Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of Cs Removal from Fukushima Weathered Biotite by Heat Treatment with a NaCl–CaCl2 Mixed Salt

An in situ extended X-ray absorption fine structure (in situ EXAFS) spectroscopic analysis at high temperature was conducted to investigate the mechanism of Cs removal from weathered biotite (WB) from Fukushima, induced by heating with a mixed salt of NaCl and CaCl2. This indicated that most Cs remained in WB during heating at 200–700 °C. In addition, the in situ EXAFS spectra gradually changed on heating with the mixed salt and a completely different spectrum was observed for the sample after cooling from 700 °C to room temperature (RT). Ex situ EXAFS measurements and X-ray fluorescence analyses were also conducted on samples after heat treatment and removal of the mixed salt to clarify the temperature dependence of the Cs removal ratio. On the basis of the results of radial structure function analysis obtained from in situ EXAFS, we concluded that almost all of the Cs was removed from WB by heating at 700 °C with the mixed salt, and that Cs formed Cs–Cl bonds after cooling to RT from 700 °C. In contrast, although more than half of the Cs present was removed from WB by heat treatment at 500 °C, most Cs was surrounded by silica tetrahedrons, maintained by Cs–O bonds. On the basis of these results, different Cs removal processes are suggested for the high-temperature (600–700 °C) and low-temperature (400–500 °C) regions.
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts

Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding. Previous work on cross-document coreference resolution (CDCR) typically considers mentions of events in the news, which seldom involve abstract technical concepts that are prevalent in science and technology. These complex concepts take diverse or ambiguous forms and have many hierarchical levels of granularity (e.g., tasks and subtasks), posing challenges for CDCR. We present a new task of Hierarchical CDCR (H-CDCR) with the goal of jointly inferring coreference clusters and hierarchy between them. We create SciCo, an expert-annotated dataset for H-CDCR in scientific papers, 3X larger than the prominent ECB+ resource. We study strong baseline models that we customize for H-CDCR, and highlight challenges for future work.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation

Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging software. Lightweight neural network is one practical and effective way to accomplish the single-image segmentation task. This work focuses on the single-image segmentation problem only with some seeds such as scribbles. Inspired by the dynamic receptive field in the human being’s visual system, we propose the Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the contextual information for neural networks. The core idea is randomly selecting the spatial sampling area according to the Gaussian distribution offsets. Our GDC can be easily used as a module to build lightweight or complex segmentation networks. We adopt the proposed GDC to address the typical single-image segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation. Experiments demonstrate that the GDC outperforms other existing convolutions on three benchmark segmentation datasets including Pascal-Context, Pascal-VOC 2012, and Cityscapes. Additional experiments are also conducted to illustrate that the GDC can produce richer and more vivid features compared with other convolutions. In general, our GDC is conducive to the convolutional neural networks to form an overall impression of the image.
SIGIR 2021 E-Commerce Workshop Data Challenge

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for"In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.
Improving Neural Machine Translation with Compact Word Embedding Tables

Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens. 1 In this paper, we analyze the impact and utility of such matrices in the context of neural machine translation (NMT). We show that detracting syntactic and semantic information from word embeddings and running NMT systems with random embeddings is not as damaging as it initially sounds. We also show how incorporating only a limited amount of task-speciﬁc knowledge from fully-trained embeddings can boost the performance NMT systems. Our ﬁndings demonstrate that in exchange for negligible deterioration in performance, any NMT model can be run with partially random embeddings. Working with such structures means a minimal memory requirement as there is no longer need to store large embedding tables, which is a signiﬁcant gain in industrial and on-device settings. We evaluated our embeddings in translating English into German and French and achieved a 5 . 3 x compression rate. Despite having a considerably smaller architecture, our models in some cases are even able to outperform state-of-the-art baselines.
Mining Latent Structures for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features.
Effect of photooxidation on the surface properties of polystyrene

The effect of UV irradiation on the surface properties of polystyrene was studied. It was established that an increase in the exposure dose significantly improves the water wettability of polystyrene; that is, its surface undergoes hydrophilization under the action of photooxidation. It was found that a partial relaxation (rearrangement) of the surface of the photooxidized polymer to the initial low-energy state takes place at the annealing temperatures which are appreciably below the glass transition temperature T g of block polystyrene. An analogous effect was observed when evacuation of the photooxidized samples was carried out even at room temperature. Hence, already at the early stages of photooxidation, rather low-molecular-mass polar products of degradation are formed on the surface layer of polystyrene. These products plasticize the surface layer and markedly decrease its T g . This conclusion is supported by the data of atomic force microscopy, which suggest that, after sufficiently prolonged UV irradiation, the surface layer of polystyrene behaves as a high-viscous liquid in contact with the edge of a microscope.
Multimodal Deception Detection in Videos via Analyzing Emotional State-based Feature

— Deception detection is an important task that has been a hot research topic due to its potential applications. It can be applied to many areas from national security (e.g, airport security, jurisprudence, and law enforcement) to real-life applications (e.g., business and computer vision). However, some critical problems still exist and worth more investigation. One of the major challenges is the data scarcity problem. Until now, only one multimodal benchmark dataset on deception detection has been published, which contains 121 video clips for deception detection (61 for deceptive class and 60 for truthful class). This amount of data is hard to drive deep neural network-based methods. Hence, they often suffered from the overﬁtting problem and the bad generalization ability. Also, the ground truth data contains some unusable frames for many factors including the face is too small to be recognized the facial expression, face is covered by text, ﬁle corruption, etc. However, most of the literature did not consider these problems. In this paper, we design a series of data preprocessing methods to deal with the problem ﬁrst. Then, we propose a multimodal deception detection framework to construct our novel emotional state-based feature and used open toolkit openSMILE to extract the features from audio modality. A voting scheme is also designed to combine the emotional state information obtained from both visual modality and audio modality. Finally, the novel emotion state transformation (EST) feature is determined by our algorithm. The critical analysis and comparison of the proposed methods with the state-of-the-art multimodal method are showed that the overall performance has a great improvement of accuracy from 84.16% to 91.67% and ROC-AUC from 0.9211 to 0.9244.
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces

Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents. This work presents a novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain. Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (high-resource) and 5 target (low-resource) corpora.
Guilt by Association: Emotion Intensities in Lexical Representations

What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.
Planning with Expectation Models for Control

In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather than the full distribution, or a sample thereof, with no loss in planning performance. Such expectation models are of interest when the environment is stochastic and non-stationary, and the model is approximate, such as when it is learned using function approximation. In these cases a full distribution model may be impractical and a sample model may be either more expensive computationally or of high variance. Wan et al. considered only planning for prediction to evaluate a fixed policy. In this paper, we treat the control case - planning to improve and find a good approximate policy. We prove that planning with an expectation model must update a state-value function, not an action-value function as previously suggested (e.g., Sorg&Singh, 2010). This opens the question of how planning influences action selections. We consider three strategies for this and present general MBRL algorithms for each. We identify the strengths and weaknesses of these algorithms in computational experiments. Our algorithms and experiments are the first to treat MBRL with expectation models in a general setting.
Intent Features for Rich Natural Language Understanding

Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. However, these models are often created from scratch, for specific clients and use cases and require the annotation of large datasets. This encourages the sharing of annotated data across multiple clients. To facilitate this we introduce the idea of intent features: domain and topic agnostic properties of intents that can be learnt from the syntactic cues only, and hence can be shared. We introduce a new neural network architecture, the Global-Local model, that shows significant improvement over strong baselines for identifying these features in a deployed, multi-intent natural language understanding module, and more generally in a classification setting where a part of an utterance has to be classified utilizing the whole context.
Searching for Extragalactic Exoplanetary Systems: The Curious Case of BD+20 2457

Planets and their host stars carry a long-term memory of their origin in their chemical compositions. Thus, identifying planets formed in different environments improves our understating of planetary formation. Although restricted to detecting exoplanets within the solar vicinity, we might be able to detect planetary systems that formed in small external galaxies and later merged with the Milky Way. In fact, Gaia data have unequivocally shown that the Galaxy underwent several significant minor mergers during its first billion years of formation. The stellar debris of one of these mergers, Gaia-Enceladus (GE), is thought to have built up most of the stellar halo in the solar neighborhood. In this Letter, we investigate the origin of known planet-host stars combining data from the NASA Exoplanet Archive with Gaia EDR3 and large-scale spectroscopic surveys. We adopt a kinematic criterion and identify 42 stars associated with the Milky Way’s thick disk and one halo star. The only halo star identified, BD+20 2457, known to harbor two exoplanets, moves on a retrograde and highly eccentric orbit. Its chemical abundance pattern situates the star at the border between the thick disk, the old halo, and accreted populations. Given its orbital parameters and chemical properties, we suggest that BD+20 2457 is likely formed in the protodisk of the Galaxy, but we do not exclude the possibility of the star belonging to the debris of GE. Finally, we estimate a minimum age and mass limit for the star, which has implications for its planetary system and will be tested with future Transiting Exoplanet Survey Satellite observations.
Multi-Perspective Abstractive Answer Summarization

Community Question Answering (CQA) forums such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of questions. Each question thread can receive a large number of answers with different perspectives. The goal of multi-perspective answer summarization is to produce a summary that includes all perspectives of the answer. A major obstacle for multi-perspective, abstractive answer summarization is the absence of a dataset to provide supervision for producing such summaries. This work introduces a novel dataset creation method to automatically create multi-perspective, bullet-point abstractive summaries from an existing CQA forum. Supervision provided by this dataset trains models to inherently produce multi-perspective summaries. Additionally, to train models to output more diverse, faithful answer summaries while retaining multiple perspectives, we propose a multi-reward optimization technique coupled with a sentence-relevance prediction multi-task loss. Our methods demonstrate improved coverage of perspectives and faithfulness as measured by automatic and human evaluations compared to a strong baseline.
Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization

To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initializer which provides a simple guess and requires significant optimization but is nonetheless suitable for adaptation to upcoming terrain. We demonstrate experiments using the ANYmal C quadruped, with fully onboard sensing and computation, to cross obstacles at moderate speeds using this technique. Our second approach uses latent-mode trajectory regression (LMTR) to imitate expert data—while avoiding invalid interpolations between distinct behaviors—such that minimal optimization is needed. This enables high-speed motions that make more expansive use of the robot’s capabilities. We demonstrate it on flat ground with the real robot and provide numerical trials that progress toward deployment on terrain. These results illustrate a paradigm for advancing beyond short-horizon dynamic reactions, toward the type of intuitive and adaptive locomotion planning exhibited by animals and humans.
Convolutional Neural Networks in Orthodontics: a review

Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This review presents the application of convolutional neural networks in one of the fields of dentistry - orthodontics. Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and classification of defects on X-Ray panoramic images. In this work, we describe the current methods, the architectures of deep convolutional neural networks used, and their implementations, together with a comparison of the results achieved by them. The promising results and visualizations of the described studies show that the use of methods based on convolutional neural networks allows for the improvement of computer-based orthodontic treatment planning, both by reducing the examination time and, in many cases, by performing the analysis much more accurately than a manual orthodontist does.
Lifelong Learning with Sketched Structural Regularization

Preventing catastrophic forgetting while continually learning new tasks is an essential problem in lifelong learning. Structural regularization (SR) refers to a family of algorithms that mitigate catastrophic forgetting by penalizing the network for changing its"critical parameters"from previous tasks while learning a new one. The penalty is often induced via a quadratic regularizer defined by an \emph{importance matrix}, e.g., the (empirical) Fisher information matrix in the Elastic Weight Consolidation framework. In practice and due to computational constraints, most SR methods crudely approximate the importance matrix by its diagonal. In this paper, we propose \emph{Sketched Structural Regularization} (Sketched SR) as an alternative approach to compress the importance matrices used for regularizing in SR methods. Specifically, we apply \emph{linear sketching methods} to better approximate the importance matrices in SR algorithms. We show that sketched SR: (i) is computationally efficient and straightforward to implement, (ii) provides an approximation error that is justified in theory, and (iii) is method oblivious by construction and can be adapted to any method that belongs to the structural regularization class. We show that our proposed approach consistently improves various SR algorithms' performance on both synthetic experiments and benchmark continual learning tasks, including permuted-MNIST and CIFAR-100.
Are there reliable DFT approaches for 13C NMR chemical shift predictions of fullerene C60 derivatives

The relationships between experimental and theoretical 13C NMR chemical shifts of a pristine fullerene C60, monoadducts from [2 + n] cycloaddition (n = 1–3), and one [2 + 1] bis-adduct are systematically analyzed for the first time by using diverse quantum-chemical levels of theory. These levels involved B3LYP, B3PW91, B97-2, mPW1PW91, PBE1PBE, and X3LYP hybrid functionals combined with 3-21G, 6-31G, 6-31G(d), 6-31G(d,p), 6-31G(d,2p), LanL2DZ, and SDDAll basis sets. X3LYP/6-31G approach is determined to have the lowest deviations from the 13C NMR experimental data compared to the other methods for all the fullerene compounds (mean absolute error value is 0.856 ppm and root mean squared error value is 1.197 ppm). The highest deviations are characteristic for α (sp2 C2/C5/C8/C10) and β (sp2 C6/C7/C11/C12) carbon atoms relative to a functionalization site and for those (sp3 C1/C9) directly attached with a side fragment in the [2 + n] monoadducts (n = 1–3). A probable reason of such deviation is that the approaches do not take into account a contribution of paramagnetic ring currents to 13C NMR chemical shifts. The results will be useful in design of novel fullerene derivatives and in performing unambiguous 13C NMR chemical shift assignments with modern quantum chemistry calculations.