Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches

Machine reading comprehension (MRC) is a long-standing topic in natural language processing (NLP). The MRC task aims to answer a question based on the given context. Recently studies focus on multi-hop MRC which is a more challenging extension of MRC, which to answer a question some disjoint pieces of information across the context are required. Due to the complexity and importance of multi-hop MRC, a large number of studies have been focused on this topic in recent years, therefore, it is necessary and worth reviewing the related literature. This study aims to investigate recent advances in the multi-hop MRC approaches based on 31 studies from 2018 to 2022. In this regard, first, the multi-hop MRC problem definition will be introduced, then 31 models will be reviewed in detail with a strong focus on their multi-hop aspects. They also will be categorized based on their main techniques. Finally, a fine-grain comprehensive comparison of the models and techniques will be presented.
Model-based trajectory stitching for improved behavioural cloning and its applications

Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential decision-making policy from expert demonstrations. However, when the quality of the data is not optimal, the resulting behavioural policy also performs sub-optimally once deployed. Recently, there has been a surge in offline reinforcement learning methods that hold the promise to extract high-quality policies from sub-optimal historical data. A common approach is to perform regularisation during training, encouraging updates during policy evaluation and/or policy improvement to stay close to the underlying data. In this work, we investigate whether an offline approach to improving the quality of the existing data can lead to improved behavioural policies without any changes in the BC algorithm. The proposed data improvement approach - Model-Based Trajectory Stitching (MBTS) - generates new trajectories (sequences of states and actions) by ‘stitching’ pairs of states that were disconnected in the original data and generating their connecting new action. By construction, these new transitions are guaranteed to be highly plausible according to probabilistic models of the environment, and to improve a state-value function. We demonstrate that the iterative process of replacing old trajectories with new ones incrementally improves the underlying behavioural policy. Extensive experimental results show that significant performance gains can be achieved using MBTS over BC policies extracted from the original data. Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining MBTS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).
Abelian combinatorial gauge symmetry

Combinatorial gauge symmetry is a principle that allows us to construct lattice gauge theories with two key and distinguishing properties: a) only one- and two-body interactions are needed; and b) the symmetry is exact rather than emergent in an effective or perturbative limit. The ground state exhibits topological order for a range of parameters. This paper is a generalization of the construction to any finite Abelian group. In addition to the general mathematical construction, we present a physical implementation in superconducting wire arrays, which offers a route to the experimental realization of lattice gauge theories with static Hamiltonians.
Observations of the Velocity Field of NGC 4051

Observations of the velocity field of the Seyfert galaxy NGC 4051 were made using 1.88 m reflector of Okayama Astrophysical Observatory. For 106 H II and nuclear regions in 17 long-slit spectra (P.A. = 110°) which covered almost an entire optical image of - 3 I X 3 I , we measured Ha to present the velocities in tabular form. The emission region is rather annular, and the observed Ha velocities are gradually increasing with distance from the center to a radius of - 90" with no maximum or flat part. Analyses give V h = 697± 11 km s-1 and ¢= 130° ± 14° for the heliocentric systemic velocity and the major axis position angle, respectively. Comparison shows that the Ha velocity structure is consistent with the H I observations of Liszt & Dickey (1995, AJ, 110, 998). However,
Mining Explainable Predictive Features for Water Quality Management

With water quality management processes, identifying and interpreting relationships between features, such as location and weather variable tuples, and water quality variables, such as levels of bacteria, is key to gaining insights and identifying areas where interventions should be made. There is a need for a search process to identify the locations and types of phenomena that are influencing water quality and a need to explain how the quality is being affected and which factors are most relevant. This paper addresses both of these issues. A process is developed for collecting data for features that represent a variety of variables over a spatial region and which are used for training models and inference. An analysis of the performance of the features is undertaken using the models and Shapley values. Shapley values originated in cooperative game theory and can be used to aid in the interpretation of machine learning results. Evaluations are performed using several machine learning algorithms and water quality data from the Dublin Grand Canal basin.
Utility-Based Communication Requirements for Stable Matching in Large Markets

Results from the communication complexity literature have demonstrated that stable matching requires communication: one cannot find or verify a stable match without having access to essentially all of the ordinal preference information held privately by the agents in the market. Stated differently, these results show that stable matching mechanisms are not robust to even a small number of labeled inaccuracies in the input preferences. In practice, these results indicate that agents must go through the time-intensive process of accurately ranking each and every potential match candidate if they wish for the resulting match to be guaranteedly stable. Thus, in large markets, communication requirements for stable matching may be impractically high. A natural question to ask, given this result, is whether some higher-order structure in the market can indicate which large markets have steeper communication requirements. In this paper, we perform such an analysis in a regime where agents have a utility-based notion of preference. We consider a dynamic model where agents only have access to an approximation of their utility that satisfies a universal multiplicative error bound. We apply guarantees from the theoretical computer science literature on low-distortion embeddings of finite metric spaces to understand the communication requirements of stable matching in large markets in terms of their structural properties. Our results show that for a broad family of markets, the error bound may not grow faster than $n^2\log(n)$ while maintaining a deterministic guarantee on the behavior of stable matching mechanisms in the limit. We also show that a stronger probabilistic guarantee may be made so long as the bound grows at most logarithmically in the underlying topological complexity of the market.
Analysis of Drug repurposing Knowledge graphs for Covid-19

Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models. Different models are used to get the node embedding by changing the objective of the model. These embeddings are later used to predict if a candidate drug is effective to treat a disease or how likely it is for a drug to bind to a protein associated to a disease which can be modelled as a link prediction task between two nodes. RESCAL performed the best on the test dataset in terms of MR, MRR and Hits@3.
BEVBert: Multimodal Map Pre-training for Language-guided Navigation

Large-scale pre-training has shown promising results on the vision-and-language navigation (VLN) task. However, most existing pre-training methods employ discrete panoramas to learn visual-textual associations. This requires the model to implicitly correlate incomplete, duplicate observations within the panoramas, which may impair an agent's spatial understanding. Thus, we propose a new map-based pre-training paradigm that is spatial-aware for use in VLN. Concretely, we build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. This hybrid design can balance the demand of VLN for both short-term reasoning and long-term planning. Then, based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal. Extensive experiments demonstrate the effectiveness of the map-based pre-training route for VLN, and the proposed method achieves state-of-the-art on four VLN benchmarks.
Fast and Practical DAG Decomposition with Reachability Applications

We present practical linear and almost linear-time algorithms to compute a chain decomposition of a directed acyclic graph (DAG), G = ( V, E ). The number of vertex-disjoint chains computed is very close to the minimum. The time complexity of our algorithm is O ( | E | + c ∗ l ), where c is the number of path concatenations and l is the length of a longest path of the graph. We give a com-prehensive explanation on factors c and l in the following sections. Our techniques have important applications in many areas, including the design of faster practical transitive closure algorithms. We observe that | E red | ≤ width ∗ | V | ( E red : non-transitive edges) and show how to ﬁnd a substantially large subset of E tr (transitive edges) using a chain decomposition in linear time, without calculating the transitive closure. Our extensive experimental results show the interplay between the width, E red , E tr in various models of graphs. We show how to compute a reachability indexing scheme in O ( k c ∗| E red | ) time, where k c is the number of chains and | E red | is the number of non-transitive edges. This scheme can answer reachabilitiy queries in constant time. The space complexity of the scheme is O ( k c ∗| V | ). The experimental results reveal that our methods are even better in practice than the theoretical bounds imply, indicating how fast chain decomposition algorithms can be applied to the transitive closure problem.
A Distributed Block Chebyshev-Davidson Algorithm for Parallel Spectral Clustering

We develop a distributed Block Chebyshev-Davidson algorithm to solve large-scale leading eigenvalue problems for spectral analysis in spectral clustering. First, the efficiency of the Chebyshev-Davidson algorithm relies on the prior knowledge of the eigenvalue spectrum, which could be expensive to estimate. This issue can be lessened by the analytic spectrum estimation of the Laplacian or normalized Laplacian matrices in spectral clustering, making the proposed algorithm very efficient for spectral clustering. Second, to make the proposed algorithm capable of analyzing big data, a distributed and parallel version has been developed with attractive scalability. The speedup by parallel computing is approximately equivalent to $\sqrt{p}$, where $p$ denotes the number of processes. {Numerical results will be provided to demonstrate its efficiency in spectral clustering and scalability advantage over existing eigensolvers used for spectral clustering in parallel computing environments. }
Online Distributed Algorithm for Optimal Power Flow problem with Regret Analysis

We investigate the distributed DC-Optimal Power Flow (DC-OPF) problem for a dynamic and uncertain environment. The unpredictable supply of renewable resources and varying prices of the electricity market are a few factors responsible for the uncertainty. We propose to address this problem using the framework of online convex optimization, where the cost functions are not known apriori because of the uncertainty and are revealed only incrementally over time. We also consider a distributed setting, where each agent (generators and loads) in the power network is only privy to their own local objectives and constraints but can communicate with their neighbours. A distributed online algorithm is proposed based on the modified primal-dual approach. The performance of the online algorithm is evaluated using the regret (static) function, which is the difference between the actual cost incurred by employing the proposed algorithm and the optimal fixed decision in hindsight. Since we deal with a constrained optimization problem, analogous to the notion of regret the accumulation of the constraint violation is also calculated at each step. We establish a sub-linear bound on the static regret and constraint violation under suitable assumptions on step-size and cost function. Finally, we use the standard IEEE-14 bus system to demonstrate the performance of our algorithm.
Generating Holistic 3D Human Motion from Speech

This work addresses the problem of generating 3D holistic body motions from human speech. Given a speech recording, we synthesize sequences of 3D body poses, hand gestures, and facial expressions that are realistic and diverse. To achieve this, we first build a high-quality dataset of 3D holistic body meshes with synchronous speech. We then define a novel speech-to-motion generation framework in which the face, body, and hands are modeled separately. The separated modeling stems from the fact that face articulation strongly correlates with human speech, while body poses and hand gestures are less correlated. Specifically, we employ an autoencoder for face motions, and a compositional vector-quantized variational autoencoder (VQ- VAE) for the body and hand motions. The compositional VQ-VAE is key to generating diverse results. Additionally, we propose a cross-conditional autoregressive model that generates body poses and hand gestures, leading to coherent and realistic motions. Extensive experiments and user studies demonstrate that our proposed approach achieves state-of-the-art performance both qualitatively and quantitatively. Our dataset and code are released for research purposes at https://talkshow.is. tue.mpg. de/.
The Pan-STARRS1 z > 5.6 Quasar Survey. II. Discovery of 55 Quasars at 5.6 < z < 6.5

The identification of bright quasars at z ≳ 6 enables detailed studies of supermassive black holes, massive galaxies, structure formation, and the state of the intergalactic medium within the first billion years after the Big Bang. We present the spectroscopic confirmation of 55 quasars at redshifts 5.6 < z < 6.5 and UV magnitudes −24.5 < M 1450 < −28.5 identified in the optical Pan-STARRS1 and near-IR VIKING surveys (48 and 7, respectively). Five of these quasars have independently been discovered in other studies. The quasar sample shows an extensive range of physical properties, including 17 objects with weak emission lines, 10 broad absorption line quasars, and 5 objects with strong radio emission (radio-loud quasars). There are also a few notable sources in the sample, including a blazar candidate at z = 6.23, a likely gravitationally lensed quasar at z = 6.41, and a z = 5.84 quasar in the outskirts of the nearby (D ∼ 3 Mpc) spiral galaxy M81. The blazar candidate remains undetected in NOEMA observations of the [C ii] and underlying emission, implying a star formation rate <30–70 M ⊙ yr−1. A significant fraction of the quasars presented here lies at the foundation of the first measurement of the z ∼ 6 quasar luminosity function from Pan-STARRS1 (introduced in a companion paper). These quasars will enable further studies of the high-redshift quasar population with current and future facilities.
Logit Clipping for Robust Learning against Label Noise

In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been shown to be not robust to noisy labels due to its unboundedness . To alleviate this issue, existing works typically design specialized robust losses with the symmetric condition, which usually lead to the underﬁtting issue. In this paper, our key idea is to induce a loss bound at the logit level, thus universally enhancing the noise robustness of existing losses. Speciﬁcally, we propose logit clipping ( LogitClip ), which clamps the norm of the logit vector to ensure that it is upper bounded by a constant. In this manner, CE loss equipped with our LogitClip method is eﬀectively bounded, mitigating the overﬁtting to examples with noisy labels. Moreover, we present theoretical analyses to certify the noise-tolerant ability of LogitClip. Extensive experiments show that LogitClip not only signiﬁcantly improves the noise robustness of CE loss, but also broadly enhances the generalization performance of popular robust losses.
Fruit Quality Assessment with Densely Connected Convolutional Neural Network

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.
Anderson acceleration for a regularized Bingham model

This article studies a finite element discretization of the regularized Bingham equations that describe viscoplastic flow. An efficient nonlinear solver for the discrete model is then proposed and analyzed. The solver is based on Anderson acceleration (AA) applied to a Picard iteration, and we show accelerated convergence of the method by applying AA theory (recently developed by the authors) to the iteration, after showing sufficient smoothness properties of the associated fixed point operator. Numerical tests of spatial convergence are provided, as are results of the model for 2D and 3D driven cavity simulations. For each numerical test, the proposed nonlinear solver is also tested and shown to be very effective and robust with respect to the regularization parameter as it goes to zero.
The missing cone problem and low-pass distortion in optical serial sectioning microscopy

In optical serial sectioning, the 3-D structure of a microscopic specimen is observed by incrementing the focusing plane of a light microscope through the specimen. If the depth of field of the microscope is infinitesimal, the image obtained from each focusing plane is an in-focus slice of the optical density of the specimen. The authors show that the finite aperture of any practical microscope inevitably results in the loss of a biconic region of frequencies in the 3-D Fourier spectrum of the optical density, oriented in the direction of the optical axis. Thus, the resolution along this axis is severely reduced. Outside the missing cone of frequencies, the spectrum is distorted by a strong low-pass effect. A closed form expression is obtained for the overall distortion function using principles of geometric optics, and by assuming that the absorption of the specimen is linear and nondiffractive. Methods for restoring the 3-D images obtained through optical serial sectioning are considered, and several examples are provided. <<ETX>>
Untangling the evolution of body-part terminology in Pano: conservative versus innovative traits in body-part lexicalization

Although language-family specific traits which do not find direct counterparts outside a given language family are usually ignored in quantitative phylogenetic studies, scholars have made ample use of them in qualitative investigations, revealing their potential for identifying language relationships. An example of such a family specific trait are body-part expressions in Pano languages, which are often lexicalized forms, composed of bound roots (also called body-part prefixes in the literature) and non-productive derivative morphemes (called here body-part formatives). We use various statistical methods to demonstrate that whereas body-part roots are generally conservative, body-part formatives exhibit diverse chronologies and are often the result of recent and parallel innovations. In line with this, the phylogenetic structure of body-part roots projects the major branches of the family, while formatives are highly non-tree-like. Beyond its contribution to the phylogenetic analysis of Pano languages, this study provides significative insights into the role of grammatical innovations for language classification, the origin of morphological complexity in the Amazon and the phylogenetic signal of specific grammatical traits in language families.
Diversity, multilingualism and inter-ethnic relations in the long-term history of the Upper Rio Negro region of the Amazon

The Upper Rio Negro regional social system is made up of more than 30 languages belonging to six linguistic families. This results from socio-historical processes stretching back at least two millennia, which have built a system with different levels of autonomy and hierarchy associated with a mythical and ritual complex, and with social and linguistic exchanges. The analysis of these processes require an interdisciplinary outlook to understand the ways in which people from different linguistic families interacted and created it. More specifically, we ask how linguistic and cultural diversity have been created in the context of intense relations of multilingualism and inter-ethnic contact. To this end, we integrate perspectives from historical linguistics (regarding languages from the Tukanoan, Arawakan and Naduhup families) with archaeological data from the Amazonian past. Through this multidisciplinary approach, we seek to develop a linguistic–anthropological understanding of the dynamics shaping the region's diversity and inter-ethnic relations. We show that processes creating diversity are interrelated with changes in social histories, and are especially tied to the establishment of new forms of social organization as a result of pre-colonial inter-ethnic relations. This has led to the construction of various local multilingual ecologies connected to macro-regional processes in Amazonia.