Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns, pace, and processes of water‐quality variability in a long‐studied estuary

Environmental time series have rich information content that is invaluable for measuring and understanding changes over time and guiding policies to manage change. I extracted information from measurements of 10 water‐quality constituents in upper San Francisco Bay from 1975 to 2016, one of the longest observational records in a U.S. estuary. Changes were detected at every time scale captured by monthly sampling. Long‐term trends included increased ammonium (+53%), nitrate + nitrate (+50%), silicate (+14%), Secchi depth (+42%), and decreased chlorophyll a (Chl a) (−74%) and suspended particulate matter (−45%). Changes at the decadal scale included abrupt shifts (Chl a, nitrate + nitrite) and oscillations between shorter trends of increase and decrease (Secchi depth, phosphate). Long‐term trends were not expressed equally across all seasons, and seasonal patterns of change varied across constituents. These examples illustrate key features of environmental variability at the land–sea interface: (1) water‐quality components change continually at time scales from months to decades; (2) patterns of seasonal, multiyear, and multidecadal change are complex and vary across constituents; (3) primary drivers of change are freshwater inflow, the master regulator of estuarine dynamics, and human activities such as river damming, water diversions, wastewater discharge, environmental policies, and species introductions; (4) extracting the full information content of time series requires multiple analyses, each revealing a different layer of insight into how changes develop over time; (5) water‐quality variability is nonstationary, so future changes cannot be forecast reliably; (6) repeated observation is an essential method of Earth system science with applications in the design and performance measures of environmental policies.
Mobile Manipulator for Autonomous Localization, Grasping and Precise Placement of Construction Material in a Semi-Structured Environment

Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditions alike. Finally, we present means to extend the perceptive radius of the vehicle by using it in cooperation with an autonomous drone, which provides aerial reconnaissance. Performance of the proposed system has been evaluated in a series of experiments conducted in real-world conditions.
Gradient Episodic Memory with a Soft Constraint for Continual Learning

Catastrophic forgetting in continual learning is a common destructive phenomenon in gradient-based neural networks that learn sequential tasks, and it is much different from forgetting in humans, who can learn and accumulate knowledge throughout their whole lives. Catastrophic forgetting is the fatal shortcoming of a large decrease in performance on previous tasks when the model is learning a novel task. To alleviate this problem, the model should have the capacity to learn new knowledge and preserve learned knowledge. We propose an average gradient episodic memory (A-GEM) with a soft constraint $\epsilon \in [0, 1]$, which is a balance factor between learning new knowledge and preserving learned knowledge; our method is called gradient episodic memory with a soft constraint $\epsilon$ ($\epsilon$-SOFT-GEM). $\epsilon$-SOFT-GEM outperforms A-GEM and several continual learning benchmarks in a single training epoch; additionally, it has state-of-the-art average accuracy and efficiency for computation and memory, like A-GEM, and provides a better trade-off between the stability of preserving learned knowledge and the plasticity of learning new knowledge.
Global diversity in bearings in primary THA

Choice of articulating materials, head size and the design of the articulation will become decisive for the long-term performance of a total hip arthroplasty (THA) and especially in terms of risk for dislocation and wear-related problems. Here we account for common alternatives based on available studies and the evidence that can be derived from them. Metal or ceramic femoral heads articulating against a liner or cup made of highly cross-linked polyethylene and ceramic-on-ceramic articulations have about similar risk for complications leading to revision, whereas the performance of metal-on-metal articulations, especially with use of big heads, is inferior. The clinical significance of problems related to ceramic-on-ceramic articulations such as squeaking remains unclear. With use of current technology ceramic fractures are rare. Large femoral heads have the potential to increase the range of hip movement before impingement occurs and are therefore expected to reduce dislocation rates. On the other hand, issues related to bearing wear, corrosion at the taper-trunnion junction and groin pain may arise with larger heads and jeopardize the longevity of THA. Based on current knowledge, 32-mm heads seem to be optimal for metal-on-polyethylene bearings. Patients with ceramic-on-ceramic bearings may benefit from even larger heads such as 36 or 40 mm, but so far there are no long-term reports that confirm the safety of bearings larger than 36 mm. Assessment of lipped liners is difficult because randomized studies are lacking, but retrospective clinical studies and registry data seem to indicate that this liner modification will reduce the rate of dislocation or revision due to dislocation without clear evidence of clinically obvious problems due to neck-liner impingement. The majority of studies support the view that constrained liners and dual mobility cups (DMC) will reduce the risk of revision due to dislocation both in primary and revision THA, the latter gaining increasing popularity in some countries. Both these devices suffer from implant-specific problems, which seem to be more common for the constrained liner designs. The majority of studies of these implants suffer from various methodological problems, not least selection bias, which calls for randomized studies preferably in a multi-centre setting to obtain sufficient power. In the 2020s, the orthopaedic profession should place more effort on such studies, as has already been achieved within other medical specialties, to improve the level of evidence in the choice of articulation when performing one of the most common in-hospital surgical procedures in Europe. Cite this article: EFORT Open Rev 2020;5:763-775. DOI: 10.1302/2058-5241.5. 200002
On some topological realizations of groups and homomorphisms

Let $f:G\rightarrow H$ be a homomorphism of groups, we construct a topological space $X_f$ such that its group of homeomorphisms is isomorphic to $G$, its group of homotopy classes of self-homotopy equivalences is isomorphic to $H$ and the natural map between the group of homeomorphisms of $X_f$ and the group of homotopy classes of self-homotopy equivalences of $X_f$ is precisely $f$. In addition, realization problems involving homology, homotopy groups and groups of automorphisms are considered.
Co-optimisation and Settlement of Power-Gas Coupled System in Day-ahead Market under Multiple Uncertainties

The interdependency of power systems and natural gas systems is being reinforced by the emerging power-to-gas facilities (PtGs), and the existing gas-fired generators. To jointly improve the efficiency and security under diverse uncertainties from renewable energy resources and load demands, it is essential to co-optimise these two energy systems for day-ahead market clearance. In this paper, a data-driven integrated electricity-gas system stochastic co-optimisation model is proposed. The model is accurately approximated by sequential mixed integer second-order cone programming, which can then be solved in parallel and decentralised manners by leveraging generalised Benders decomposition. Since the price formation and settlement issues have rarely been investigated for integrated electricity-gas systems in an uncertainty setting, a novel concept of expected locational marginal value is proposed to credit the flexibility of PtGs that helps hedging uncertainties. By comparing with a deterministic model and a distributionally robust model, the advantage of the proposed stochastic model and the efficiency of the proposed solution method are validated. Detailed results of pricing and settlement for PtGs are presented, showing that the expected locational marginal value can fairly credit the contribution of PtGs and reflect the system deficiency of capturing uncertainties.
Impaired Coupling of the Brain’s Default Network During Sleep Deprivation: A Resting-State EEG Study

Introduction Sleep deprivation (SD) has a negative influence on mood and emotion processing, and previous studies have elucidated the impaired coupling within the default network (DN) after SD. However, the dynamic characteristic with high temporal precision was rarely investigated in the DN after SD. Methods Here, the resting-state EEG after nocturnal sleep (NS) and SD was collected from 31 participants. The cortical electrical activities of the posterior cingulate cortex (PCC) and the anterior medial prefrontal cortex (aMPFC) were reconstructed applying the eLORETA, and the functional connectivity (FC) of PCC-aMPFC was calculated using the power envelope connectivity (PEC). Results Compared with NS, the power spectrums of the PCC and the FC of PCC-aMPFC were significantly reduced in the α band after SD. Interestingly, the impaired PCC-aMPFC integration was positively correlated with the decreased positive affect, implying that the DN plays a critical role in the subjective mood state. Our moderation analysis further revealed that the intensity of the DN posterior–anterior interaction moderated sleep loss and positive affect. Discussion Overall, the results reveal the strong relationship between the uncoupling of DN and the feeling down of mood. Our research may contribute towards a better understanding of the mood and cognition processing after sleep loss.
Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models

Credit scoring is an essential tool used by global financial institutions and credit lenders for financial decision making. In this paper, we introduce a new method based on Gaussian Mixture Model (GMM) to forecast the probability of default for individual loan applicants. Clustering similar customers with each other, our model associates a probability of being healthy to each group. In addition, our GMM-based model probabilistically associates individual samples to clusters, and then estimates the probability of default for each individual based on how it relates to GMM clusters. We provide applications for risk managers and decision makers in banks and non-bank financial institutions to maximize profit and mitigate the expected loss by giving loans to those who have a probability of default below a decision threshold. Our model has a number of advantages. First, it gives a probabilistic view of credit standing for each individual applicant instead of a binary classification and therefore provides more information for financial decision makers. Second, the expected loss on the train set calculated by our GMM-based default probabilities is very close to the actual loss, and third, our approach is computationally efficient.
Privacy-Preserving Pose Estimation for Human-Robot Interaction

Pose estimation is an important technique for nonverbal human-robot interaction. That said, the presence of a camera in a person's space raises privacy concerns and could lead to distrust of the robot. In this paper, we propose a privacy-preserving camera-based pose estimation method. The proposed system consists of a user-controlled translucent filter that covers the camera and an image enhancement module designed to facilitate pose estimation from the filtered (shadow) images, while never capturing clear images of the user. We evaluate the system's performance on a new filtered image dataset, considering the effects of distance from the camera, background clutter, and film thickness. Based on our findings, we conclude that our system can protect humans' privacy while detecting humans' pose information effectively.
Predicting Human Strategies in Simulated Search and Rescue Task

In a search and rescue scenario, rescuers may have different knowledge of the environment and strategies for exploration. Understanding what is inside a rescuer's mind will enable an observer agent to proactively assist them with critical information that can help them perform their task efficiently. To this end, we propose to build models of the rescuers based on their trajectory observations to predict their strategies. In our efforts to model the rescuer's mind, we begin with a simple simulated search and rescue task in Minecraft with human participants. We formulate neural sequence models to predict the triage strategy and the next location of the rescuer. As the neural networks are data-driven, we design a diverse set of artificial "faux human" agents for training, to test them with limited human rescuer trajectory data. To evaluate the agents, we compare it to an evidence accumulation method that explicitly incorporates all available background knowledge and provides an intended upper bound for the expected performance. Further, we perform experiments where the observer/predictor is human. We show results in terms of prediction accuracy of our computational approaches as compared with that of human observers.
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network

In this paper, we focus on a popular DML framework -- the parameter server computation paradigm and iterative learning algorithms that proceed in rounds. We aim to reduce the communication complexity of Byzantine-tolerant DML algorithms in the single-hop radio network. Inspired by the CGC filter developed by Gupta and Vaidya, PODC 2020, we propose a gradient descent-based algorithm, Echo-CGC. Our main novelty is a mechanism to utilize the broadcast properties of the radio network to avoid transmitting the raw gradients (full $d$-dimensional vectors). In the radio network, each worker is able to overhear previous gradients that were transmitted to the parameter server. Roughly speaking, in Echo-CGC, if a worker "agrees" with a combination of prior gradients, it will broadcast the "echo message" instead of the its raw local gradient. The echo message contains a vector of coefficients (of size at most $n$) and the ratio of the magnitude between two gradients (a float). In comparison, the traditional approaches need to send $n$ local gradients in each round, where each gradient is typically a vector in an ultra-high dimensional space ($d\gg n$). The improvement on communication complexity of our algorithm depends on multiple factors, including number of nodes, number of faulty workers in an execution, and the cost function. We numerically analyze the improvement, and show that with a large number of nodes, Echo-CGC reduces $80\%$ of the communication under standard assumptions.
Providing More Efficient Access to Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege

At present, the review process for material that is exempt from disclosure under the Freedom of Information Act (FOIA) in the United States of America, and under many similar government transparency regimes worldwide, is entirely manual. Public access to the records of their government is thus inhibited by the long backlogs of material awaiting such reviews. This article studies one aspect of that problem by first creating a new public test collection with annotations for one class of exempt material subject to the deliberative process privilege, and then by using that test collection to study the ability of current text classification techniques to identify those materials that are exempt from release under that privilege. Results show that when the system is trained and evaluated using annotations from the same reviewer, even difficult cases can often be reliably detected. However, results also show that differences in reviewer interpretations, differences in record custodians, and differences in topics of the records used for training and testing can pose challenges.
IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.
Igniting Weak Interactions in Neutron Star Postmerger Accretion Disks

The merger of two neutron stars or a neutron star and a black hole typically results in the formation of a postmerger accretion disk. Outflows from disks may dominate the overall ejecta from mergers and be a major source of r-process nuclei in our universe. We explore the parameter space of such disks and their outflows and r-process yields by performing 3D general-relativistic magnetohydrodynamic simulations with weak interactions and approximate neutrino transport. We discuss the mapping between the initial binary parameters and the parameter space of the resulting disks, chiefly characterized by their initial accretion rate. We demonstrate the existence of an ignition threshold for weak interactions at around ∼10−3 M ⊙ s−1 for typical parameters by means of analytic calculations and numerical simulations. We find a degenerate, self-regulated, neutrino-cooled regime above the threshold and an advection-dominated regime below the threshold. Excess heating in the absence of neutrino cooling below the threshold leads to ≳60% of the initial disk mass being ejected in outflows, with typical velocities of ∼(0.1–0.2)c, compared to ≲40% at ∼(0.1–0.15)c above the threshold. While disks below the threshold show suppressed production of light r-process elements, disks above the threshold can produce the entire range of r-process elements, in good agreement with the observed solar system abundances. Disks below the ignition threshold may produce an overabundance of actinides seen in actinide-boost stars. As gravitational-wave detectors start to sample the neutron star merger parameter space, different disk realizations may be observable via their associated kilonova emission.
Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment

Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time Warping (DTW) and employ handcrafted features, which cannot be adapted to different acoustic conditions. We propose a method to overcome this limitation using learned frame similarity for audio-to-score alignment. We focus on offline audio-to-score alignment of piano music. Experiments on music data from different acoustic conditions demonstrate that our method achieves higher alignment accuracy than a standard DTW-based method that uses handcrafted features, and generates robust alignments whilst being adaptable to different domains at the same time.
Meaningful Answer Generation of E-Commerce Question-Answering

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation, which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews. We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.
Learning Associative Inference Using Fast Weight Memory

Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed Fast Weight Memory (FWM). Through differentiable operations at every step of a given input sequence, the LSTM updates and maintains compositional associations stored in the rapidly changing FWM weights. Our model is trained end-to-end by gradient descent and yields excellent performance on compositional language reasoning problems, meta-reinforcement-learning for POMDPs, and small-scale word-level language modelling.