Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Reliable Remote Memory Pager

Traditional operating systems use magnetic disks as paging devices, even though the cost of a disk transfer measured in processor cycles continues to increase. In this paper we explore the use of remote main memory for paging. We describe the design, implementation and evaluation of a pager that uses main memory of remote workstations as a faster-than-disk paging device and provides reliability in case of single workstation failures. Our pager has been implemented as a block device driver linked to the DEC OSF/1 operating system, without any modifications to the kernel code. Using several test applications we measure the performance of remote memory paging over an Ethernet interconnection network and find it to be faster than traditional disk paging. We evaluate the performance of various reliability policies and prove their feasibility even over low bandwidth networks, like Ethernet. We conclude that the benefits of reliable remote memory paging in workstation clusters are significant today and will probably increase in the near future.
Patch (1) Considered Harmful

Linux is increasingly used to power everything from embedded devices to supercomputers. Developers of such systems often start with a mainline kernel from kernel.org and then apply patches for their application domain. Many of these patches represent crosscutting concerns in that they do not fit within a single program module and are scattered throughout the kernel sources--easily affecting over a hundred files. It requires nontrivial effort to maintain such a crosscutting patch, even across minor kernel upgrades due to the variability of the kernel proper. Moreover, it is a significant challenge to ensure the kernel's correctness when integrating multiple crosscutting concerns. To make matters worse, developers use simple code merging tools that directly manipulate source file lines instead of relying on a lexical, grammatical, or semantic level of abstraction. The result is that patch maintenance is extremely time consuming and error prone. In this paper, we propose a new tool, called c4, designed to help manipulate patches at the level of their abstract syntax and semantics. We believe our approach will simplify the management of OS variations and thereby improve OS evolution.
HOW TO APPROACH DATA ANALYSIS OF TEXTS

Analysis of large text data sets is gaining popularity providing the users some insights into their own (potentially even very unstructured) data sets that where difficult to get using the standard methods. This kind of data analysis difJers from the standard analysis in the following three directions: (1) the used methodsfor data analysis difJer from the standard statistical methods, (2) the data we are analyzing have difJerent characteristics than the standard, structured data bases, and (3) the users of the data analysis results have difJerent needs and requirements than the usual users of common analytical services (statistics, data-mining, OLAP). This paper gives a brief idea of the area addressing that kind of data analysis commonly referred to as Text-Mining. It is agrowing area placed at the intersection of Information-Retrival (IR), Data-Mining (DM), MachineLearning (ML), Natural-Language-Processing (NLP). The problems usually addressed in Text-Mining are topic detection and tracking, document categorization, visualization of document collections, user profil ing, information extraction, construction and updating of hierarchical indices and document collections, intelligent search.
The Adhesion G-Protein-Coupled Receptor, GPR56/ADGRG1, Inhibits Cell–Extracellular Matrix Signaling to Prevent Metastatic Melanoma Growth

Metastatic growth is considered a rate-limiting step in cancer progression, and upregulation of extracellular matrix (ECM) deposition and cell–ECM signaling are major drivers of this process. Mechanisms to reverse ECM upregulation in cancer could potentially facilitate its prevention and treatment but they are poorly understood. We previously reported that the adhesion G-protein-coupled receptor GPR56/ADGRG1 is downregulated in melanoma metastases. Its re-expression inhibited melanoma growth and metastasis and reduced the deposition of fibronectin, a major ECM component. We hypothesize that its effect on fibronectin deposition contributes to its inhibitory role on metastatic growth. To test this, we investigated the function of GPR56 on cell–fibronectin adhesion and its relationship with metastatic growth in melanoma. Our results reveal that GPR56 inhibits melanoma metastatic growth by impeding the expansion of micrometastases to macrometastases. Meanwhile, we present evidence that GPR56 inhibits fibronectin deposition and its downstream signaling, such as phosphorylation of focal adhesion kinase (FAK), during this process. Administration of the FAK inhibitor Y15 perturbed the proliferation of melanoma metastases, supporting a causative link between the cell adhesion defect induced by GPR56 and its inhibition of metastatic growth. Taken together, our results suggest that GPR56 in melanoma metastases inhibits ECM accumulation and adhesion, which contributes to its negative effects on metastatic growth.
Optimal power allocation and relay selection in dual-hop and multi-hop cognitive networks

In this paper, we consider a cognitive relay network which contains a source node, a destination node and a group of network clusters each consisting of several cognitive relay nodes. We choose one relay node with the optimal power value in each cluster to aid the data transmission from source to destination, with the goals of minimizing the total transmitting power and maximizing the network capacity under outage and interference constraints. The research is based on a dual-hop scenario and a multi-hop scenario. The proposed schemes achieve the optimal power allocation and efficient relay selection in both scenarios. The closed-form optimal power allocation and relay selection for a conventional relay network are also listed in this paper.
Discrete Integrals Based on Comonotonic Modularity

It is known that several discrete integrals, including the Choquet and Sugeno integrals, as well as some of their generalizations, are comonotonically modular functions. Based on a recent description of the class of comonotonically modular functions, we axiomatically identify more general families of discrete integrals that are comonotonically modular, including signed Choquet integrals and symmetric signed Choquet integrals, as well as natural extensions of Sugeno integrals.
A rough sets based approach to feature selection

Feature selection techniques aim at reducing the number of unnecessary features in classification rules. The features are measured by their necessity in heuristic feature selection techniques. Rough set theory has been used to define the necessity of features in literature. We propose a new rough set based feature selection approach called Parameterized Average Support Heuristic (PASH). The PASH considers the overall quality of the potential set of rules. It selects features causing high average support of rules over all decision classes. In addition, the PASH arms with parameters that are used to adjust the level of approximation.
Brief summary of the evolution of high-temperature creep-fatigue life prediction models for crack initiation

The evolution of high-temperature, creep-fatigue, life-prediction methods used for cyclic crack initiation is traced from inception in the late 1940's. The methods reviewed are material models as opposed to structural life prediction models. Material life models are used by both structural durability analysts and by material scientists. The latter use micromechanistic models as guidance to improve a material's crack initiation resistance. Nearly one hundred approaches and their variations have been proposed to date. This proliferation poses a problem in deciding which method is most appropriate for a given application. Approaches were identified as being combinations of thirteen different classifications. This review is intended to aid both developers and users of high-temperature fatigue life prediction methods by providing a background from which choices can be made. The need for high-temperature, fatigue-life prediction methods followed immediately on the heels of the development of large, costly, high-technology industrial and aerospace equipment immediately following the second world war. Major advances were made in the design and manufacture of high-temperature, high-pressure boilers and steam turbines, nuclear reactors, high-temperature forming dies, high-performance poppet valves, aeronautical gas turbine engines, reusable rocket engines, etc. These advances could no longer be accomplished simply by trial and error using the 'build-em and bust-em' approach. Development lead times were too great and costs too prohibitive to retain such an approach. Analytic assessments of anticipated performance, cost, and durability were introduced to cut costs and shorten lead times. The analytic tools were quite primitive at first and out of necessity evolved in parallel with hardware development. After forty years more descriptive, more accurate, and more efficient analytic tools are being developed. These include thermal-structural finite element and boundary element analyses, advanced constitutive stress-strain-temperature-time relations, and creep-fatigue-environmental models for crack initiation and propagation. The high-temperature durability methods that have evolved for calculating high-temperature fatigue crack initiation lives of structural engineering materials are addressed. Only a few of the methods were refined to the point of being directly useable in design. Recently, two of the methods were transcribed into computer software for use with personal computers.
Survey on time series motif discovery

Last decades witness a huge growth in medical applications, genetic analysis, and in performance of manufacturing technologies and automatised production systems. A challenging task is to identify and diagnose the behavior of such systems, which aim to produce a product with desired quality. In order to control the state of the systems, various information is gathered from different types of sensors (optical, acoustic, chemical, electric, and thermal). Time series data are a set of real‐valued variables obtained chronologically. Data mining and machine learning help derive meaningful knowledge from time series. Such tasks include clustering, classification, anomaly detection and motif discovery. Motif discovery attempts to find meaningful, new, and unknown knowledge from data. Detection of motifs in a time series is beneficial for, e.g., discovery of rules or specific events in a signal. Motifs provide useful information for the user in order to model or analyze the data. Motif discovery is applied to various areas as telecommunication, medicine, web, motion‐capture, and sensor networks. This contribution provides a review of the existing publications in time series motif discovery along with advantages and disadvantages of existing approaches. Moreover, the research issues and missing points in this field are highlighted. The main objective of this focus article is to serve as a glossary for researchers in this field. WIREs Data Mining Knowl Discov 2017, 7:e1199. doi: 10.1002/widm.1199
The impact of the judicial objective function on the enforcement of environmental standards

We investigate the influence of a judge’s objective function on the type of sanctions used for enforcing environmental standards. We focus on the difference between monetary and non-monetary penalties. We examine the extent to which judges take social costs of sanctions into account when making judgments in court in the context of environmental violations. We also conduct an empirical analysis to test the main findings of the theoretical model using court data from several Belgian jurisdictions. We find that besides minimizing environmental damages judges also take social costs of sanctions into account in their decision-making.
Learning Object Representations from LightingVariationsR

Realistic representation of objects requires models which can synthesize the image of an object under all possible viewing conditions. We propose to learn these models from examples. Methods for learning surface geometry and albedo from one or more images under fixed posed and varying lighting conditions are described. Singular value decomposition (SVD) is used to determine shape, albedo, and lighting conditions up to an unknown 3×3 matrix, which is sufficient for recognition. The use of class-specific knowledge and the integrability constraint to determine this matrix is explored. We show that when the integrability constraint is applied to objects with varying albedo it leads to an ambiguity in depth estimation similar to the bas relief ambiguity. The integrability constraint, however, is useful for resolving ambiguities which arise in current photometric theories. Object Recognition Workshop. ECCV. 1996.
Numerical Modeling of Heat Transfer for Squeeze Casting of MMCs

A finite element model is developed for the process of squeeze casting of metal matrix composites in cylindrical molds. The fluid flow and the heat transfer are fundamental phenomena in squeeze casting. To describe heat transfer in the solidification of molten aluminum, the energy equation written in terms of temperature and enthalpy are applied in an axisymmetric model which is similar to the experimental system. A 1-D flow model simulates the transient metal flow. A direct iteration technique was used to solve the resulting nonlinear algebraic equations, using a computer program to calculate the enthalpy, temperature and fluid velocity. The cooling curves and temperature distribution during infiltration and solidification were calculated for pure aluminum. Experimentally, the temperature was measured and recorded using thermocouple wire. The measured time–temperature data were compared with the calculated cooling curves. The resulting agreement shows that the finite element model can accurately estimate the solidification time and predict the cooling process.
Longitudinal variation in the stress–strain properties of wool fibers

In order to obtain information on possible end-to-end differences in the physical properties of wool caused by weathering of the more exposed portions, a comparison has been made of the stress–strain characteristics of the tip and root halves of fibers taken from the midback region of sheep reared outdoors over a 6-month summer to winter period. The results were further compared with the tip and root halves of fibers taken from animals reared indoors under conditions of essentially continuous darkness. When 11 features of the stress–strain curves were compared, it was found that for the outdoor wools the tip halves had lower initial moduli, higher yield strains, and lower yield moduli than the root halves. In contrast, the dark grown wools showed no end-to-end differences. The greater ease of extension of the tip halves of outdoor wools may be attributed to light-induced diminution in the levels of crosslinking within the keratin structure. The results show that normal environmental influences caused end-to-end differences and that longitudinal variations in physical properties may be a typical characteristic of all field grown wools at the time of harvesting.
Site-selective dopant profiling of p-n junction specimens in the dual-beam FIB/SEM system

Results from site-specific dopant profiling in a dual-beam FIB/SEM system are reported. Si specimens containing p-n junctions were milled using Ga+ ion beam energies ranging from 30 keV to 2 keV, and analysed in situin the vacuum chamber. We compare the dopant contrast observed when milling a cleaved surface to that obtained from a side-wall of a trench cut using 30 kV Ga+ ions, and using successively lower ion beam energies. The latter technique is suitable for site-specific dopant profiling. We find that lower energy ion beam milling significantly improves contrast, but only achieves 50 % of that observed on a freshly-cleaved surface. Furthermore, the contrast on a side-wall previously milled using high energy Ga+ ions is less than that of a cleaved surface subjected to the same ion beam energy.
Interaction of Survival and Death Signaling in Basal Forebrain Neurons: Roles of Neurotrophins and Proneurotrophins

Proneurotrophins bind with high affinity to p75 neurotrophin receptor (p75NTR) and lack the capacity to bind Trk receptors, suggesting that proneurotrophins can elicit apoptosis via p75NTR even in cells expressing survival-promoting Trk receptors. In the CNS, basal forebrain (BF) neurons are particularly vulnerable to degeneration in Alzheimer’s disease, and are among the few populations of brain neurons that express p75NTR throughout life. These neurons also express Trk receptors and may be concomitantly exposed to both proneurotrophins and mature neurotrophins during development, disease, or after injury. We investigated the interaction of mature and proneurotrophin signaling in these CNS neurons. Kainic acid-induced seizures elicited production of pro-NGF by BF astrocytes before caspase activation in p75NTR-positive BF neurons, demonstrating local production of proneurotrophins under pathological conditions and suggesting apoptotic signaling in vivo. Mechanisms of proneurotrophin-induced death were analyzed in cultured BF neurons, and required both p75NTR and its coreceptor sortilin. Surprisingly, exposure to both mature neurotrophins and proneurotrophins demonstrated that Trk phosphorylation did not prevent pro-NGF-induced apoptosis via p75NTR. However, activation of PI3K (phosphatidylinositol 3-kinase)/Akt and MEK (mitogen-activated protein kinase kinase)/Erk pathways prevented pro-NGF-induced apoptosis, revealing a novel critical checkpoint in survival versus apoptotic signaling downstream of Trk activation, and suggesting that pro-NGF blocks survival signaling by preventing Akt and Erk activation. This study shows that proneurotrophins are produced in the brain under pathological conditions, and can elicit apoptosis of BF neurons even when Trk receptors are activated.
Validation of Transcranial Doppler with Magnetic Resonance Angiography in Acute Cerebral Ischemia

Transcranial Doppler (TCD) is often used as a screening tool for detecting intracranial stenosis. Since TCD results may vary among laboratories and sonographers, it requires validation against an imaging modality. We evaluated diagnostic accuracy of TCD performed in our laboratory against time‐of‐flight (TOF) magnetic resonance angiography (MRA) in detecting intracranial stenosis in patients with acute cerebral ischemia.
Genetic and Environmental Determinants of Serum Lipids and Lipoproteins in French Canadian Families

The contribution of genetic and environmental factors In serum trlglycerldes (TG), total cholesterol (CHOL), low density llpoproteln cholesterol (LDL-C), high density llpoproteln cholesterol (HDL-C), and HDL-C/CHOL ratio were studied In 1630 subjects from 375 families of French descent by using a path analysis procedure. Familial correlations were computed In several pairs of biological relatives and relatives by adoption after adjustment for age and gender effects and after further adjustment for physical fitness, level of habitual physical activity, total body fat and fat distribution, diet, smoking, and alcohol consumption. The model of path analysis allowed the separation of transmissible variance (t2) into genetic (h2) and cultural (b2) components of Inheritance. Under the most parsimonious solution and after adjustment for age, gender, and concomitants, the transmissible variance was entirely accounted for by genetic factors (t2=h2), with h2 estimates of 0.52, 0.55, 0.60, 0.62, and 0.63 for TG, CHOL, LDL-C, HDL-C, and HDL-C/CHOL, respectively. These estimates were similar to those obtained after adjustment for age and gender effects only. The contribution of nontransmlsslble environmental factors ranged from 0.48 for TG to 0.37 for HDL-C/CHOL ratio. These results suggest that both genetic and environmental factors contribute to the variation In blood llplds and lipoproteins In this population and that nongenetlc influences are not associated with cultural factors transmitted across generations.
Network localization using angle of arrival

In this paper, we propose two localization methods using angle of arrival (AoA) information. We assume that nodespsila axis orientations are unknown. Therefore, all AoA measurements are employed to calculate the angle differences of two different nodes viewed by the third one. Distance measurements between two nodes within the communication range are also utilized in the first method. For the second method, only AoA information is required. As all distance and angle measurements are accurate enough, the localization problem can be formulated as a linear program (LP). Otherwise, by introducing auxiliary variables, it can be cast as a quadratic program (QP). Simulation examples are presented to illustrate the effectiveness of the proposed methods.
Extractive Summarization Method for Contact Center Dialogues based on Call Logs

This paper proposes a novel extractive summarization method for speech dialogues between agents and customers in contact centers. The proposed method does not require any extra cost for applying the method such as preparing rules or creating training data. Conventional methods such as the tf*idf method, which gives importance to characteristic words in an input text, can miss the essential points for contact center work. Our proposed method evaluates the importance of each utterance from the standpoint of call agents who report calls for managing or analyzing calls. Specifically, the proposed method includes information frequently reported by call agents in summaries using past call logs commonly recorded in the contact center. Evaluation using real data (call dialogues and call logs) shows that the proposed method can extract essential points in terms of contact center work and outperforms the conventional method.
Stop-line detection and localization method for intersection scenarios

This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach that combines 2D detections in grayscale images with stereo-vision based 3D validations in order to increase the robustness and accuracy of the results. Model based reasoning is also used to eliminate false positive situations and to detect exactly the objects of interest (stop-lines, pedestrian or bicycle crossings). The detected horizontal road markings can be used as landmarks for a very accurate longitudinal localization of the ego vehicle within intersection scenarios compensating missing GPS data or its lack of accuracy.