Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing Knowledge of Emerging Entities from Extended Search Snippets

Google and other search engines feature the entity search by representing a knowledge card summarizing related facts about the user-supplied entity. However, the knowledge card is limited to certain entities that have a Wiki page or an entry in encyclopedias such as Freebase. The current encyclopedias are limited to highly popular entities, which are far fewer compared with the emerging entities. Despite the availability of knowledge about the emerging entities on the search results, yet there are no approaches to capture, abstract, summerize, fuse, and validate fragmented pieces of knowledge about them. Thus, in this paper, we develop approaches to capture two types of knowledge about the emerging entities from a corpus extended from top-n search snippets of a given emerging entity. The first kind of knowledge identifies the role(s) of the emerging entity as, e.g., who is s/he? The second kind captures the entities closely associated with the emerging entity. As the testbed, we considered a collection of 20 emerging entities and 20 popular entities as the ground truth. Our approach is an unsupervised approach based on text analysis and entity embeddings. Our experimental studies show promising results as the accuracy of more than $87\%$ for recognizing entities and $75\%$ for ranking them. Besides $87\%$ of the entailed types were recognizable. Our testbed and source code is available on Github https://github.com/sunnyUD/research_source_code.
Branch-and-cut algorithms for the covering salesman problem

The Covering Salesman Problem (CSP) is a generalization of the Traveling Salesman Problem in which the tour is not required to visit all vertices, as long as all vertices are covered by the tour. The objective of CSP is to find a minimum length Hamiltonian cycle over a subset of vertices that covers an undirected graph. In this paper, valid inequalities from the generalized traveling salesman problem are applied to the CSP in addition to new valid inequalities that explore distinct aspects of the problem. A branch-and-cut framework assembles exact and heuristic separation routines for integer and fractional CSP solutions. Computational experiments show that the proposed framework outperformed methodologies from literature with respect to optimality gaps. Moreover, optimal solutions were proven for several previously unsolved instances.
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at https://github.com/pytorch/fairseq.
Label‐Free Sensing of Biomolecules with Field‐Effect Devices for Clinical Applications

Among the variety of transducer concepts pro- posed for label-free detection of biomolecules, the semi- conductor field-effect device (FED) is one of the most at- tractive platforms. As medical techniques continue to progress towards diagnostic and therapies based on bio- markers, the ability of FEDs for a label-free, fast and real-time detection of multiple pathogenic and physiologi- cally relevant molecules with high specificity and sensitiv- ity offers very promising prospects for their application in point-of-care and personalized medicine for an early diag- nosis and treatment of diseases. The presented paper re- views recent advances and current trends in research and development of different FEDs for label-free, direct elec- trical detection of charged biomolecules by their intrinsic molecular charge. The authors are mainly focusing on the detection of the DNA hybridization event, antibody-anti- gen affinity reaction as well as clinically relevant biomole- cules such as cardiac and cancer biomarkers.
A pr 2 02 1 Thurston Geometries in New Massive Gravity

Daniel Flores-Alfonso, ∗ Cesar S. Lopez-Monsalvo, † and Marco Maceda ‡ Departamento de F́ısica, CINVESTAV-IPN, A.P. 14-740, C.P. 07000, Ciudad de México, Mexico Conacyt-Universidad Autónoma Metropolitana Azcapotzalco, Avenida San Pablo Xalpa 180, Azcapotzalco, Reynosa Tamaulipas, C.P. 02200, Ciudad de México, Mexico Departamento de F́ısica, Universidad Autónoma Metropolitana Iztapalapa, Avenida San Rafael Atlixco 186, A.P. 55534, C.P. 09340, Ciudad de México, Mexico
Daisen: A Framework for Visualizing Detailed GPU Execution

Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator‐generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator‐generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator‐generated GPU execution traces. Daisen also includes a web‐based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open‐sourced implementation of Daisen can be found at gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at osf.io/j5ghq.
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping

Conventional works that learn grasping affordance from demonstrations need to explicitly predict grasping configurations, such as gripper approaching angles or grasping preshapes. Classic motion planners could then sample trajectories by using such predicted configurations. In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks. From a psychological perspective, there is a close association between attention and affordance. Therefore, with an end-to-end neural network, we propose to learn affordance cues as visual attention that serves as a useful indicating signal of how a demonstrator accomplishes tasks, instead of explicitly modeling affordances. To achieve this, we propose a contrastive learning framework that consists of a Siamese encoder and a trajectory decoder. We further introduce a coupled triplet loss to encourage the discovered affordance cues to be more affordance-relevant. Our experimental results demonstrate that our model with the coupled triplet loss achieves the highest grasping success rate in a simulated robot environment. Our project website can be accessed at1.
Deep Learning-based Codebook Design for Code-domain Non-Orthogonal Multiple Access Achieving a Single-User Bit Error Rate Performance

The codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered as a constellation design for multi-user multi-dimensional modulation (MU-MDM). This paper proposes an autoencoder (AE)-based constellation design for MU-MDM with the objective of achieving a comparable bit error rate (BER) performance to single-user multi-dimensional modulation (SU-MDM), i.e., alleviating performance degradation in non-optimal AE design caused by overloading multiple users. Recognizing that various constraints in a receiver structure degrade a BER performance of the codebook design in the existing CD-NOMA, the MU-MDM design aims at global optimization on the common ground with SU-MDM by leveraging the agnosticism of the neural network-based multi-user decoder obtained from AE training, while mitigating the power normalization constraint and exploiting dense resource mapping in the MU-MDM AE structure. Moreover, as opposed to the existing loss function for MU-MDM which has failed to minimize BER for different levels of signal-to-noise ratio (SNR), a hyperparameterized loss function and proper training procedures are introduced to jointly optimize the signal points for MU-MDM constellation and their bit-to-symbol mapping. It has been demonstrated that the proposed design achieves a single-user BER bound with only 0.2dB loss, equivalently outperforming the existing CD-NOMA designs, while maintaining their overloading factor.
End-to-end learning of keypoint detection and matching for relative pose estimation

We propose a new method for estimating the relative pose between two images, where we jointly learn keypoint detection, description extraction, matching and robust pose estimation. While our architecture follows the traditional pipeline for pose estimation from geometric computer vision, all steps are learnt in an end-to-end fashion, including feature matching. We demonstrate our method for the task of visual localization of a query image within a database of images with known pose. Pairwise pose estimation has many practical applications for robotic mapping, navigation, and AR. For example, the display of persistent AR objects in the scene relies on a precise camera localization to make the digital models appear anchored to the physical environment. We train our pipeline end-to-end specifically for the problem of visual localization. We evaluate our proposed approach on localization accuracy, robustness and runtime speed. Our method achieves state of the art localization accuracy on the 7 Scenes dataset.
Scene Graphs: A Survey of Generations and Applications

—Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisﬁed with simply detecting and recognizing objects in images; instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also know the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and ﬁnd similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present. To this end, this survey conducts a comprehensive investigation of the current scene graph research. More speciﬁcally, we ﬁrst summarized the general deﬁnition of the scene graph, then conducted a comprehensive and systematic discussion on the generation method of the scene graph (SGG) and the SGG with the aid of prior knowledge. We then investigated the main applications of scene graphs and summarized the most commonly used datasets. Finally, we provide some insights into the future development of scene graphs. We believe this will be a very helpful foundation for future research on scene graphs.
CURIE: An Iterative Querying Approach for Reasoning About Situations

Predicting the effects of unexpected situations is an important reasoning task, e.g., would cloudy skies help or hinder plant growth? Given a context, the goal of such situational reasoning is to elicit the consequences of a new situation (st) that arises in that context. We propose CURIE, a method to iteratively build a graph of relevant consequences explicitly in a structured situational graph (st graph) using natural language queries over a finetuned language model. Across multiple domains, CURIE generates st graphs that humans find relevant and meaningful in eliciting the consequences of a new situation (75% of the graphs were judged correct by humans). We present a case study of a situation reasoning end task (WIQA-QA), where simply augmenting their input with st graphs improves accuracy by 3 points. We show that these improvements mainly come from a hard subset of the data, that requires background knowledge and multi-hop reasoning.
Liquid-liquid equilibria for aromatics extraction systems with tetraethylene glycol

Liquid−Liquid equilibria (LLE) have been measured for five ternary and two multicomponent mixtures containing cyclohexane, benzene, toluene, p-xylene, tetraethylene glycol (TTEG), and water at 60 °C and 140 °C. A new UNIFAC group was defined, and its parameters were determined from the experimental data. The calculated values are in good agreement with the experimental and literature data with sufficient accuracy for the engineering design.
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation

This paper tackles automatically discovering phone-like acoustic units (AUD) from unlabeled speech data. Past studies usually proposed single-step approaches. We propose a two-stage approach: the first stage learns a subword-discriminative feature representation and the second stage applies clustering to the learned representation and obtains phone-like clusters as the discovered acoustic units. In the first stage, a recently proposed method in the task of unsupervised subword modeling is improved by replacing a monolingual out-of-domain (OOD) ASR system with a multilingual one to create a subword-discriminative representation that is more language-independent. In the second stage, segment-level k-means is adopted, and two methods to represent the variable-length speech segments as fixed-dimension feature vectors are compared. Experiments on a very low-resource Mboshi language corpus show that our approach outperforms state-of-the-art AUD in both normalized mutual information (NMI) and F-score. The multilingual ASR improved upon the monolingual ASR in providing OOD phone labels and in estimating the phone boundaries. A comparison of our systems with and without knowing the ground-truth phone boundaries showed a 16% NMI performance gap, suggesting that the current approach can significantly benefit from improved phone boundary estimation.
Microring Weight Banks Control beyond 8.5-bits Accuracy

The microring resonators (MRR) weight bank, a ubiquitous element in silicon photonics providing weight addition, yet, has long suffered from the weakness of low accuracy, so that harming its potential in various promising applications. Here, we demonstrated a novel dithering method for MRR weight control, reaching an accuracy of up to 8.5 bits and a precision of up to 9.0 bits, which rivals that of the analog processors. Also, this method maintains good scalability to control multiMRRs with little additional increase in searching time and system complexity, and has efficacious suppression to the inter-channel crosstalk. Given these features, this method could widely benefit the applications of MRR weight banks and facilitate the prevalence of the MRR in silicon photonics. © 2021 Optical Society of America
Synchronized SCUBA: D2D Communication for Out-of-Sync Devices

Device-to-device (D2D) communication is an essential component enabling connectivity for the Internet-of-Things (IoT). SCUBA, which stands for Sidelink Communication on Unlicensed Bands, is a novel medium access control protocol that facilitates D2D communications on the sidelink for IoT and machine-type communication (MTC) cellular devices. SCUBA includes support for direct peer-to-peer communication on the unlicensed bands by operating in a time division multiplexed manner to coexist with the underlying primary radio access technology, e.g., long term evolution - MTC (LTE-M). A fundamental requirement in the current version of SCUBA is that the communicating devices are to be synchronized with each other so that timing occasions of the devices can be accurately estimated by each other. However, when the devices are out of sync with each other, which may be caused due to one or more of the devices being out of cellular coverage region or are being served by different base stations, typically observed in mobile devices, operation of legacy SCUBA fails. To this end, we design synchronization methods to establish successful SCUBA links between devices that are out-of-sync with each other. Due to the inherent timing discovery embedded in our method, our solution also extends the operating range of SCUBA by eliminating its reliance on timing-agnostic communication. We analyze and compare the performance of our methods in terms of power consumption and the resultant impact on device battery life to show the potential of our solutions.
IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task

This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression task, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Towards high-rate RPC-based thermal neutron detectors using low-resistivity electrodes

We present experimental results on the counting rate measurements for several single-gap 10B lined resistive plate chambers (10B-RPCs) with anodes made from standard float glass, low-resistivity glass and ceramic. The measurements were performed at the V17 monochromatic neutron beamline (3.35 Å) at the Helmholtz-Zentrum Berlin. For the 10B-RPCs with 0.28mm thick float glass a maximum counting rate density of about 8 × 103 Hz/cm2 was obtained. In the case of low resistivity glass and ceramic, the counting rate density did not deviate from linear dependence on the neutron flux up to the maximum flux available at this beamline and exceeded a value of 3 × 104 Hz/cm2.
Data Augmentation with Manifold Barycenters

The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new data augmentation methods to alleviate the challenge. Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original knowledge domain. In this paper, we propose a new way of representing the available knowledge in the manifold of data barycenters. Such a representation allows performing data augmentation based on interpolation between the nearest data elements us-ing Wasserstein distance. The proposed method ﬁnds cliques in the nearest-neighbors graph and, at each sampling iteration, randomly draws one clique to compute the Wasserstein barycenter with random uniform weights. These barycenters then become the new natural-looking elements that one could add to the dataset. We apply this approach to the problem of landmarks detection and augment the available landmarks data within the dataset. Additionally, the idea is validated on cardiac data for the task of medical segmentation. Our approach reduces the overﬁtting and improves the quality metrics both beyond the original data outcome and beyond the result obtained with classical augmentation methods.
DiffAqua

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.
Topic Scaling: A Joint Document Scaling - Topic Model Approach To Learn Time-Specific Topics

This paper proposes a new methodology to study sequential corpora by implementing a two-stage algorithm that learns time-based topics with respect to a scale of document positions and introduces the concept of Topic Scaling, which ranks learned topics within the same document scale. The first stage ranks documents using Wordfish, a Poisson-based document-scaling method, to estimate document positions that serve, in the second stage, as a dependent variable to learn relevant topics via a supervised Latent Dirichlet Allocation. This novelty brings two innovations in text mining as it explains document positions, whose scale is a latent variable, and ranks the inferred topics on the document scale to match their occurrences within the corpus and track their evolution. Tested on the U.S. State Of The Union two-party addresses, this inductive approach reveals that each party dominates one end of the learned scale with interchangeable transitions that follow the parties’ term of office, while it shows for the corpus of German economic forecasting reports a shift in the narrative style adopted by economic institutions following the 2008 financial crisis. Besides a demonstrated high accuracy in predicting in-sample document positions from topic scores, this method unfolds further hidden topics that differentiate similar documents by increasing the number of learned topics to expand potential nested hierarchical topic structures. Compared to other popular topic models, Topic Scaling learns topics with respect to document similarities without specifying a time frequency to learn topic evolution, thus capturing broader topic patterns than dynamic topic models and yielding more interpretable outputs than a plain Latent Dirichlet Allocation.