling, allowed for estimation of how erasing and blurring differentially contribute to forgetting. In contrast to the similarity assumption, we show that highly dissimilar interfering items caused the greatest increase in random guess responses, suggesting a greater frequency of memory erasure (Experiments 1–3). Moreover, we found that observers were generally able to resist interference from highly similar items, perhaps through surround suppression (Experiments 1 and 4). Finally, we report that interference from items of intermediate similarity tended to blur or decrease memory precision (Experiments 3 and 4). These results reveal that the nature of visual similarity can differentially alter how information is lost from memory.
The implementation of reinforcement learning algorithms on the elevator control system

The machine learning technology has been implemented in control systems successfully especially in the stochastic process. Among the machine learning algorithms, the reinforcement learning algorithm is fairly significant. On the other hand, the elevator, as a facility in daily life, is implemented with several heuristic control methods in the past practice. Simultaneously, the elevator working procedure can be viewed as a stochastic process, so this paper tries to discuss the implementation of several reinforcement learning algorithms on the elevator control system. Through the application of the reinforcement learning algorithms, the elevator system can perform better.
Analysis tools for GRACE and related data sets, theoretical basis

These lecture notes were compiled on the occasion of the summerschool ’Global Hydrological Cycle’, organized by DFG’s priority program SPP1257 ’Mass Transport and Mass Distribution in the System Earth’ at September 12-16, 2011 in Mayschoss/Ahr. Our aim was to familiarize students with different background (geodesy, hydrology, oceanography, geophysics) with some mathematical concepts that are fundamental for analysing level-2 products (sets of spherical harmonic coeﬃcients) from the GRACE mission and related geophysical data sets (model outputs in gridded form). The focus was on concepts, and technical proofs were avoided. Speciﬁc topics were ﬁltering and basin averaging, and the application of the principal component analysis technique.
Segmenting Non-Ideal Irises Using Geodesic Active Contours

The richness and the apparent stability of the iris texture makes it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to isolate the iris structure from the other components in its vicinity, viz., the sclera, pupil, eyelids and eyelashes. Most segmentation models in the literature assume that the pupillary, the limbic and the eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. In this paper, we describe a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions in the image. The performance of an iris recognition system based on multiple Gabor filters is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the WVU and CASIA v1.0 iris databases indicate the efficacy of the proposed technique.
The Java memory model is fatally flawed

The Java memory model described in Chapter 17 of the Java Language Specification gives constraints on how threads interact through memory. This chapter is hard to interpret and poorly understood; it imposes constraints that prohibit common compiler optimizations and are expensive to implement on existing hardware. Most JVMs violate the constraints of the existing Java memory model; conforming to the existing specification would impose significant performance penalties. In addition, programming idioms used by some programmers and used within Sun's Java Development Kit is not guaranteed to be valid according to the existing Java memory model.Furthermore, implementing Java on a shared-memory multiprocessor that implements a weakmemory model poses some implementation challenges not previously considered. Copyright © 2000 John Wiley & Sons, Ltd.
Adaptive inverse disturbance canceling control system based on least square support vector machines

Adaptive inverse disturbance canceling control uses some adaptive filters. The neural network methods of training these filters have been fully researched. However, the problems of local minimum, curse of dimensionality and overfitting limit the application of neural networks. Comparatively, support vector machines effectively overcome these limitations. A kind of adaptive inverse disturbance canceling control system based on least squares support vector machines (LS-SVM) is proposed. The approach of modeling and inverse modeling using LS-SVM is presented. A parameter selecting method within the Bayesian evidence framework is given for SVM regression with Gaussian kernel. Simulation results show that the approach is effective.