Miracle-dz/UniFAR-FTUs huggingface dataset:
WebTraceMiner: a web service for processing and mining EST sequence trace files

Expressed sequence tags (ESTs) remain a dominant approach for characterizing the protein-encoding portions of various genomes. Due to inherent deficiencies, they also present serious challenges for data quality control. Before GenBank submission, EST sequences are typically screened and trimmed of vector and adapter/linker sequences, as well as polyA/T tails. Removal of these sequences presents an obstacle for data validation of error-prone ESTs and impedes data mining of certain functional motifs, whose detection relies on accurate annotation of positional information for polyA tails added posttranscriptionally. As raw DNA sequence information is made increasingly available from public repositories, such as NCBI Trace Archive, new tools will be necessary to reanalyze and mine this data for new information. WebTraceMiner (www.conifergdb.org/software/wtm) was designed as a public sequence processing service for raw EST traces, with a focus on detection and mining of sequence features that help characterize 3′ and 5′ termini of cDNA inserts, including vector fragments, adapter/linker sequences, insert-flanking restriction endonuclease recognition sites and polyA or polyT tails. WebTraceMiner complements other public EST resources and should prove to be a unique tool to facilitate data validation and mining of error-prone ESTs (e.g. discovery of new functional motifs).
Geometrically Consistent Mesh Modification

A new paradigm of adaptivity is to execute refinement, coarsening, and smoothing of meshes on manifolds with incomplete information about their geometry and yet preserve position and curvature accuracy. We refer to this collectively as geometrically consistent (GC) mesh modification. We discuss the concept of discrete GC, show the failure of naive approaches, and propose and analyze a simple algorithm that is GC and accuracy preserving.
Systemic Lupus Erythematosus and Systemic Autoimmune Connective Tissue Disorders behind Recurrent Diastolic Heart Failure

Diastolic heart failure (DHF) remains unexplained in some patients with recurrent admissions after full investigation. A study was directed for screening SLE and systemic autoimmune connective tissue disorders in recurrent unexplained DHF patients admitted at a short-stay and intermediate care unit. It was found that systemic autoimmune conditions explained 11% from all of cases. Therapy also prevented new readmissions. Autoimmunity should be investigated in DHF.
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people’s activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of “smart clothing” system.
Quantum assisted Gaussian process regression

Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.
A Comparative Study on the Performance of Electronic Reservation Services

This study aims to understand how customers perceive the value of the electronic reservation services and how the perception influences their acceptance intention toward the services. Four potential factors (perceived ease of use, usefulness, negativity, and compatibility) are incorporated to examine their impacts on customers' acceptance of the three electronic reservation services (Internet, telephone, and interactive voice response). The comparisons among the three reservation services about their relative position in each influencing factor are also evaluated. Liner regression and ANOVA were employed to analyze data among 140 usable surveys. The results reveal that perceived ease of use, usefulness, negativity, and compatibility are all related to customers' acceptance of the three electronic services. However, perceived negativity turns to insignificant when considering all the variables simultaneously. The results also indicate that telephone is the least and Internet is the most negative tool for making reservations.
Stereo matching algorithm by propagation of correspondences and stereo vision instrumentation

STEREO MATCHING ALGORITHM BY PROPAGATION OF CORRESPONDENCES AND STEREO VISION INSTRUMENTATION by Peerajak Witoonchart A new image processing method is described for measuring the 3-D coordinates of a complex, biological surface. One of the problems in stereo vision is known as the accuracy-precision trade-off problem. This thesis proposes a new method that promises to solve this problem. To do so, two issues are addressed. First, stereo vision instrumentation methods are described. This instrumentation includes a camera system as well as camera calibration, rectification, matching and triangulation. Second, the approach employs an array of cameras that allow accurate computation of the depth map of a surface by propagation of correspondences through pair-wise camera views. The new method proposed in this thesis employs an array of cameras, and preserves the small baseline advantage by finding accurate correspondences in pairs of adjacent cameras. These correspondences are then propagated along the consecutive pairs of cameras in the array until a large baseline is accomplished. The resulting large baseline disparities are then used for triangulation to achieve advantage of precision in depth measurement. The matching is done by an area-based intensity correlation function called Sum of Squared Differences (SSD). In this thesis, the feasibility of using these data for further processing to achieve surface or volume measurements in the future is discussed. STEREO MATCHING ALGORITHM BY PROPAGATION OF CORRESPONDENCES AND STEREO VISION INSTRUMENTATION by Peerajak Witoonchart A Thesis Submitted to the Faculty of New Jersey Institute of Technology In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Engineering Department of Electrical and Computer Engineering
Maximum-Margin Feature Combination for Detection and Categorization

In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which are non-convex and/or generative we propose to use a discriminative model leading to convex problem formulation and complexity control. Furthermore we assert that decision functions should not compare apples and oranges by comparing features of different types directly. Instead we propose to combine different similarity measures for each different feature type. Furthermore we argue that the question: ”Which feature type is more discriminative for task X? ” is ill-posed and show empirically that the answer to this question might depend on the complexity of the decision function.
Working Paper 437 Princeton University Industrial Relations Section April 2000

After documenting the long decline in private sector unionism over the last 50 years, we examine data on NLRB representation elections to determine if changes in the administration of the NLRA during the 1980s reduced the level of organizing activity and success. While organizing activity sharply declined in 1981 (just before President Reagan's showdown with the air tra c controllers' union, PATCO), we nd little evidence that the changes in the administration of the NLRA later in the decade adversely a ected the level of union organizing activity. We then present an accounting framework that decomposes the sharp decline in the private-sector union membership rate into components due to 1) di erential growth rates in employment between the union and nonunion sectors and 2) changes in the union new organization rate (through NLRB-supervised representation elections). We nd that most of the decline in the union membership rate is due to di erential employment growth rates and that changes in union organizing activity had relatively little e ect. Given that the di erential employment growth rates are due largely to broader market and regulatory forces, we conclude that the prospects are dim for a reversal of the downward spiral of labor unions based on increased organizing activity. Farber: Industrial Relations Section, Firestone Library, Princeton University, Princeton, NJ 08544-2098. email: farber@princeton.edu. Western: Department of Sociology, 2-N-2 Green Hall, Princeton University, Princeton NJ 08544-1010. This paper was drafted while Western was a visiting fellow at the Russell Sage Foundation, New York. email: western@princeton.edu. We gratefully acknowledge the contributions of seminar participants at Harvard and Princeton Universities and the Federal Reserve Bank of New York.
Real-time Enhancement of Image and Video Saliency using Semantic Depth of Field

In this paper, we propose a method for automatically directing viewers’ visual attention to important regions of images and videos in low-level vision. Inspired by the modern model of visual attention, the importance map of an input scene is automatically calculated by the combination of low-level features such as intensity and color, which are extracted using spatial filters in different spatial frequencies, together with a set of temporal features extracted using a temporal filter in case of dynamic scenes. A variable-kernel-convolution based on the importance map is then performed on the input scene, in order to make semantic depth of field effects in a way that important regions remain focused while others are blurred. The pipeline of our method is efficient enough to be executed in real time on modern low-end machines, and the associated experiment demonstrates that the proposed system can be complementary to the human visual system.
The Study of Improved CamShift Algorithms for Goal Tacking in Complex Environment

Design an improved CamShift tracking algorithm that based on the greedy algorithm, the algorithm can overcome CamShift algorithm’s defect that has high request to the initial location, which can greatly improve the tracking of moving objects in real time and reliability. The experimental results show that this modified CamShift approach is still effective in tracking the target with the complex circumstances.
Environmental Reconstruction of Tuyoq in the Fifth Century and Its Bearing on Buddhism in Turpan, Xinjiang, China

The Thousand Buddha Grottoes of Tuyoq, Turpan, Xinjiang, China were once a famous Buddhist temple along the ancient Silk Road which was first constructed in the Fifth Century (A.D.). Although archaeological researches about the Grottoes have been undertaken for over a century, the ancient environment has remained enigmatic. Based on seven clay samples from the Grottoes’ adobes, pollen and leaf epidermis were analyzed to decipher the vegetation and climate of Fifth Century Turpan, and the environmental landscape was reconstructed in three dimensions. The results suggest that temperate steppe vegetation dominated the Tuyoq region under a warmer and wetter environment with more moderate seasonality than today, as the ancient mean annual temperature was 15.3°C, the mean annual precipitation was approximately 1000 mm and the temperature difference between coldest and warmest months was 24°C using Co-existence Approach. Taken in the context of wheat and grape cultivation as shown by pollen of Vitis and leaf epidermis of Triticum, we infer that the Tuyoq region was an oasis with booming Buddhism in the Fifth Century, which was probably encouraged by a 1°C warmer temperature with an abundant water supply compared to the coeval world that experienced the 1.4 k BP cooling event.
The effects of anger, sadness and happiness on persuasive message processing: a test of the negative state relief Model

Although scholars have posited that message strength has a weak effect for those in happy moods, and a strong effect on individuals in sad or neutral moods (e.g., Bless, Mackie, & Schwarz, 1992), research findings reveal contradictory results. Moreover, several theoretical observations of this effect have treated emotion as if it exists on a continuum with “negative emotion” on one end, and “positive” emotion on the other end. We sought to further examine the affect of happiness and sadness on persuasion, and to compare these effects with the impact of anger on attitude positivity and persuasive message processing. We predicted if the negative state relief model is the dominant model of emotion, then anger and sadness should “behave” similarly, but if emotions are discrete and unique, anger and sadness should elicit distinct effects. A4 (anger, sadness, happiness, control) X2 (strong message, weak message) independent groups design was employed. Consistent with past research, message strength was positively correlated with attitude, intention and behavior, but was negatively correlated with negative thoughts, and counterarguments. When considering recall, and thought relevance unique interactions emerged dependent on the emotion(s) participants experienced. In general, the negative state relief effect was not supported.
Optimizing Checkpoint Size in the C 3 System

The running times of many computational science applications are much longer than the mean-time-between-failures (MTBF) of current high-performance computing platforms. To run to completion, such applications must tolerate hardware failures. Checkpoint-and-restart (CPR) is the most commonly used scheme for accomplishing this the state of the computation is saved periodically on stable storage, and when a hardware failure is detected, the computation is restarted from the most recently saved state. Most automatic CPR schemes in the literature can be classified as system-level checkpointing schemes because they take core-dump style snapshots of the computational state when all the processes are blocked at global barriers in the program. Unfortunately, a system that implements this style of checkpointing is tied to a particular platform amd cannot optimize the checkpointing process using application-specific knowledge. We are exploring an alternative called automatic applicationlevel checkpointing. In our approach, programs are transformed by a pre-processor so that they become self-checkpointing and self-restartable on any platform. In this paper, we evaluate a mechanism that utilizes application knowledge to minimize the amount of information saved in a checkpoint.
Systematizing Policy Learning

The field of policy learning is characterised by a proliferation of concepts and lack of systematic findings. However, the literature has struggled to move beyond the seminal contributions made more than three decades ago however. We argue that different strands in the literature have failed to communicate because of selection bias and ambiguity about what constitutes evidence of learning in public policy. This conceptual paper suggests a typology wide enough to capture the variation observed by different strands of literature on learning in public policy. We first present a basic model of policy learning based on four categories. The model arises out of an overview of political science studies on learning. We then generate variation within each cell by using rigorous concepts drawn from behavioural research. Specifically, we conceptualize learning as control over the contents and goals of knowledge. By looking at learning through the lenses of knowledge utilization, we show that the basic model can be expanded to reveal sixteen types of learning. These sixteen types are all well-established in the literature, but up until now the scope conditions and connections among types have not been clarified. By providing a comprehensive typology, we aim to lay the foundations for the systematic comparison across and within cases.
Fault-Tolerance in Universal Middleware Bridge

Universal middleware bridge (UMB) provides seamless interoperation among heterogeneous home network middleware. There have been high demands for the UMB components (UMB core and adaptors) to have fault- tolerance capabilities. This paper presents a TMO structuring approach together with new implementation techniques for the fault-tolerant TMO-replica structuring scheme called PSTR. PSTR implementations of UMB components provide fault tolerance capabilities essential in realizing high reliability for the UMB facility.
Cascaded change detection for foreground segmentation

The extraction of relevant objects (foreground) from a background is an important first step in many applications. We propose a technique that tackles this problem using a cascade of change detection tests, including noise-induced, illumination variation and structural changes. An objective comparison of pixel-wise modellingmethods is first presented. Given its best relation performance/complexity, the mixture of Gaussians was chosen to be used in the proposed method to detect structural changes. Experimental results show that the cascade technique consistently outperforms the commonly used mixture of Gaussians, without additional post-processing and without the expense of processing overheads.
A class of quasi-quartic trigonometric bézier curves and surfaces

A new kind of quasi-quartic trigonometric polynomial base functions with a shape parameter λ over the space Ω=span {1, sint, cost, sint2t, cos2t} is presented, and the corresponding quasi-quartic trigonometric Bézier curves and surfaces are defined by the introduced base functions. The quasi-quartic trigonometric Bézier curves inherit most of properties similar to those of quartic Bézier curves, and can be adjusted easily by using the shape parameter λ. With the shape parameter chosen properly, the defined curves can express exactly any plane curves or space curves defined by parametric equation based on {1, sint, cost, sint2t, cos2t} and circular helix with high degree of accuracy without using rational form. The corresponding tensor product surfaces can also represent precisely some quadratic surfaces, such as sphere, paraboloid, cylindrical surfaces, and some complex surfaces. The relationship between quasi-quartic trigonometric Bézier curves and quartic Bézier curves is also discussed, the larger is parameter λ, and the more approach is the quasi-quartic trigonometric Bézier curve to the control polygon. Examples are given to illustrate that the curves and surfaces can be used as an efficient new model for geometric design in the fields of CAGD.
Energy-Efficient Update Protocols for Mobile User Context

Nowadays, rich information about the context of mobile users is directly captured on the users' mobile phones in real-time. Especially, discrete context (e.g., the user's activity) has become highly interesting for many applications since it provides an intuitive and human-understandable description of the user's current state. However, while sensing is executed locally on the mobile device, changes of user context need to be distributed from the device to a large number of interested consumers, e.g., the friends in an online social network. This produces a large overhead for the continuous transmission of context updates and represents a serious challenge for the limited energy budget of battery-equipped mobile devices. In this paper, we propose different strategies for energy-efficient context updates. We present a number of update protocols that are characterized by an inherent trade-off between quality of context and message overhead. To this end, we investigate update criteria that allow consumers of context information to express their tolerance towards the inaccurateness of received context, and we propose update protocols that exploit this tolerance to save updates and, thus, energy. In our evaluation we analyse our update protocols based on a real-world trace of user activities and show that applications can save more than 80% of the messages when tolerating a minor degradation of the context quality only.
Efficient Codebooks for Visual Concept Recognition by ASSOms Activation

In this paper, we propose a novel method for robustly characterizing and classifying visual concepts, and more precisely for detecting several categories of complex objects within images. In order to achieve this aim, we propose a scheme that relies on Adaptive-Subspace SelfOrganizing Maps (ASSOMs). Robust local signatures are first extracted from training object images and projected into specialized ASSOM networks. The extracted local signatures activate several neural maps producing activation energies. These activation energies are then fused into global feature vectors representing the object images. Object recognition is then performed via a supervised SVM (Support Vector Machine) classification. A multi-scale search approach completes the system in order to obtain the object localization and identification in complex scenes. The proposed method allows a good detection rate of 85.08% for the PASCAL 2005 challenge, composed of 689 complex real life images, containing four different objects which vary significantly in terms of shape, size, pose and illumination conditions.
Tracking the History of Knowledge Using Historical Editions of Encyclopedia

Despite the wealth of newly available digital materials, the scope of text-based inv stigations has mostly been limited to either synchronous or short-term historical analysis. In this paper, we report o n the first stage of the project that focuses on tracking long-range historical change, specifically, on the history of ideas and concepts. Th e project’s aim is to map out the history of representation of knowledge in Europe over last three centuries using as a proxy the histor y of changes in historical editions of Encyclopedia Britannica. We describe a series of corpus-analytical tasks necessary for buildin g the analytical and comparative tools for historical analysis using scanned noisy text. In this first stage of the project, we focus specifically on the tools for tracking and visualizing the relative importance of people, interconnections between them, and the rise and fall of their re putations.