Miracle-dz/UniFAR-FTUs huggingface dataset:
Debugging with Software Visualization and Contract Discovery

Despite advances, debugging remains a drudgery, especially for software without proper documentation. Both top-down visualization-based approaches and bottom-up programmatic automation can improve this process. For a given system under test (SUT), we propose a combined method of: (cid:1) Program trace visualization, with capability to zoom in to method call details (inputs and outputs) (cid:1) Interactive declaration of observables for the internal state of the system (cid:1) Statistical and visual analysis of the collected observable data (cid:1) Programmatic declaration of expected behavior of the system defined through invariant relationships and satisfaction of contracts (Design by Contract) on these observables (cid:1) The ability to modify and repeat these steps without restarting the SUT We report that instrumentation necessary to collect trace data is feasible; large amounts of data can be gathered without significant performance penalty while the visualizer remains responsive to tester interaction. Our personal experience is that the system is very quick to set up, faults are discovered quickly, and inefficient algorithms (which may produce correct results) become obvious through the visualizer. We are setting up human-interaction experiments to support the claim that our approach improves the efficiency of discovery of fault origin for a given system failure, compared to using a state-of-the-art debugger for Java (Eclipse).
PaperPoint: a paper-based presentation and interactive paper prototyping tool

Recent developments in digital pen and paper solutions enable, not only the digital capture of handwriting, but also paper to be used as an interactive medium that links to digital information and services. We present a tool that builds on technologies for interactive paper to enable PowerPoint presentations to be controlled from printed slide handouts. Furthermore, slides can be easily annotated during presentations by simply drawing on the printed version of the slide. As well as discussing the advantages of such a paper-based interface and initial findings on its use, we describe how we were also able to exploit it to provide a general prototyping tool for interactive paper applications.
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server

This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems. The algorithm can handle both general convex (possibly non-smooth) regularizers and general convex constraints. When the empirical data loss is strongly convex, we establish linear convergence rate, give explicit expressions for step-size choices that guarantee convergence to the optimum, and bound the associated convergence factors. The expressions have an explicit dependence on the degree of asynchrony and recover classical results under synchronous operation. Simulations and implementations on commercial compute clouds validate our findings.
Automated planning for hydrothermal vent prospecting using AUVs

This thesis presents two families of novel algorithms for automated planning under uncertainty. It focuses on the domain of searching the ocean floor for hydrothermal vents, using autonomous underwater vehicles (AUVs). This is a hard problem because the AUV's sensors cannot directly measure the range or bearing to vents, but instead detecting the plume from a vent indicates the source vent lies somewhere up-current, within a relatively large swathe of the search area. An unknown number of vents may be located anywhere in the search area, giving rise to a problem that is naturally formulated as a partially-observable Markov decision process (POMDP), but with a very large state space (of the order of 10\(^{123}\) states). This size of problem is intractable for current POMDP solvers, so instead heuristic solutions were sought. The problem is one of chemical plume tracing, which can be solved using simple reactive algorithms for a single chemical source, but the potential for multiple sources makes a more principled approach desirable for this domain. This thesis presents several novel planning methods, which all rely on an existing occupancy grid mapping algorithm to infer vent location probabilities from observations. The novel algorithms are information lookahead and expected-entropy-change planners, together with an orienteering problem (OP) correction that can be used with either planner. Information lookahead applies online POMDP methods to the problem, and was found to be effective in locating vents even with small lookahead values. The best of the entropy-based algorithms was one that attempts to maximise the expected change in entropy for all cells along a path, where the path is found using an OP solver. This expected-entropy-change algorithm was at least as effective as the information-lookahead approach, and with slightly better computational efficiency.
Structural basis for the specific recognition of 18S rRNA by APUM23

Abstract PUF (Pumilio/fem-3 mRNA binding factor) proteins, a conserved family of RNA-binding proteins, recognize specific single-strand RNA targets in a specific modular way. Although plants have a greater number of PUF protein members than do animal and fungal systems, they have been the subject of fewer structural and functional investigations. The aim of this study was to elucidate the involvement of APUM23, a nucleolar PUF protein in the plant Arabidopsis, in pre-rRNA processing. APUM23 is distinct from classical PUF family proteins, which are located in the cytoplasm and bind to 3′UTRs of mRNA to modulate mRNA expression and localization. We found that the complete RNA target sequence of APUM23 comprises 11 nt in 18S rRNA at positions 1141–1151. The complex structure shows that APUM23 has 10 PUF repeats; it assembles into a C-shape, with an insertion located within the inner concave surface. We found several different RNA recognition features. A notable structural feature of APUM23 is an insertion in the third PUF repeat that participates in nucleotide recognition and maintains the correct conformation of the target RNA. Our findings elucidate the mechanism for APUM23’s-specific recognition of 18S rRNA.
Memory Systems: Cache, DRAM, Disk

Is your memory hierarchy stopping your microprocessor from performing at the high level it should be? Memory Systems: Cache, DRAM, Disk shows you how to resolve this problem. The book tells you everything you need to know about the logical design and operation, physical design and operation, performance characteristics and resulting design trade-offs, and the energy consumption of modern memory hierarchies. You learn how to to tackle the challenging optimization problems that result from the side-effects that can appear at any point in the entire hierarchy.As a result you will be able to design and emulate the entire memory hierarchy. Understand all levels of the system hierarchy -Xcache, DRAM, and disk. Evaluate the system-level effects of all design choices. Model performance and energy consumption for each component in the memory hierarchy.
Dealing with Noise in EEG Recording and Data Analysis

EEG recording is highly susceptible to various forms and sources of noise, which present significant difficulties and challenges in analysis and interpretation of EEG data. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common sources of noise and review methods for prevention and removal of noise in EEG recording, including elimination of noise sources, signal averaging, data rejection and noise removal, along with their key advantages and challenges. Spoprijemanje s šumom pri zajemanju in analizi EEG signala Author's institution: Department of Psychology, Faculty of Arts, University of Ljubljana, Slovenia. Contact person: Grega Repovš, Oddelek za psihologijo, Univerza v Ljubljani, Aškerčeva 2, SI-1000 Ljubljana. email: grega.repovs@psy.ff. uni-lj.si. Prejeto / Received: 16.08. 2010 Sprejeto / Accepted: 26.08. 2010 Izvleček. EEG signal je zelo občutljiv na raznolike vire in oblike šuma, ki predstavlja pomemben izziv pri analizi in interpretaciji zajetega signala. Za uspešno spoprijemanje s šumom tako v času zajemanja EEG signala kot v okviru priprave podatkov na analizo je na voljo več strategij. Namen prispevka je predstaviti najpogostejše vire šuma ter podati pregled tehnik za njegovo preprečevanje in odstranjevanje, kot so odstranjevanje virov šuma, povprečevanje signala, zavračanje podatkov ter odštevanje šuma. Podane so tudi prednosti in izzivi pri uporabi teh tehnik.  Infor Med Slov: 2010; 15(1): 18-25 Informatica Medica Slovenica 2010; 15(1) 19
Genetic Encoding of Photocaged Tyrosines with Improved Light‐Activation Properties for the Optical Control of Protease Function

We genetically encoded three new caged tyrosine analogues with improved photochemical properties by using an engineered pyrrolysyl‐tRNA synthetase/tRNACUA pair in bacterial and mammalian cells. We applied the new tyrosine analogues to the photoregulation of firefly luciferase by caging its key tyrosine residue, Tyr340, and observed excellent off‐to‐on light switching. This reporter was then used to evaluate the activation rates of the different light‐removable protecting groups in live cells. We identified the nitropiperonyl caging group as an excellent compromise between incorporation efficiency and photoactivation properties. To demonstrate applicability of the new caged tyrosines, an important proteolytic enzyme, tobacco etch virus (TEV) protease, was engineered for optical control. The ability to incorporate differently caged tyrosine analogues into proteins in live cells further expands the unnatural amino acid and optogenetic toolbox.
Intelligent route generation: discovery and search of correlation between shared resources

Sharing information and resources on the Internet has become an important activity for education. The use of ubiquitous devices makes it possible for learning participants to be engaged in an open and connected social environment, and also allows the learning activities to be performed at any time and any place. In this study, the discovery of correlation among shared resources is concentrated. A hypothetical scenario is considered that the information, such as photos and thoughts, is applicable to be shared with implicit context (i.e., geographical information) by learners through a practical implementation, PadSCORM, on a mobile device. Two major contributions are achieved. First, the correlations among resources are determined through usage experiences mining and geographical information adjustment. It then assists learners in filtering out redundant information by highlighting the significance of resources. Second, an intelligent searching algorithm is proposed to visualize adaptive routes to facilitate search process and to enrich the learning activity. The empirical experiments revealing the feasibility and performance (e.g., accuracy and effectiveness) are conducted in the universities in North Taiwan. Copyright © 2012 John Wiley & Sons, Ltd.
Structure and Interactions of the TPR Domain of Sgt2 with Yeast Chaperones and Ybr137wp

Small glutamine-rich tetratricopeptide repeat-containing protein 2 (Sgt2) is a multi-module co-chaperone involved in several protein quality control pathways. The TPR domain of Sgt2 and several other proteins, including SGTA, Hop, and CHIP, is a highly conserved motif known to form transient complexes with molecular chaperones such as Hsp70 and Hsp90. In this work, we present the first high resolution crystal structures of Sgt2_TPR alone and in complex with a C-terminal peptide PTVEEVD from heat shock protein, Ssa1. Using nuclear magnetic resonance spectroscopy and isothermal titration calorimetry, we demonstrate that Sgt2_TPR interacts with peptides corresponding to the C-termini of Ssa1, Hsc82, and Ybr137wp with similar binding modes and affinities.
High-Resolution SNP Microarray Investigation of Copy Number Variations on Chromosome 18 in a Control Cohort

Copy number variations (CNVs) as described in the healthy population are purported to contribute significantly to genetic heterogeneity. Recent studies have described CNVs using lymphoblastoid cell lines or by application of specifically developed algorithms to interrogate previously described data. However, the full extent of CNVs remains unclear. Using high-density SNP array, we have undertaken a comprehensive investigation of chromosome 18 for CNV discovery and characterisation of distribution and association with chromosome architecture. We identified 399 CNVs, of which loss represents 98%, 58% are less than 2.5 kb in size and 71% are intergenic. Intronic deletions account for the majority of copy number changes with gene involvement. Furthermore, one-third of CNVs do not have putative breakpoints within repetitive sequences. We conclude that replicative processes, mediated either by repetitive elements or microhomology, account for the majority of CNVs in the healthy population. Genomic instability involving the formation of a non-B structure is demonstrated in one region.
Robotic vs. Standard Laparoscopic Technique – What is Better?

Introduction: Laparoscopic surgery is subject to certain limitations that can be a problem when performing complex minimally invasive operations. Robotic surgery was developed precisely to overcome such technical limitations. The question therefore arises whether robotic surgery leads to significantly better results compared with standard laparoscopic surgery. Methods: Based on comparative systematic reviews and meta-analyses, this paper examines whether the robotic technique when used for abdominal and visceral surgery procedures confers advantages on the patient compared with the standard laparoscopic technique. Results: Even for demanding visceral surgery procedures, the perioperative complication rate for robotic surgery is not higher than for open or laparoscopic surgical procedures. In cancer cases, the oncological accuracy of robotic resection for gastric, pancreatic, and rectal resection is seen to be adequate. Only the operating time is generally longer than for standard laparoscopic and open procedures. But, on the other hand, in some procedures blood loss is less, conversion rates are lower and hospital stay shorter. Conclusion: To evaluate the future role of the robotic technique for visceral surgery, high-quality prospective randomized trials are urgently needed.
Segmenting a beating heart using polysegment and Spatial GPCA

Given a volume of cardiac MR images, we consider the problem of segmenting the heart based on intensity and dynamics. We first segment the heart and the chest from the background using an algebraic technique for intensity based segmentation called polysegment. As the heart and the chest exhibit different dynamics, we model the image temporal evolution as the output of two different linear dynamical systems. Under this model, the trajectories of the heart and chest intensities lie in different subspaces. We thus propose a method called Spatial GPCA for clustering data points lying in multiple subspaces, while maintaining the spatial coherence of the data points. We compare the segmentation results of Spatial GPCA to those of K-subspaces, another popular subspace clustering algorithm
Polydopamine-clay functionalized Calotropis gigantea fiber: A recyclable oil-absorbing material with large lumens

ABSTRACT A facile route was used for improving the oil-absorbing performance of Calotropis gigantea fiber (CGF) via a convenient oxidative polymerization of dopamine in the presence of attapulgite (APT) and subsequent instant immersion in an ethanolic solution of 1H,1H,2H,2H-perfluorooctyltriethoxysilane (PFS). The scanning electron microscope (SEM) coupled with element mapping indicated that the CGF had been successfully modified by polydopamine–APT–PFS, by which a roughened surface with higher contact angle against water can be formed. Consequently, the resulting fiber showed a significant enhancement in the oil-absorbing capacity, with 99.2 g/g for blend oil, 97.7 g/g for soybean oil, and 88.4 g/g for kerosene, respectively. Compared with previous methods, this approach is convenient for the construction of a novel material with tailored morphology and functionality, and the resulting fiber shows its promising application as a highly efficient and recyclable oil-absorbing material for oily wastewater treatment.
A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information

We present a probabilistic framework for ecient non-negative matrix factorization of discrete (count/binary) data with sideinformation. The side-information is given as a multi-level structure, taxonomy, or ontology, with nodes at each level being categorical-valued observations. For example, when modeling documents with a twolevel side-information (documents being at level-zero), level-one may represent (one or more) authors associated with each document and level-two may represent aliations of each author. The model easily generalizes to more than two levels (or taxonomy/ontology of arbitrary depth). Our model can learn embeddings of entities present at each level in the data/sideinformation hierarchy (e.g., documents, authors, aliations, in the previous example), with appropriate sharing of information across levels. The model also enjoys full local conjugacy, facilitating ecient Gibbs sampling for model inference. Inference cost scales in the number of non-zero entries in the data matrix, which is especially appealing for real-world massive but sparse matrices. We demonstrate the eectiveness of the model on several real-world data sets.
Digital Face Replacement in Photographs

In this paper, we describe a simple method for digitally replacing an individual’s face in a single photograph with that of another person. Our work focuses on the illumination aspect of face replacement, whereby the lighting conditions across a human face in an arbitrary image are extracted and then applied to a target replacement face. We also describe a method to seamlessly merge this newly lit face into the original photograph. The face replacement images are shown to produce convincing results in many cases, even with large variations in skin tones.
On the Mobility of Moderate Speed Users in Ultra Dense Small Cell Deployments with mmW

This paper deals with mobility robustness in a context of ultra-dense deployments operating at the millimeter wave (mmW) band. In order to increase the threshold speed of user equipments (UEs) allowed at the small cell layer, we propose the use of selective Control/User-plane split combined with a non-coherent cooperative multi- point (CoMP) joint transmission (JT). This way, users at moderate speeds can also benefit from the new cell layer and improve their performance with respect to a pure macrocellular operation. The combination of this strategy with a fine tuning of the measurement gap interval allows reducing handover failure rates, which eventually translates into a throughput improvement. On the other hand, increasing the number of measurements leads to a higher UE power consumption, fact that has also been quantified and evaluated.
Neural Network for Heterogeneous Annotations

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multi-view learning, respectively, ﬁnding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank.
Monolayer GeS as a potential candidate for NO2 gas sensors and capturers

The gas sensing properties of single-layer GeS have been studied by performing density functional theory (DFT) calculations, and the present results demonstrate that monolayer GeS exhibits high selectivity and sensitivity toward NO2 molecules and short recovery time, coupled with a strong anisotropic transport feature. Moreover, applying biaxial strains and electric fields could effectively modulate the capture and release of gaseous NO2. Further calculations indicate that oxygen atoms and water molecules decorated on GeS hardly affect the superb selectivity toward NO2 against the other gases. The presence of an S-vacancy on the GeS monolayer greatly promotes the adsorption of NO2, facilitating molecular dissociation. The underlying mechanism is also elaborated at the level of electronic structures. The present findings make GeS monolayers potential materials for NO2 gas sensors and capturers in future applications.