Miracle-dz/UniFAR-FTUs huggingface dataset:
A logic-based resampling with matching approach to multiple imputation of missing data

Researchers often use model-based multiple imputation to handle missing at random data to minimize bias while making the best use of all available data. However, there are sometimes constraints within the data that make model-based imputation difficult and may result in implausible values. In these contexts, we explore the advantages of employing a logic-based resampling with matching (RWM) approach for multiple imputation. This approach is similar to random hot deck imputation and allows for more plausible imputations when model-based approaches are not feasible. We illustrate RWM imputation for missing pain, activity frequency, and sport data using The Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-DK). We match records with missing data to several observed records, generate probabilities for matched records using observed data, and sample from these records based on the probability of each occurring. Because imputed values are generated randomly, multiple complete datasets can be created. They are then analyzed and averaged in the same way as model-based multiple imputation. This approach can be extended to other datasets as an alternative when model-based approaches are infeasible, specifically where there are constraints between covariates.
The quantum query complexity of composition with a relation

The negative weight adversary method, $\mathrm{ADV}^\pm(g)$, is known to characterize the bounded-error quantum query complexity of any Boolean function $g$, and also obeys a perfect composition theorem $\mathrm{ADV}^\pm(f \circ g^n) = \mathrm{ADV}^\pm(f) \mathrm{ADV}^\pm(g)$. Belovs gave a modified version of the negative weight adversary method, $\mathrm{ADV}_{rel}^\pm(f)$, that characterizes the bounded-error quantum query complexity of a relation $f \subseteq \{0,1\}^n \times [K]$, provided the relation is efficiently verifiable. A relation is efficiently verifiable if $\mathrm{ADV}^\pm(f_a) = o(\mathrm{ADV}_{rel}^\pm(f))$ for every $a \in [K]$, where $f_a$ is the Boolean function defined as $f_a(x) = 1$ if and only if $(x,a) \in f$. In this note we show a perfect composition theorem for the composition of a relation $f$ with a Boolean function $g$ \[ \mathrm{ADV}_{rel}^\pm(f \circ g^n) = \mathrm{ADV}_{rel}^\pm(f) \mathrm{ADV}^\pm(g) \enspace . \] For an efficiently verifiable relation $f$ this means $Q(f \circ g^n) = \Theta( \mathrm{ADV}_{rel}^\pm(f) \mathrm{ADV}^\pm(g) )$.
Cascade Neural Ensemble for Identifying Scientifically Sound Articles

Background: A significant barrier to conducting systematic reviews and meta-analysis is efficiently finding scientifically sound relevant articles. Typically, less than 1% of articles match this requirement which leads to a highly imbalanced task. Although feature-engineered and early neural networks models were studied for this task, there is an opportunity to improve the results. Methods: We framed the problem of filtering articles as a classification task, and trained and tested several ensemble architectures of SciBERT, a variant of BERT pre-trained on scientific articles, on a manually annotated dataset of about 50K articles from MEDLINE. Since scientifically sound articles are identified through a multi-step process we proposed a novel cascade ensemble analogous to the selection process. We compared the performance of the cascade ensemble with a single integrated model and other types of ensembles as well as with results from previous studies. Results: The cascade ensemble architecture achieved 0.7505 F measure, an impressive 49.1% error rate reduction, compared to a CNN model that was previously proposed and evaluated on a selected subset of the 50K articles. On the full dataset, the cascade ensemble achieved 0.7639 F measure, resulting in an error rate reduction of 19.7% compared to the best performance reported in a previous study that used the full dataset. Conclusion: Pre-trained contextual encoder neural networks (e.g. SciBERT) perform better than the models studied previously and manually created search filters in filtering for scientifically sound relevant articles. The superior performance achieved by the cascade ensemble is a significant result that generalizes beyond this task and the dataset, and is analogous to query optimization in IR and databases.
Equivariant calculus on $\mu$-character and $\mu$K-stability of polarized schemes

We introduce and study $\mu$K-stability of polarized schemes with respect to general test configurations as an algebro-geometric aspect of $\mu$-cscK metric we introduced in the last paper. There are two fundamental ingeredients. On one hand, we develop a framework on `derivative of relative equivariant intersection', which we name equivariant calculus. As a part of equivariant calculus, we establish convergence results on some infinite series of equivariant cohomology classes given by relative equivariant intersection, based on a basic observation on the Cartan model of equivariant cohomology. On the other hand, we derive an equivariant character $\bm{\mu}^\lambda$ called $\mu$-character for equivariant family of polarized schemes from the equivariant intersection formula of the log of the $\mu$-volume functional. The derivative of the $\mu$-character not even yields $\mu$-Futaki invariant, but also produces an analogue of the equivariant first Chern class of CM line bundle, which is irrational in our general $\mu$K-stability setup. The product invites us to approach the compactification problem on the moduli space of Fano manifolds with Kahler--Ricci solitons.
Author Name Disambiguation in Bibliographic Databases: A Survey

Entity resolution is a challenging and hot research area in the field of Information Systems since last decade. Author Name Disambiguation (AND) in Bibliographic Databases (BD) like DBLP , Citeseer , and Scopus is a specialized field of entity resolution. Given many citations of underlying authors, the AND task is to find which citations belong to the same author. In this survey, we start with three basic AND problems, followed by need for solution and challenges. A generic, five-step framework is provided for handling AND issues. These steps are; (1) Preparation of dataset (2) Selection of publication attributes (3) Selection of similarity metrics (4) Selection of models and (5) Clustering Performance evaluation. Categorization and elaboration of similarity metrics and methods are also provided. Finally, future directions and recommendations are given for this dynamic area of research.
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence

Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.
Modelling and docking of Indian SARS-CoV-2 spike protein 1 with ACE2: implications for co-morbidity and therapeutic intervention

Presently, India bears amongst the highest burden of non-communicable diseases such as diabetes mellitus (DM), hypertension (HT), and cardio vascular disease (CVD) and thus represents a vulnerable target to the SARS-CoV-2/COVID-19 pandemic. Involvement of the angiotensin converting enzyme 2 (ACE2) in susceptibility to infection and pathogenesis by SARS-CoV-2 is currently an actively pursued research area. An increased susceptibility to infection in individuals with DM, HT and CVD together with higher levels of circulating ACE2 in these settings presents a scenario where interaction with soluble ACE2 may result in disseminated virus-receptor complexes that could enhance virus acquisition and pathogenesis. Thus, understanding the SARS-CoV-2 receptor binding domain-ACE2 interaction, both membrane bound and in the cell free context may contribute to elucidating the role of co-morbidities in increased susceptibility to infection and pathogenesis. Both Azithromycin and Hydroxychloroquine (HCQ) have shown efficacy in mitigating viral carriage in infected individuals. Furthermore, each of these compounds generate active metabolites which in turn may also modulate virus-receptor interaction and thus influence clinical outcomes. In this study, we model the structural interaction of S1 with both full-length and soluble ACE2. Additionally, therapeutic drugs and their active metabolites were docked with soluble ACE2 protein. Our results show that S1 from either of the reported Indian sequences can bind both full-length and soluble ACE2, albeit with varying affinity that can be attributed to a reported substitution in the RBD. Furthermore, both Azythromycin and HCQ together with their active metabolites can allosterically affect, to a range of extents, binding of S1 to ACE2.
A Detailed Account of The First Question Generation Shared Task Evaluation Challenge

The paper provides a detailed account of the First Shared Task Evaluation Challenge on Question Generation that took place in 2010. The campaign included two tasks that take text as input and produce text, i.e. questions, as output: Task A – Question Generation from Paragraphs and Task B – Question Generation from Sentences. Motivation, data sets, evaluation criteria, guidelines for judges, and results are presented for the two tasks. Lessons learned and advice for future Question Generation Shared Task Evaluation Challenges (QG-STEC) are also offered.
Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations

Methods that find insightful low-dimensional projections are essential to effectively explore high-dimensional data. Principal Component Analysis is used pervasively to find low-dimensional projections, not only because it is straightforward to use, but it is also often effective, because the variance in data is often dominated by relevant structure. However, even if the projections highlight real structure in the data, not all structure is interesting to every user. If a user is already aware of, or not interested in the dominant structure, Principal Component Analysis is less effective for finding interesting components. We introduce a new method called Subjectively Interesting Component Analysis (SICA), designed to find data projections that are subjectively interesting, i.e, projections that truly surprise the end-user. It is rooted in information theory and employs an explicit model of a user's prior expectations about the data. The corresponding optimization problem is a simple eigenvalue problem, and the result is a trade-off between explained variance and novelty. We present five case studies on synthetic data, images, time-series, and spatial data, to illustrate how SICA enables users to find (subjectively) interesting projections.
Multi-Source Attention for Unsupervised Domain Adaptation

We model source-selection in multi-source Unsupervised Domain Adaptation (UDA) as an attention-learning problem, where we learn attention over the sources per given target instance. We first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn domain-attention scores over the sources for aggregating the predictions of the source-specific models. Experimental results on two cross-domain sentiment classification datasets show that the proposed method reports consistently good performance across domains, and at times outperforming more complex prior proposals. Moreover, the computed domain-attention scores enable us to find explanations for the predictions made by the proposed method.
System-level modelling and design space exploration for multiprocessor embedded system-on-chip architectures

Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other hand, they still need to show high (sometimes real-time) performance, and often support multiple applications and standards which requires high programmability. This wide spectrum of design requirements leads to complex heterogeneous System-on-Chip (SoC) architectures -- consisting of several types of processors from fully programmable microprocessors to configurable processing cores and customized hardware components, integrated on a single chip. This study targets such multiprocessor embedded systems and strives to develop algorithms, methods, and tools to deal with a number of fundamental problems which are encountered by the system designers during the early design stages.
Data-driven crowd analysis in videos

In this work we present a new crowd analysis algorithm powered by behavior priors that are learned on a large database of crowd videos gathered from the Internet. The algorithm works by first learning a set of crowd behavior priors off-line. During testing, crowd patches are matched to the database and behavior priors are transferred. We adhere to the insight that despite the fact that the entire space of possible crowd behaviors is infinite, the space of distinguishable crowd motion patterns may not be all that large. For many individuals in a crowd, we are able to find analogous crowd patches in our database which contain similar patterns of behavior that can effectively act as priors to constrain the difficult task of tracking an individual in a crowd. Our algorithm is data-driven and, unlike some crowd characterization methods, does not require us to have seen the test video beforehand. It performs like state-of-the-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individual behaves in an unusual way.
A Multimodal Human-Computer Interaction Framework for Research into Crisis Management

Unreliable communication networks, chaotic environments and stressful conditions can make communication during crisis events difficult. The current practice in crisis management can be improved by introducing ICT systems in the process. However, much experimentation is needed to determine where and how ICT can aid. Therefore, we propose a framework in which predefined modules can be connected in an ad hoc fashion. Such a framework allows for rapid development and evaluation of such ICT systems. The framework offers recognition of various communication modalities including speech, lip movement, facial expression, handwriting and drawing, body gesture, text and visual symbols. It provides mechanisms to fuse these modalities into a context dependent interpretation of the current situation and generate appropriate the multimodal information responses. The proposed toolbox can be used as part of a disaster and rescue simulation. We propose evaluation methods, and focus on the technological aspects of our framework.
Convolutive Bottleneck Network with Dropout for Dysarthric Speech Recognition

In this paper, we investigate the recognition of speech produced by a person with an articulation disorder resulting from athetoid cerebral palsy. The articulation of the first spoken words tends to become unstable due to strain on speech muscles, and that causes degradation of speech recognition. Therefore, we propose a robust feature extraction method using a convolutive bottleneck network (CBN) instead of the well-known MFCC. The CBN stacks multiple various types of layers, such as a convolution layer, a subsampling layer, and a bottleneck layer, forming a deep network. Applying the CBN to feature extraction for dysarthric speech, we expect that the CBN will reduce the influence of the unstable speaking style caused by the athetoid symptoms. Furthermore, we also adopt dropout in the output layer since automatically assigned labels to the dysarthric speech are usually unreliable due to ambiguous phonemes uttered by the person with speech disorders. We confirmed its effectiveness through word recognition experiments, where the CNN based feature extraction method outperformed the conventional feature extraction method.
Round-Efficient Distributed Byzantine Computation

We present the first round efficient algorithms for several fundamental distributed tasks in the presence of a Byzantine edge. Our algorithms work in the CONGEST model of distributed computing. In the \emph{Byzantine Broadcast} problem, given is a network $G=(V,E)$ with an unknown Byzantine edge $e'$. There is a source node $s$ holding an initial message $m_0$, and the goal is for all the nodes in the network to receive a copy of $m_0$, while ignoring all other messages. Perhaps surprisingly, to the best of our knowledge, all existing algorithms for the problem either assume that the Byzantine behavior is probabilistic, use polynomially large messages or else suffer from a large round complexity. We give an $\widetilde{O}(D^2)$-round \footnote{The notion $\widetilde{O}$ hides poly-logarithmic terms, and the notion $\widehat{O}$ hides a multiplicative factor of an $2^{O(\sqrt{\log n})}$ term. } algorithm for the Byzantine Broadcast problem, where $D$ is the diameter of the graph. The communication graph is required to be $3$-edge connected, which is known to be a necessary condition. We also provide a Leader Election algorithm in the presence of a Byzantine edge with the same round complexity of $\widetilde{O}(D^2)$ rounds. We use these algorithms to provide the efficient construction of \emph{Byzantine cycle covers} which serve the basis for (i) Byzantine BFS algorithms and (ii) a general compiler for algorithms in the presence of a Byzantine edge. We hope that the tools provided in this paper will pave the way towards obtaining \textbf{round-efficient algorithms} for many more distributed problems in the presence of Byzantine edges and nodes.
Degree of Schedulability of Mixed-Criticality Real-Time Systems with Probabilistic Sporadic Tasks

We present the concept of degree of schedulability for mixed-criticality scheduling systems. This concept is given in terms of the two factors 1) Percentage of Missed Deadlines (PoMD), and 2) Degradation of the Quality of Service (DoQoS). The novel aspect is that we consider task arrival patterns that follow user-defined continuous probability distributions. We determine the degree of schedulability of a single scheduling component which can contain both periodic and sporadic tasks using statistical model checking in the form of UPPAAL SMC. We support uniform, exponential, Gaussian and any user-defined probability distribution.
SSRNet: Scalable 3D Surface Reconstruction Network

Existing learning-based surface reconstruction methods from point clouds are still facing challenges in terms of scalability and preservation of details on large-scale point clouds. In this paper, we propose the SSRNet, a novel scalable learning-based method for surface reconstruction. The proposed SSRNet constructs local geometry-aware features for octree vertices and designs a scalable reconstruction pipeline, which not only greatly enhances the predication accuracy of the relative position between the vertices and the implicit surface facilitating the surface reconstruction quality, but also allows dividing the point cloud and octree vertices and processing different parts in parallel for superior scalability on large-scale point clouds with millions of points. Moreover, SSRNet demonstrates outstanding generalization capability and only needs several surface data for training, much less than other learning-based reconstruction methods, which can effectively avoid overfitting. The trained model of SSRNet on one dataset can be directly used on other datasets with superior performance. Finally, the time consumption with SSRNet on a large-scale point cloud is acceptable and competitive. To our knowledge, the proposed SSRNet is the first to really bring a convincing solution to the scalability issue of the learning-based surface reconstruction methods, and is an important step to make learning-based methods competitive with respect to geometry processing methods on real-world and challenging data. Experiments show that our method achieves a breakthrough in scalability and quality compared with state-of-the-art learning-based methods.
Efficient Suspected Infected Crowds Detection Based on Spatio-Temporal Trajectories

Virus transmission from person to person is an emergency event facing the global public. Early detection and isolation of potentially susceptible crowds can effectively control the epidemic of its disease. Existing metrics can not correctly address the infected rate on trajectories. To solve this problem, we propose a novel spatio-temporal infected rate (IR) measure based on human moving trajectories that can adequately describe the risk of being infected by a given query trajectory of a patient. Then, we manage source data through an efficient spatio-temporal index to make our system more scalable, and can quickly query susceptible crowds from massive trajectories. Besides, we design several pruning strategies that can effectively reduce calculations. Further, we design a spatial first time (SFT) index, which enables us to quickly query multiple trajectories without much I/O consumption and data redundancy. The performance of the solutions is demonstrated in experiments based on real and synthetic trajectory datasets that have shown the effectiveness and efficiency of our solutions.