Miracle-dz/UniFAR-FTUs huggingface dataset:
ExQuisiTe: Explaining Quantities in Text

Web pages and other documents often contain tables to provide numerical details in a structured manner. Typically, the text explains and highlights important quantities, often using approximate numbers and aggregates such as totals, averages or ratios. For a human reader, it is crucial to navigate between text and tables to understand the key information in its context, drill down into tables when details are needed, and obtain explanations on specific figures from the accompanying text. In this demo, we present ExQuisiTe: a system to align quantity mentions in the text with related quantity mentions in tables, and enable extractive summarization that considers table contents. ExQuisiTe links quantity mentions in the text with quantities in tables, to support user-friendly exploration. ExQuisiTe handles exact single-cell references as well as rounded or truncated numbers and aggregations such as row or column totals.
Modeling multiple interactions with a Markov random field in query expansion for session search

How to automatically understand and answer users' questions (eg, queries issued to a search engine) expressed with natural language has become an important yet difficult problem across the research fields of information retrieval and artificial intelligence. In a typical interactive Web search scenario, namely, session search, to obtain relevant information, the user usually interacts with the search engine for several rounds in the forms of, eg, query reformulations, clicks, and skips. These interactions are usually mixed and intertwined with each other in a complex way. For the ideal goal, an intelligent search engine can be seen as an artificial intelligence agent that is able to infer what information the user needs from these interactions. However, there still exists a big gap between the current state of the art and this goal. In this paper, in order to bridge the gap, we propose a Markov random field–based approach to capture dependence relations among interactions, queries, and clicked documents for automatic query expansion (as a way of inferring the information needs of the user). An extensive empirical evaluation is conducted on large‐scale web search data sets, and the results demonstrate the effectiveness of our proposed models.
Unsupervised Topic Extraction from Privacy Policies

This paper suggests the use of automatic topic modeling for large-scale corpora of privacy policies using unsupervised learning techniques. The advantages of using unsupervised learning for this task are numerous. The primary advantages include the ability to analyze any new corpus with a fraction of the effort required by supervised learning, the ability to study changes in topics of interest along time, and the ability to identify finer-grained topics of interest in these privacy policies. Based on general principles of document analysis we synthesize a cohesive framework for privacy policy topic modeling and apply it over a corpus of 4,982 privacy policies of mobile applications crawled from the Google Play Store. The results demonstrate that even with this relatively moderate-size corpus quite comprehensive insights can be attained regarding the focus and scope of current privacy policy documents. The topics extracted, their structure and the applicability of the unsupervised approach for that matter are validated through an extensive comparison to similar findings reported in prior work that uses supervised learning (which heavily depends on manual annotation of experts). The comparison suggests a substantial overlap between the topics found and those reported in prior work, and also unveils some new topics of interest.
METTL3/m6A/miRNA-873-5p Attenuated Oxidative Stress and Apoptosis in Colistin-Induced Kidney Injury by Modulating Keap1/Nrf2 Pathway

Nephrotoxicity of colistin is the major factor limiting its clinical application. However, the exact mechanism of colistin-induced nephrotoxicity is still elusive. N6-Methyladenosine (m6A) modification has been implicated in many biological processes, however, its role in colistin-induced nephrotoxicity needs to be elucidated. Mouse renal tubular epithelial cells (mRTECs) were treated with 200 μM colistin with or without METTL3 overexpression. Cells injury, m6A assay, oxidative stress and apoptosis were examined. Levels of m6A are decreased after colistin treatment in mRTECs. METTL3 is the major factor involved in abnormal m6A modification. METTL3 overexpression plays a protective role against colistin-induced oxidative stress and apoptosis. Moreover, METTL3 interacts with the microprocessor protein DGCR8 and positively modulates miR-873-5p mature process in an m6A-dependent manner. Further experiments show that miR-873-5p could regulate Keap1-Nrf2 pathway against colistin-induced oxidative stress and apoptosis. These studies revealed an important role of METTL3/m6A in colistin-induced nephrotoxicity and provide a new insight on m6A modification in drug induced toxicity.
Supervised Classification of Geriatric Anxiety

Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly. As a result, we showed that classification is possible without using all standard GAI questionnaires.
Cheese Manufacturing and Bioactive Substance Separation: Separation and Preliminary Purification of cAMP from Whey

Cheese consumption has been gradually increased in China. However, both the manufacturing process of cheese and the utilization of its main by-product were not well developed. Based on the sensory evaluation, Box-Behnken Design (BBD) was performed in the present study to optimize the cheese processing, which was proved more suitable for Chinese. The optimal parameters were: rennet 0.052 g/L, start culture 0.025 g/L and CaCl2 0.1 g/L. The composition analysis of fresh bovine milk and whey showed that whey contained most of the soluble nutrients of milk, which indicated that whey was a potential resource of cyclic adenosine-3’, 5’-monophosphate (cAMP). Thus, the cAMP was isolated from whey, the results of high-performance liquid chromatography (HPLC) analysis showed that the macroporous adsorption resins (MAR) D290 could increase the concentration of cAMP from 0.058 µmol/mL to 0.095 µmol/mL. We firstly purified the cAMP from the whey, which could become a new source of cAMP.
Efficient Data Supply for Parallel Heterogeneous Architectures

Decoupling techniques have been proposed to reduce the amount of memory latency exposed to high-performance accelerators as they fetch data. Although decoupled access-execute (DAE) and more recent decoupled data supply approaches offer promising single-threaded performance improvements, little work has considered how to extend them into parallel scenarios. This article explores the opportunities and challenges of designing parallel, high-performance, resource-efficient decoupled data supply systems. We propose Mercury, a parallel decoupled data supply system that utilizes thread-level parallelism for high-throughput data supply with good portability attributes. Additionally, we introduce some microarchitectural improvements for data supply units to efficiently handle long-latency indirect loads.
Why and How to Increase SSD Performance Transparency

Even on modern SSDs, I/O scheduling is a first-order performance concern. However, it is unclear how best to optimize I/O patterns for SSDs, because a complex layer of proprietary firmware hides many principal aspects of performance, as well as SSD lifetime. Losing this information leads to research papers drawing incorrect conclusions about prototype systems, as well as real-world systems realizing sub-optimal performance and lifetime. It is our position that a useful performance model of a foundational system component is essential, and the community should support efforts to construct models of SSD performance. We show examples from the literature and our own measurements that illustrate serious limitations of current SSD modeling tools and disk statistics. We observe an opportunity to resolve this problem by reverse engineering SSDs, leveraging recent trends toward component standardization within SSDs. This paper presents a feasibility study and initial results to reverse engineer a commercial SSD's firmware, and discusses limitations and open problems.
Spectral Approximate Inference

Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterative approximation algorithms exploring certain local structure/consistency of GM have been investigated as popular choices in practice. However, due to their local/iterative nature, they often output poor approximations or even do not converge, e.g., in low-temperature regimes (hard instances of large parameters). To overcome the limitation, we propose a novel approach utilizing the global spectral feature of GM. Our contribution is two-fold: (a) we first propose a fully polynomial-time approximation scheme (FPTAS) for approximating the partition function of GM associating with a low-rank coupling matrix; (b) for general high-rank GMs, we design a spectral mean-field scheme utilizing (a) as a subroutine, where it approximates a high-rank GM into a product of rank-1 GMs for an efficient approximation of the partition function. The proposed algorithm is more robust in its running time and accuracy than prior methods, i.e., neither suffers from the convergence issue nor depends on hard local structures, as demonstrated in our experiments.
Identification of Cyanobacterial Strains with Potential for the Treatment of Obesity-Related Co-Morbidities by Bioactivity, Toxicity Evaluation and Metabolite Profiling

Obesity is a complex disease resulting in several metabolic co-morbidities and is increasing at epidemic rates. The marine environment is an interesting resource of novel compounds and in particular cyanobacteria are well known for their capacity to produce novel secondary metabolites. In this work, we explored the potential of cyanobacteria for the production of compounds with relevant activities towards metabolic diseases using a blend of target-based, phenotypic and zebrafish assays as whole small animal models. A total of 46 cyanobacterial strains were grown and biomass fractionated, yielding in total 263 fractions. Bioactivities related to metabolic function were tested in different in vitro and in vivo models. Studying adipogenic and thermogenic gene expression in brown adipocytes, lipid metabolism and glucose uptake in hepatocytes, as well as lipid metabolism in zebrafish larvae, we identified 66 (25%) active fractions. This together with metabolite profiling and the evaluation of toxicity allowed the identification of 18 (7%) fractions with promising bioactivity towards different aspects of metabolic disease. Among those, we identified several known compounds, such as eryloside T, leptosin F, pheophorbide A, phaeophytin A, chlorophyll A, present as minor peaks. Those compounds were previously not described to have bioactivities in metabolic regulation, and both known or unknown compounds could be responsible for such effects. In summary, we find that cyanobacteria hold a huge repertoire of molecules with specific bioactivities towards metabolic diseases, which needs to be explored in the future.
Cooperative Hierarchical Framework for Group Activity Recognition: From Group Detection to Multi-activity Recognition

Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.
A Multi-Vocal Review of Security Orchestration

Organizations use diverse types of security solutions to prevent cyber-attacks. Multiple vendors provide security solutions developed using heterogeneous technologies and paradigms. Hence, it is a challenging rather impossible to easily make security solutions to work an integrated fashion. Security orchestration aims at smoothly integrating multivendor security tools that can effectively and efficiently interoperate to support security staff of a Security Operation Centre (SOC). Given the increasing role and importance of security orchestration, there has been an increasing amount of literature on different aspects of security orchestration solutions. However, there has been no effort to systematically review and analyze the reported solutions. We report a Multivocal Literature Review that has systematically selected and reviewed both academic and grey (blogs, web pages, white papers) literature on different aspects of security orchestration published from January 2007 until July 2017. The review has enabled us to provide a working definition of security orchestration and classify the main functionalities of security orchestration into three main areas—unification, orchestration, and automation. We have also identified the core components of a security orchestration platform and categorized the drivers of security orchestration based on technical and socio-technical aspects. We also provide a taxonomy of security orchestration based on the execution environment, automation strategy, deployment type, mode of task and resource type. This review has helped us to reveal several areas of further research and development in security orchestration.
CPR for SSDs

Modern storage systems are built upon the assumption that the capacity of a storage device does not change. This capacity-invariant interface forces a flash-based storage device to trade performance for reliability when, in fact, it can maintain both if a graceful reduction in capacity were to be allowed. We argue that relaxing the fixed capacity abstraction of the storage device allows for a better capacity-performance-reliability (CPR) tradeoff. We then outline existing device-internal mechanisms for building a capacity-variant flash device, and describe the necessary changes in the storage stack.
The relational processing limits of classic and contemporary neural network models of language processing

ABSTRACT Whether neural networks can capture relational knowledge is a matter of long-standing controversy. Recently, some researchers have argued that (1) classic connectionist models can handle relational structure and (2) the success of deep learning approaches to natural language processing suggests that structured representations are unnecessary to model human language. We tested the Story Gestalt model, a classic connectionist model of text comprehension, and a Sequence-to-Sequence with Attention model, a modern deep learning architecture for natural language processing. Both models were trained to answer questions about stories based on abstract thematic roles. Two simulations varied the statistical structure of new stories while keeping their relational structure intact. The performance of each model fell below chance at least under one manipulation. We argue that both models fail our tests because they can't perform dynamic binding. These results cast doubts on the suitability of traditional neural networks for explaining relational reasoning and language processing phenomena.
Finite Automata, Probabilistic Method, and Occurrence Enumeration of a Pattern in Words and Permutations

The main theme of this paper is the enumeration of the order-isomorphic occurrence of a pattern in words and permutations. We mainly focus on asymptotic properties of the sequence f r v ( k , n ) , the number of n-array k-ary words that contain a given pattern v exactly r times. In addition, we study the asymptotic behavior of the random variable Xn , the number of pattern occurrences in a random n-array word. The two topics are closely related through the identity P ( X n = r ) = 1 k n f r v ( k , n ) . In particular, we show that for any r ≥ 0, the Stanley-Wilf sequence ( f r v ( k , n ) ) 1 ∕ n converges to a limit independent of r, and determine the value of the limit. We then obtain several limit theorems for the distribution of Xn , including a central limit theorem, large deviation estimates, and the exact growth rate of the entropy of Xn . Furthermore, we introduce a concept of weak avoidance and link it to a certain family of non-product measures on words that penalize pattern occurrences but do not forbid them entirely. We analyze this family of probability measures in a small parameter regime, where the distributions can be understood as a perturbation of a uniform measure. Finally, we extend some of our results for words, including the one regarding the equivalence of the limits of the Stanley-Wilf sequences, to pattern occurrences in permutations.
Extracting Meaningful Insights on City and Zone Levels Utilizing US Open Government Data

It is estimated that merely 4% of the world's population reside on US soil. Remarkably, 43% of the entire population of prominent websites are hosted in the United States (Fig. 1). Even though most data content on the Web is unstructured, the US government has had big contributions in producing and actively releasing structured datasets related to different fields such as health, education, safety and finance. Aforementioned datasets are referred to as Open Government Data (OGD) and are aimed at increasing the structured data pool in conjunction with promoting government transparency and accountability. In this paper, we present a new system “OGDXplor” which processes raw OGD through a well-defined procedure leveraging machine learning algorithms and produces meaningful insights. The novelty of this work is encompassed by the collective approach utilized in developing the system and tackling challenges. First by addressing arising challenges due to data being collected and aggregated from heterogeneous sources that otherwise would have been impossible to acquire as a comprehensive unit. moreover, classification and comparisons are drawn on a much finer level that we refer to as zone level. Zones are the areas encompassed and defined by zip codes and are seldomly used in classifying and extracting insights as presented here. OGDXplor facilitates comparing and classifying zones located in different cities or zones within an individual city. The system is presented to end-users as a web application allowing users to elect zones and features relevant to their use case. Results are presented in both chart and map formats which aids the decision-making process.
Effects of K channel inhibitors on potassium transport in bovine adrenal chromaffin granules

The Ca2+‐independent K+ selective channel in the membrane of adrenal gland chromaffin granules has earlier been identified. In the present report we describe new properties of this potassium channel using 86Rb+, a K+ analogue, flux measurements. The studies are performed in membrane vesicles prepared from chromaffin granules. The electrogenic 86Rb+ transport is inhibited by quinacrine, barium, zinc and magnesium. The effects of other potassium channel blockers on 86Rb+ transport into chromaffin granules are also reported.
Dynamic Linked Data: A SPARQL Event Processing Architecture

This paper presents a decentralized Web-based architecture designed to support the development of distributed, dynamic, context-aware and interoperable services and applications. The architecture enables the detection and notification of changes over the Web of Data by means of a content-based publish-subscribe mechanism where the W3C SPARQL 1.1 Update and Query languages are fully supported and used respectively by publishers and subscribers. The architecture is built on top of the W3C SPARQL 1.1 Protocol and introduces the SPARQL 1.1 Secure Event protocol and the SPARQL 1.1 Subscribe Language as a means for conveying and expressing subscription requests and notifications. The reference implementation of the architecture offers to developers a design pattern for a modular, scalable and effective application development.
Near-Optimal Path Planning for Complex Robotic Inspection Tasks

In this paper, we consider the problem of generating inspection paths for robots. These paths should allow an attached measurement device to perform high-quality measurements. We formally show that generating robot paths, while maximizing the inspection quality, naturally corresponds to the submodular orienteering problem. Traditional methods that are able to generate solutions with mathematical guarantees do not scale to real-world problems. In this work, we propose a method that is able to generate near-optimal solutions for real-world complex problems. We experimentally test this method in a wide variety of inspection problems and show that it nearly always outperforms traditional methods. We furthermore show that the near-optimality of our approach makes it more robust to changing the inspection problem, and is thus more general.
β-cell compensation concomitant with adaptive ER stress and β-cell neogenesis in a diet-induced type 2 diabetes model.

Insulin-secreting pancreatic β-cells adapt to obesity-related insulin resistance via increases in insulin secretion and β-cell mass. Failed β-cell compensation predicts the onset of type 2 diabetes (T2D). However, the mechanisms of β-cell compensation are not fully understood. Our previous study reported changes in β-cell mass during the progression of T2D in the Nile rat (NR) fed standard chow. In the present study, we measured other β-cell adaptive responses including glucose metabolism and β-cell insulin secretion in NRs at different ages, thus characterizing NR at 2 months as a model of β-cell compensation followed by decompensation at 6 months. We observed increased proinsulin secretion in the transition from compensation to decompensation, indicative of impaired insulin processing. Subsequently, we compared adaptive unfolded protein response (UPR) in β-cells and demonstrated a positive role of ER chaperones in insulin secretion. In addition, the incidence of insulin-positive neogenic but not proliferative cells increased during the compensation phase, suggesting non-proliferative β-cell growth as a mechanism of β-cell mass adaptation. In contrast, decreased neogenesis and β-cell dedifferentiation were observed in β-cell dysfunction. Furthermore, the progression of T2D and pathophysiological changes of β-cells were prevented by increasing fibre content of the diet. • Our study characterized a novel model for β-cell compensation with adaptive responses in cell function and mass • The temporal association of adaptive ER chaperones with blood insulin and glucose suggests upregulated chaperone capacity as an adaptive mechanism • β-cell neogenesis but not proliferation contributes to β-cell mass adaptation.