Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Mention Learning for Reading Comprehension with Neural Cascades

Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via truncation or other means), and carefully searching for the answer within that passage. However, in some cases, this strategy can be suboptimal, since by focusing on a specific passage, it becomes difficult to leverage multiple mentions of the same answer throughout the document. In this work, we take a different approach by constructing lightweight models that are combined in a cascade to find the answer. Each submodel consists only of feed-forward networks equipped with an attention mechanism, making it trivially parallelizable. We show that our approach can scale to approximately an order of magnitude larger evidence documents and can aggregate information at the representation level from multiple mentions of each answer candidate across the document. Empirically, our approach achieves state-of-the-art performance on both the Wikipedia and web domains of the TriviaQA dataset, outperforming more complex, recurrent architectures.
A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering.
A practical split manufacturing framework for Trojan prevention via simultaneous wire lifting and cell insertion

Trojans and backdoors inserted by untrusted foundries have become serious threats to hardware security. Split manufacturing is proposed to prevent Trojan insertion proactively. Existing methods depend on wire lifting to hide partial circuit interconnections, which usually suffer from large overhead and lack of security guarantee. In this paper, we propose a novel split manufacturing framework that not only guarantees to achieve the required security level but also allows for a drastic reduction of the introduced overhead. In our framework, insertion of dummy circuit cells and wires is considered simultaneously with wire lifting. To support cell and wire insertion, we propose a new security criterion, and further derive its sufficient condition to avoid computation intensive operations in traditional methods. Then, for the first time, a novel mixed integer linear programming formulation is proposed to simultaneously consider cell and wire insertion together with wire lifting, which significantly enlarges the design space to guarantee the realization of the sufficient condition under the security requirements and overhead constraints. With extensive experimental results, our framework demonstrates much better efficiency, overhead reduction, and security guarantee compared with existing methods.
Characteristic analysis of novel Double-Fork piezoelectric energy harvester

With maturity of micro-electro-mechanical system (MEMS) technology, varieties of electronic products have entered into the era of integration, miniaturization and low power consumption. It is noted that the traditional battery power supply method restricted the development of electronic products. Vibration energy harvesting is one of the most promising solutions. Because the vibration frequency in normal circumstance is about 1–100Hz, the lower resonant frequency of piezoelectric harvester is demanded. Based on the piezoelectric effect, the characteristics of a novel kind of Double-Fork piezoelectric energy harvester (DFPEH) was studied. A model of the DFPEH was built in the finite element analysis software ANSYS, and the corresponding static analysis, modal analysis, harmonic response analysis were made sequentially. Results show that the first three order resonance frequencies of the harvester are 21Hz, 47Hz and 91Hz separately, which are suitable for low-frequency vibration environment. The frequency gap between the first-order resonant frequency and the second-order resonant frequency is 26Hz. Besides, the 3dB bandwidth of the first-order resonant frequency is 8Hz under condition of acceleration value 9.9m/s2 and the damping factor 0.025, which is applied on the fixed end of the DFPEH. Consequently, the DFPEH has shown the distinguished broadband characteristic.
A quantitative structure-activity relationship study on serotonin 5-HT6 receptor ligands: indolyl and piperidinyl sulphonamides

The serotonin 5-HT6 binding affinity of indolyl- and piperidinyl-sulphonamide derivatives has been analysed with topological and molecular features with DRAGON software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 25 descriptors for modelling the activity. The study clearly suggested the role of an average Randic-type eigenvector-based index from adjacency matrix, VRA2, number of secondary aliphatic amines, nNHR, the sum of the topological distance between N and O, T(N···O), ring tertiary carbon atoms, nCrHR, and CH2RX type fragment, C-006, in a molecular structure to optimize the 5-HT6 binding affinities of titled compounds. The PLS analysis confirmed the dominance of information content of CP-MLR identified descriptors for modelling the activity when compared with those of leftover ones.
Increasing Intergroup Distinctiveness

Discrimination is often used to increase public perceptions of group distinctiveness. The current research studied the effectiveness of third party helping as an alternative, more benign strategy to this end. Across four studies, we examined whether helping a third party can position the helping group as more distinct from, or more similar to, a comparison group, depending on the nature of the comparison group’s relationship with the third party. Results from three studies showed that third party helping was as effective as discrimination of the comparison group, but third party helping elicited a more positive public image of the group compared with discrimination. Study 4 provided evidence for the spontaneous use of third party helping in response to distinctiveness threat. These findings extend insights from classic balance theories and research on strategic intergroup helping to the domain of intergroup differentiation, and highlight a benign strategy to achieve positive group distinctiveness.
Numerical simulation of fouling on super-heater tube walls

This thesis describes a detailed investigation into fouling in biomass fired boilers according to fuel mixture, combustion conditions, transportation of particles by the flue gas and the probability of particles impinging and sticking onto heat transfer tubes. The effects of fouling on overall boiler performance and the efficacy of soot blowing are also investigated.Both theoretical simulations and practical experiments on a 157 MW circulating fluidized bed boiler are presented.The deposit thickness on and around a heat exchanger tube is shown to be mainly dependent on the ash particle size, as particles larger than 10 µm (Stokes number larger than 0.1) mainly impinge on the windward side of tubes. The study also shows that fuel containing small amounts of chlorine and zinc – common elements in recycled wood – may cause both higher deposit growth rates and rapid increases in corrosion rates. These elements (chlorine and zinc), together with alkali metals from the biomass have the potential to form sticky compounds that increase the deposit growth rate.Reducing deposits by soot blowing is very effective at removing loose deposits but the hard sintered part of the deposits is almost unaffected. The use of recycled wood has a larger impact on the deposit growth rate than the soot blowing interval.Numerical simulations show that deposits on the superheater tubes redistribute the heat transfer rate from the superheaters to reheater 1 and partially redistribute turbine power from the high pressure turbine to the intermediate pressure turbine
LINEAR AND NONLINEAR VISCOELASTIC ANALYSIS OF THE MICROSTRUCTURE OF ASPHALT CONCRETES

The paper at hand presents a methodology for analyzing the viscoelastic behavior of asphalt concretes, whose microstructure is captured through two-dimensional imaging techniques. The paper describes the viscoelastic behavior of the binder through mechanistic models fitted to rheological data obtained at different strain levels. The resulting binder stress-strain behavior is computed through a convolution integral approach and implemented into a subroutine defining material behavior in a commercially available finite element program. The use of a convolution integral approach is shown to facilitate incorporating the binder nonlinear viscoelatic behavior in the analysis of the asphalt concrete microstructure response. This is conducted by assigning the binder model constants as a function of strain level. The model is tested by comparing asphalt concrete shear modulus G* predictions to earlier predictions obtained with a generalized piecewise linear viscoelastic model, as well as measurements obtained with a simple shear tester. The model is used to explain some of the discrepancies observed between experimentally obtained axial and shear dynamic moduli.
Therapeutic efficacy of the bromodomain inhibitor OTX015/MK-8628 in ALK-positive anaplastic large cell lymphoma: an alternative modality to overcome resistant phenotypes

Anaplastic large cell lymphomas (ALCL) represent a peripheral T-cell lymphoma subgroup, stratified based on the presence or absence of anaplastic lymphoma kinase (ALK) chimeras. Although ALK-positive ALCLs have a more favorable outcome than ALK-negative ALCL, refractory and/or relapsed forms are common and novel treatments are needed. Here we investigated the therapeutic potential of a novel bromodomain inhibitor, OTX015/MK-8628 in ALK-positive ALCLs. The effects of OTX015 on a panel of ALK+ ALCL cell lines was evaluated in terms of proliferation, cell cycle and downstream signaling, including gene expression profiling analyses. Synergy was tested with combination targeted therapies. Bromodomain inhibition with OTX015 led primarily to ALCL cell cycle arrest in a dose-dependent manner, along with downregulation of MYC and its downstream regulated genes. MYC overexpression did not compensate this OTX015-mediated phenotype. Transcriptomic analysis of OTX015-treated ALCL cells identified a gene signature common to various hematologic malignancies treated with bromodomain inhibitors, notably large cell lymphoma. OTX015-modulated genes included transcription factors (E2F2, NFKBIZ, FOS, JUNB, ID1, HOXA5 and HOXC6), members of multiple signaling pathways (ITK, PRKCH, and MKNK2), and histones (clusters 1-3). Combination of OTX015 with the Bruton's tyrosine kinase (BTK) inhibitor ibrutinib led to cell cycle arrest then cell death, and combination with suboptimal doses of the ALK inhibitor CEP28122 caused cell cycle arrest. When OTX015 was associated with GANT61, a selective GLI1/2 inhibitor, C1156Y-resistant ALK ALCL growth was impaired. These findings support OTX015 clinical trials in refractory ALCL in combination with inhibitors of interleukin-2-inducible kinase or SHH/GLI1.
The effect of dabigatran on the activated partial thromboplastin time and thrombin time as determined by the Hemoclot thrombin inhibitor assay in patient plasma samples

Summary Dabigatran is an oral direct thrombin inhibitor that does not require routine laboratory monitoring. However, an assessment of its anticoagulant effect in certain clinical settings is desirable. We examined the relationship between dabigatran levels, as determined by the Hemoclot thrombin inhibitor assay (HTI), the thrombin time (TT) and the activated partial thromboplastin time (aPTT) using different reagents. We describe these parameters with the clinical outcomes of patients receiving dabigatran. Seventy-five plasma samples from 47 patients were analysed. The HTI assay was established to measure dabigatran level. aPTTs were performed using TriniCLOT aPTT S reagent (TC) and three additional aPTT reagents. From linear regression lines, we established the aPTT ranges corresponding to the therapeutic drug levels for dabigatran (90–180 ng/ml). The aPTT demonstrated a modest correlation with the dabigatran level (r= 0.80) but the correlation became less reliable at higher dabigatran levels. The therapeutic aPTT ranges for reagents were clinically and statistically different compared with our reference reagent (46–54 s (TC) vs 51–60 s (SP), 54–64 s (SS) and 61–71 s (Actin FS) (p<0.05)). The TT was sensitive to the presence of dabigatran with a level of 60 ng/ml resulting in a TT > 300 s. In conclusion, the aPTT demonstrated a modest correlation with the dabigatran level and was less responsive with supra-therapeutic levels. aPTT reagents differed in their responsiveness, suggesting individual laboratories must determine their own therapeutic range for their aPTT reagent. The TT is too sensitive to quantify dabigatran levels, but a normal TT suggests minimal or no plasma dabigatran.
Noncoding RNAs and immune checkpoints—clinical implications as cancer therapeutics

A major mechanism of tumor development and progression is silencing of the patient's immune response to cancer‐specific antigens. Defects in the so‐called cancer immunity cycle may occur at any stage of tumor development. Within the tumor microenvironment, aberrant expression of immune checkpoint molecules with activating or inhibitory effects on T lymphocytes induces immune tolerance and cellular immune escape. Targeting immune checkpoint molecules such as programmed cell death protein 1 (PD‐1) and its ligand PD‐L1 with specific antibodies has proven to be a major advance in the treatment of several types of cancer. Another way to therapeutically influence the tumor microenvironment is by modulating the levels of microRNAs (miRNAs), small noncoding RNAs that shuttle bidirectionally between malignant and tumor microenvironmental cells. These small RNA transcripts have two features: (a) their expression is quite specific to distinct tumors, and (b) they are involved in early regulation of immune responses. Consequently, miRNAs may be ideal molecules for use in cancer therapy. Many miRNAs are aberrantly expressed in human cancer cells, opening new opportunities for cancer therapy, but the exact functions of these miRNAs and their interactions with immune checkpoint molecules have yet to be investigated. This review summarizes recently reported findings about miRNAs as modulators of immune checkpoint molecules and their potential application as cancer therapeutics in clinical practice.
Minimally Invasive Spine Surgery and Sagittal Correction.

T he prevalence of adult spinal deformity (ASD) has been reported to range between 5% and 30% in general in the US population and to be as high as 68% in the elderly population. ASD characteristically presents with pain and worsening disability. Operative treatment has been shown to provide significant and superior relief of pain and disability compared with nonoperative treatments. Surgical options include traditional open surgery, minimally invasive approaches, and a hybrid of the 2 techniques. Although traditional open approaches have been shown to be clinically effective, the complication rates associated with these procedures are not insignificant, with reported rates ranging from 10% to . 80%. A recent study by Smith et al in which data from the Spinal Deformity Study Group were analyzed showed that 26% of patients with ASD who underwent surgical correction from centers specializing in these complex operations suffered a minor complication and 16% suffered a major complication. Accordingly, minimally invasive techniques have gained momentum in the management of ASD as spine surgeons attempt to harness the potential benefits of minimally invasive surgery (MIS). MIS techniques for the management of ASD have the potential benefits of preserving muscle mass, reducing soft-tissue dissection, decreasing intraoperative blood loss, and decreasing the physiological stress associated with surgery and duration of hospital stay. Anand et al, in a retrospective review of 71 patients who underwent MIS correction for deformity, reported considerably lower morbidity and complication rates at early and long-term follow-up compared with traditional open approaches. Additionally, the authors reported significantly improved functional outcomes and excellent clinical and radiological outcomes at 2 and 5 years after surgery. Numerous studies have corroborated these findings; however, despite these potential advantages over traditional open approaches, MIS techniques have their own set of challenges and complications. Some of the criticisms leveled against these approaches for the treatment of ASD include but are not limited to the suboptimal degree of sagittal correction, steep learning curve, length of surgery, and high complication rates. In an effort to better understand the relative benefits and limitations of open and minimally invasive approaches in the management of ASD, we conducted this multi-institutional study with the aim of comparing these approaches in the setting of ASD.
CHAM: Action recognition using convolutional hierarchical attention model

Recently, the soft attention mechanism, which was originally proposed in language processing, has been applied in computer vision tasks like image captioning. This paper presents improvements to the soft attention model by combining a con-volutional Long Short-Term Memory (LSTM) with a hierarchical system architecture to recognize action categories in videos. We call this model the Convolutional Hierarchical Attention Model (CHAM). The model applies a convolution-al operation inside the LSTM cell and an attention map generation process to recognize actions. The hierarchical architecture of this model is able to explicitly reason on multi-granularities of action categories. The proposed architecture achieved improved results on three publicly available datasets: the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.
Lessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities

In this paper, we present the concept, content and experience with an actively running Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities. This video-based course is held in German, does not require any programming skills, and serves as an introduction to automatic text analysis. The target audience is anyone who is interested in applying basic language technology to text corpora. It has a strong empirical focus on digital representations, tools and corpus linguistics. The main goal thereby is to grasp the fundamental terminology and concepts of computational linguistics, to understand the main problems and solutions, as well as to know about the performance and limitations of current methods. Furthermore,
Memristive analog arithmetic within cellular arrays

This paper describes how memristors, together with CMOS transistors (CMOS-memristor hybrids), could be used for analog arithmetic within cellular (locally connected, regular) computing arrays, i.e., cellular nanoscale networks (CNN). Elementary analog programming of memristors and copying memristance values are described. Also, we show how memristors could be used for addition, subtraction, multiplication and division. Furthermore, we demonstrate how a local memristive computing unit could be mapped into a CNN cell. Relevant memristor models are shown and key simulations are presented.
Multi-modal joint embedding for fashion product retrieval

Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem, akin to finding a needle in a haystack. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space, which is both efficient and accurate. We train this embedding using large-scale real world e-commerce data by both minimizing the similarity between related products and using auxiliary classification networks to that encourage the embedding to have semantic meaning. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset. We also provide an analysis of the different metadata.
Bainitic Transformation Model in Low Alloy Carbon Steels Considering the Effect of Reaction Constant in JMA Equation

As a part of a study on modeling the microstructural evolution during the welding process, a prediction model of TTT diagram for bainite transformation was studied. This model consisted of a thermodynamic model for the bainite-start(Bs) temperature and a kinetic model for the bainite transformation. A kinetic model was empirically established for low alloy carbon steels, based on Johnson-Mehl-Avrami(JMA) equation. Reaction constants seemed not to have noticeable tendencies for temperatures and were averaged for each alloy. It was, however, found that the mean reaction constant significantly affected the reproducibility for the isothermal kinetics. Therefore, a calibration method to the kinetic parameter was proposed. From calibrations, rate constants were formulated as a function of alloying element and temperature. And TTT diagrams were calculated and compared with experiments.
Community-Acquired Pneumonia in Children: the Challenges of Microbiological Diagnosis

ABSTRACT Community-acquired pneumonia (CAP) is the leading cause of mortality in children under 5 years of age globally. To improve the management of CAP, we must distinguish CAP from other common pediatric conditions and develop better diagnostic methods to detect the causative organism, so as to best direct appropriate resources in both industrialized and developing countries. Here, we review the diagnostic modalities available for identifying viruses and bacteria in the upper and lower respiratory tract of children, with a discussion of their utility and limitations in diagnosing CAP in children.