Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust QA using Mixture of Experts

QA systems have become prevalent in everyday life, but the challenge of training effective QA systems to answer questions from out-of-training-domain still exists today. In this work, we explore a Mixture-of-Experts based approach to train a Robust QA system to for out-of-domain QA. We show that a Mixture of Experts based system can indeed offer competitive performance increase (+2.7 validation EM score and +3.71 validation F1 score) over a baseline model on RobustQA tasks where we have little to no knowledge and training over the evaluated datasets. Based on the results, we find that simply having a MoE system recovers most of the gains we were able to achieve, even without having a trained MLP system or hyperparam-tuning
Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection

Feature selection is an important data preprocessing in data mining and machine learning which can be used to reduce the feature dimension without deteriorating model’s performance. Since obtaining annotated data is laborious or even infeasible in many cases, unsupervised feature selection is more practical in reality. Though lots of methods for unsupervised feature selection have been proposed, these methods select features independently, thus it is no guarantee that the group of selected features is optimal. What’s more, the number of selected features must be tuned carefully to obtain a satisfactory result. To tackle these problems, we propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method in this paper, in which a l 2 , 0 -norm regularization term with respect to transformation matrix is imposed in the manifold learning for feature selection, and a graph regularization term is incorporated into the learning model to learn the local geometric structure of data adaptively. An efﬁcient and simple iterative algorithm is designed to solve the proposed optimization problem with the analysis of computational complexity. After optimized, a subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efﬁciency of the proposed method compared with several state-of-the-art approaches.
Statistical methods for Mendelian models with multiple genes and cancers

Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management. Using principles of Mendelian genetics, Bayesian probability theory, and variant‐specific knowledge, Mendelian models derive the probability of carrying a pathogenic variant and developing cancer in the future, based on family history. Existing Mendelian models are widely employed, but are generally limited to specific genes and syndromes. However, the upsurge of multigene panel germline testing has spurred the discovery of many new gene–cancer associations that are not presently accounted for in these models. We have developed PanelPRO, a flexible, efficient Mendelian risk prediction framework that can incorporate an arbitrary number of genes and cancers, overcoming the computational challenges that arise because of the increased model complexity. We implement an 11‐gene, 11‐cancer model, the largest Mendelian model created thus far, based on this framework. Using simulations and a clinical cohort with germline panel testing data, we evaluate model performance, validate the reverse‐compatibility of our approach with existing Mendelian models, and illustrate its usage. Our implementation is freely available for research use in the PanelPRO R package.
Transformer-XL on QAnet

When it was published in 2018, QAnet [1] revolutionized question answering models by replacing traditional LSTM-based models with transformers. Transformers don’t suffer from long-term dependancy information loss like RNNs do, but since they process entire sentences at a time, they can only learn from context within their fixed input window. My aim is to fix this issue by reintroducing an element of recursion into the transformer-based model as described in Transformer-XL [2] and update QAnet to be competitive on SQuAD v2.0. To accomplish this, I 1) redesigned and re-implemented the QAnet structure to include word-character embedding convolutional layers to increase contextual information learned and 2) applied transformer-XL to each encoder block by storing previous hidden states in memory, reusing them in calculations of the next hidden state, and using relative positional embeddings. I found that the word-character convolution worked exceptionally well, and when applied to the baseline BiDAF model were able to achieve EM/F1/AvNA = 59.5/63/69 . The base QAnet was able to perform at similar levels to the baseline, at EM/F1/AvNA = 53.44/56.15/64.31 , but the transformer-XL improvements did not help and in fact made the model worse, clocking in at EM/F1/AvNA = 59.5/63/69 .
ASF-Net: Adaptive Screening Feature Network for Building Footprint Extraction From Remote-Sensing Images

Building footprint extraction plays an important role in many remote-sensing (RS) applications such as urban planning and disaster monitoring. Mainly, the exploitation of contextual information in a fixed receptive field is the focus of previous research, which makes it difficult to generically extract buildings that vary greatly in size and shape, especially when isolated large buildings are surrounded by dense small buildings. To improve this problem, we attempt to teach the network to adjust the receptive field and enhance useful feature information adaptively. In this article, we propose a novel adaptive screening feature network (ASF-Net), which can independently screen and enhance effective feature information from two aspects. On the one hand, we propose a deepened space up-sampling block to screen useful information and help establish boundaries. On the other hand, we propose an Adaptive Information Utilization Block (AIUB) to enlarge the receptive field of feature maps and refine the incomplete building footprint. As a result, the more accurate multiscale building footprint is inferred from the enhanced features. Experimental results on the popular aerial image segmentation datasets show that ASF-Net obtains competitive results [80.2% intersection over union (IoU) on the Inria aerial image labeling dataset and 74.2% IoU on the Massachusetts buildings dataset] in comparison with several state-of-the-art models. The TensorFlow implementation is available at https://github.com/jyx0516/ASF-Net.
A Hierarchical Consensus Attention Network for Feature Matching of Remote Sensing Images

Feature matching, referring to establishing high reliable correspondences between two or more scenes with overlapping regions, is of extremely significance to various remote sensing tasks, such as panorama mosaic and change detection. In this work, we propose an end-to-end deep network for mismatch removal, named hierarchical consensus attention network (HCA-Net), witch is one of the critical steps in matching pipeline. Unlike existing practices, our HCA-Net does not rely on global geometric constraints and handcrafted structural representations. The key principle of the proposed HCA-Net is to adaptively enhance neighborhood consensus before evaluating correspondence. To this end, we design a consensus attention mechanism to regularize sparse matches directly. More specifically, consensus attention consists of two novel operations: an encoder-decoder module for calculating compatibility scores, and a context-based density representation module. Such attention mechanism can be easily plugged into the existing inlier/outlier classification model in a stacked way to reject outliers. And we also propose a hierarchical global-aware network for further improving the accuracy of outlier detection. We compare the proposed HCA-Net with seven state-of-the-art algorithms on several datasets (including various remote sensing images), and the results reveal that our method significantly outperforms the other competitors.
Drug Delivery from Hyaluronic Acid–BDDE Injectable Hydrogels for Antibacterial and Anti-Inflammatory Applications

Hyaluronic acid (HA) injectable biomaterials are currently applied in numerous biomedical areas, beyond their use as dermal fillers. However, bacterial infections and painful inflammations are associated with healthcare complications that can appear after injection, restricting their applicability. Fortunately, HA injectable hydrogels can also serve as drug delivery platforms for the controlled release of bioactive agents with a critical role in the control of certain diseases. Accordingly, herein, HA hydrogels were crosslinked with 1 4-butanediol diglycidyl ether (BDDE) loaded with cefuroxime (CFX), tetracycline (TCN), and amoxicillin (AMX) antibiotics and acetylsalicylic acid (ASA) anti-inflammatory agent in order to promote antibacterial and anti-inflammatory responses. The hydrogels were thoroughly characterized and a clear correlation between the crosslinking grade and the hydrogels’ physicochemical properties was found after rheology, scanning electron microscopy (SEM), thermogravimetry (TGA), and differential scanning calorimetry (DSC) analyses. The biological safety of the hydrogels, expected due to the lack of BDDE residues observed in 1H-NMR spectroscopy, was also corroborated by an exhaustive biocompatibility test. As expected, the in vitro antibacterial and anti-inflammatory activity of the drug-loaded HA-BDDE hydrogels was confirmed against Staphylococcus aureus by significantly decreasing the pro-inflammatory cytokine levels.
Recasting the (Synchrosqueezed) Short-Time Fourier Transform as an Instantaneous Spectrum

In a previous work, we proposed a time-frequency analysis called instantaneous spectral analysis (ISA), which generalizes the notion of the Fourier spectrum and in which instantaneous frequency is utilized to the fullest extent. In this paper, we recast both the Fourier transform (FT) and filterbank (FB) interpretations of the short-time Fourier transform (STFT) as instantaneous spectra. We show that to recast the FB interpretation of STFT as an instantaneous spectrum with valid structure, frequency reassignment is a fundamental necessity, thus demonstrating that this IS is closely related to the synchrosqueezed STFT. This result provides a new theoretical motivation for the synchrosqueezed STFT. Finally, we illustrate through example the instantaneous spectra corresponding to the FT and FB interpretations of STFT using two closed-form examples.
Symbolic Verification of Current-State Opacity of Discrete Event Systems Using Petri Nets

Given a discrete event system, it is said to be opaque if an intruder who can partially observe system behavior cannot infer that the system is necessarily in a predefined secret. A discrete event system is current-state opaque if an intruder is never able to verify whether the current state of the system is within the secret states. This article addresses the verification of current-state opacity of a discrete event system modeled with Petri nets, where the secret is defined as a set of states. The existing methods cannot deal with large-sized systems due to the curse of dimensionality. Multi-valued decision diagrams (MDDs) are widely accepted as an efficient and compact data structure for representing the extremely large reachability sets of Petri nets. We show that MDDs can be used in Petri nets with partial observable transitions and propose an approach for the verification of current-state opacity for bounded Petri nets. It is shown by experimental studies that the symbolic approach is practically more efficient than traditional techniques such as basis reachability graphs (BRGs).
Minimal Contact Robotic Stroke Rehabilitation on Risk of COVID-19, Work Efficiency and Sensorimotor Function

Patients with hemiparetic stroke undergo direct, labor-intensive hands-on conventional physical therapy to improve sensorimotor function, spasticity, balance, trunk stability, and activities of daily living (ADLs). Currently, direct, intensive hands-on therapeutic modalities have increased concerns during the coronavirus (COVID-19) global pandemic. We developed an innovative Walkbot to mitigate the issues surrounding conventional hands-on physical therapy. We aimed to compare the effects of minimal-contact robotic rehabilitation (MRR) and full-contact conventional rehabilitation (FCR) on static and dynamic balance, trunk stability, ADLs, spasticity, and cognition changes in patients with hemiparetic stroke. A total of 64 patients with hemiparetic stroke (mean age = 66.38 ± 13.17; 27 women) underwent either MRR or FCR three times/week for 6 weeks. Clinical outcome measurements included the Trunk Impairment Scale (TIS), the Berg Balance Scale (BBS), the modified Ashworth Scale (MAS), the Fugl—Meyer Assessment (FMA), and the modified Barthel Index (MBI) scores. A 2 × 2 repeated analysis of variance (ANOVA) was performed, and an independent t-test was used to determine statistical differences in the physiotherapists’ work efficiency and COVID-19 transmission risk. The ANOVA showed that MRR had effects superior to those of FCR on the TIS, the BBS, the FMA, and the MBI (p < 0.05), but not on the MAS (p = 0.230). MRR showed a greater decrease on the physiotherapist’s work efficiency and COVID-19 transmission risk (p < 0.05). Our results provide clinical evidence that robot-assisted locomotor training helps maximize the recovery of sensorimotor function, abnormal synergy, balance, ADLs, and trunk stability, and facilitates a safer environment and less labor demand than conventional stroke rehabilitation.
Compact and single-feed circularly polarised microstrip antenna with wide beamwidth and axial-ratio beamwidth

A novel method to realise a single-feed circularly polarised (CP) microstrip antenna with wide beamwidth and axial-ratio (AR) beamwidth is investigated. Two diagonal metal walls are put at the diagonal sides of the microstrip antenna. The diagonal metal walls can excite the orthogonal radiation fields with the same amplitude and 90° phase difference on the radiating patch for CP with wide beamwidth and AR beamwidth. Some parameters which effect on the performances of the antenna are studied. The simulated and measured results of the reflection coefficients, radiation patterns and AR radiation patterns are presented. The 3-dB beamwidths of the right-hand CP (RHCP) radiation patterns in the xoz -plane and the yoz -plane are 105° and 157°, respectively. The 3-dB AR beamwidths of the RHCP radiation patterns in the xoz -plane and yoz -plane are 100° and 155°, respectively. Good CP characteristics of the new structure antenna have been obtained in the whole operating frequency band.
Deeper Understanding of Ternary Eutectic Carbonates/Ceria-Based Oxide Composite Electrolyte through Thermal Cycling

Due to a high conductivity of about 0.1 S·cm−1, Li-Na-K carbonate eutectic and Sm-doped ceria composite material is a good electrolyte candidate for hybrid fuel cells operating between 500 °C and 600 °C. The present paper aims at a deeper understanding of the species and mechanisms involved in the ionic transport through impedance spectroscopy and thermal analyses, in oxidizing and reducing atmospheres, wet and dry, and during two heating/cooling cycles. Complementary structural analyses of post-mortem phases allowed us to evidence the irreversible partial transformation of molten carbonates into hydrogenated species, when water and/or hydrogen are added in the surrounding atmospheres. Furthermore, this modification was avoided by adding CO2 in anodic and/or cathodic compartments. Finally, a mechanistic model of such composite electrical behavior is suggested, according to the surrounding atmospheres used. It leads to the conclusions that cells based on this kind of electrolyte would preferably operate in molten carbonate fuel cell conditions, than in solid oxide fuel cell conditions, and confirms the name of “Hybrid Fuel Cells” instead of Intermediate Temperature (or even Low Temperature) Solid Oxide Fuel Cells.
Theoretical Investigation for Angle Measurement Based on Femtosecond Maker Fringe

This paper proposes to utilize a femtosecond Maker fringe for angular measurement to expand the measurement range by using the characteristic of the multiple visible peaks in the Maker fringe. Four different z-cut nonlinear materials and three different polarization combinations of SHG were considered in the study, and various theoretical results are calculated for both the intensity-based angle measurement and the frequency-domain angle measurement. As a result, the p-s polarization shows a significant angle dependence in the range of ±20° compared with the other polarization. In addition, the BBO and KDP are superior to the other investigated materials because of the relatively higher sensitivity and visibility. The refractive index difference was introduced in this paper, and it was applied to explain the angle measurement performance of the Maker fringe successfully.
Diagnostic Test to Identify Parkinson's Disease from the Blood Sera of Chinese Population: A Cross-Sectional Study

Background Parkinson's disease (PD) is a neurodegenerative disease, a hallmark by the formation of misfolded and aggregated α-synuclein proteins. The expression of potential microRNA (miRNA) candidates isolated from serum and cerebrospinal fluid (CSF) exosomes of PD patients was assessed for their diagnostic value and their potential role as biomarkers for PD was explored. In this study, we characterize the expression level of miRNAs in the exosomes of blood sera and cerebrospinal fluid and explore their potential role as biomarkers for PD. Materials and Methods A total of 209 patients having an onset of PD, along with 60 neurodegenerative (ND) disorders and 50 healthy controls were enrolled. Blood samples and CSF samples were collected and exosomes were isolated. The isolated exosomes were characterized using CD63 detection and exosomal RNA was extracted. Serum miRNA profiling was carried out by synthesizing cDNA from the purified RNA and miRNA transcripts were determined by qRT-PCR using SYBR Green PremixScript. microRNA profiling strategy was employed for extracting the exosomal miRNAs from the exosomes. Results Five common miRNAs viz. miR-151a-5p, miR-24, mir-485-5p, mir-331-5p, and mir-214 were found to be upregulated with statistical significance in both the serum exosome and CSF exosomes. The investigation revealed that serum and CSF exosomal miRNA molecules are definitive biomarkers for PD with proper specificity and sensitivity. Conclusions The significant level of miR-151a-5p, miR-24, mir-485-5p, mir-331-5p, and mir-214 was observed in the serum and CSF which may be established as a biomarker for the diagnosis of PD.
Level-Set-Based Shape Optimization on Soft Magnetic Composites With Isotropy Constraint

Soft magnetic composites (SMCs) demonstrate excellent potentials for electromagnetic applications. Isotropy is one critical feature in designing the composite. This work presents a shape optimization algorithm for SMCs subject to isotropy and volume constraints. The maximal isotropic effective permeability is the design target for a fixed volume fraction of the SMCs’ inclusion. Augmented Lagrange is applied to satisfy the volume requirement. In addition, an adaptive weighted sum approach is used to deal with the isotropy constraint. A level-set function is defined on the whole design domain and propagates the shape of the inclusion. Shape derivative is deduced to determine the descent direction. A numerical application is implemented for SMCs in magnetostatics in 2-D for various initial shapes. The local minimum is located at a rapid convergence rate.
Transparent cultures: Imagined users and the politics of software design (1975-2012)

The rapid pace of software’s development poses serious challenges for any cultural history of computing. While digital media studies often sidestep historicism, this project asserts that computing’s messy, and often hidden, history can be studied using digital tools built to adapt text-mining strategies to the textuality of source code. My project examines the emergence of personal computing, a platform underlying much of digital media studies but that itself has received little attention outside of corporate histories. Using an archive of technical papers, professional journals, popular magazines, and science fiction, I trace the origin of design strategies that led to a largely instrumentalist view of personal computing and elevated “transparent design” to a privileged status. I then apply text-mining tools that I built with this historical context in mind to study source code critically, including those features of applications hidden by transparent design strategies. This project’s first three chapters examine how and why strategies of information hiding shaped consumer software design from the 1980s on. In Chapter 1, I analyze technical literature from the 1970s and 80s to show how cognitive psychologists and computer engineers developed an ideal of transparency that discouraged users from accessing information structures underlying personal computers. Prior to the early 1980s, software designers assumed that users were at least semi-literature in the technical details of computer systems. In response to the expanding popular interest in personal computing, engineers abandoned this assumption and began to design hardware and software for users with little or no understanding of computer systems. Successful software design became defined by the invisibility of computation. Users were made to feel that they were directly manipulating their data. Intimidating command prompts were hidden, and the complex operations performed through them automated behind simpler, visual metaphors like
A Hierarchical Noise Control System Using Adaptable Tuned Vibration Absorbers

A novel noise control system is developed using adaptable tuned vibration absorbers (ATVA) to interact with a vibrating host structure in such a way as to reduce radiated acoustic energy. ATVA’s are single-degree-of-freedom resonant devices that can change their resonant frequency and damping over a range. This ATVA noise control system is targeted at applications with tonal disturbances such as propeller aircraft. The motivation for this work is to better understand and experimentally demonstrate the noise control performance of globally detuned vibration absorbers (i.e. tuned away from the disturbance) compared to that of perfectly tuned devices on complex structures. A two-tier hierarchical control approach is used where a global control algorithm attempts to minimize a global parameter such as radiated acoustic energy by directing the adaptation of subordinate ATVA’s. The global control algorithm uses an adaptive simplex search algorithm that requires no initial knowledge of the structure or the ATVA’s. The ATVA’s also require no model of the structure, each utilizing only the local vibration of its own mass and control gains set by the global controller. Noise control using a single ATVA is first studied on a small simply supported plate. Then, a multiple ATVA system is tested on a large plate structure at several test frequencies where many structural modes participate. Noise reductions up to 22 dB are achieved at locations in the radiated field. Further, it is found in some cases, classic tuning of the ATVA results in increased structural noise radiation. ATVA’s are realized by outfitting typical inertial (proof-mass) actuators with a classical feedback loop. The device’s resonant frequency and damping can be controlled independently, yet simultaneously via two control gains. The ATVA’s are designed, built, and characterized for their adaptable domain and power requirements. A cohesive analytical model of the ATVA is also developed and used to compliment the experimental results.