Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of a Bioresorbable Magnesium-Reinforced PLA-Integrated GTR/GBR Membrane as Dental Applications

Inferior mechanical properties have always been a limitation of the bioresorbable membranes in GBR/GTR. This study is aimed at fabricating a bioresorbable magnesium-reinforced polylactic acid- (PLA-) integrated membrane and investigating its mechanical properties, degradation rate, and biocompatibility. The uncoated and fluoride-coated magnesium alloys, AZ91, were made into strips. Then, magnesium-reinforced PLA-integrated membrane was made through integration. PLA strips were used in the control group instead of magnesium strips. Specimens were cut into rectangular shape and immersed in Hank's Balanced Salt Solution (HBSS) at 37°C for 4, 8, and 12 d. The weight loss of the AZ91 strips was measured. Three-point bending tests were conducted before and after the immersion to determine the maximum load on specimens. Potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) were conducted on coated and uncoated AZ91 plates to examine corrosion resistance. Murine fibroblast and osteoblast cells were cultured on circular specimens and titanium disks for 1, 3, and 5 d. Thereafter, WST test was performed to examine cell proliferation. As a result, the coated and uncoated groups showed higher maximum loads than the control group at all time points. The weight loss of AZ91 strips used in the coated group was lower than that in the uncoated group. PDP, EIS, SEM, and EDS showed that the coated AZ91 had a better corrosion resistance than the uncoated AZ91. The cell proliferation test showed that the addition of AZ91 did not have an adverse effect on osteoblast cells. Conclusively, the magnesium-reinforced PLA-integrated membrane has excellent load capacity, corrosion resistance, cell affinity, and proper degradation rate. Moreover, it has great potential as a bioresorbable membrane in the GBR/GTR application.
Range Expansion for Wireless Power Transfer: A Joint Beamforming and Waveform Architecture

Far-field Wireless Power Transfer (WPT) has emerged as a viable power source for Internet of Things (IoT) and Wireless Sensor Network (WSN). Expansion of the power transfer range is an important challenge to enable the design of efficient networks of small autonomous devices and drive the massive numbers of devices using a single wireless power source. Several signal design strategies have been proposed and verified to maximize the output DC power at the receiver under a transmit power constraint. In this paper, we study channel-adaptive beamforming and waveform to expand the power transfer range as well as output DC power performance. To that end, we have designed, prototyped a far-field WPT system and established a WPT testbed in a realistic indoor environment. The experiments have been conducted in a variety of wireless channel conditions in an indoor office environment with various distances. The measurement data have been fitted using simple analytical model to analyze the output DC power and achievable range improvement depending on the signal design schemes and the number of tones and antennas. The model shows a clear relationship between signal design versus output DC power and achievable range, and enables to predict the achievable power transfer range for given transmit power constraint and target received DC power. Results highlight the significant benefit of a channel-adaptive joint beamforming and waveform architecture to expand the power transfer range.
OLALA: Object-Level Active Learning Based Layout Annotation

In layout object detection problems, the ground-truth datasets are constructed by annotating object instances individually. Yet active learning for object detection is typically conducted at the image level, not at the object level. Because objects appear with different frequencies across images, image-level active learning may be subject to over-exposure to common objects. This reduces the efficiency of human labeling. This work introduces an Object-Level Active Learning based Layout Annotation framework, OLALA, which includes an object scoring method and a prediction correction algorithm. The object scoring method estimates the object prediction informativeness considering both the object category and the location. It selects only the most ambiguous object prediction regions within an image for annotators to label, optimizing the use of the annotation budget. For the unselected model predictions, we propose a correction algorithm to rectify two types of potential errors with minor supervision from ground-truths. The human annotated and model predicted objects are then merged as new image annotations for training the object detection models. In simulated labeling experiments, we show that OLALA helps to create the dataset more efficiently and report strong accuracy improvements of the trained models compared to image-level active learning baselines. The code is available at this https URL
Inquisitive Question Generation for High Level Text Comprehension

Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more. Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets. We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while a person is reading through a document. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. We show that readers engage in a series of pragmatic strategies to seek information. Finally, we evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUISITIVE questions.
MM-Hand: 3D-Aware Multi-Modal Guided Hand Generation for 3D Hand Pose Synthesis

Estimating the 3D hand pose from a monocular RGB image is important but challenging. A solution is training on large-scale RGB hand images with accurate 3D hand keypoint annotations. However, it is too expensive in practice. Instead, we develop a learning-based approach to synthesize realistic, diverse, and 3D pose-preserving hand images under the guidance of 3D pose information. We propose a 3D-aware multi-modal guided hand generative network (MM-Hand), together with a novel geometry-based curriculum learning strategy. Our extensive experimental results demonstrate that the 3D-annotated images generated by MM-Hand qualitatively and quantitatively outperform existing options. Moreover, the augmented data can consistently improve the quantitative performance of the state-of-the-art 3D hand pose estimators on two benchmark datasets. The code will be available at https://github.com/ScottHoang/mm-hand.
A Unified Framework for Spin-Charge Interconversion in Spin-Orbit Materials

Materials with spin-orbit coupling are of great current interest for various spintronics applications due to the efficient electrical generation and detection of electron spins. Over the past decade, a large number of materials have been studied including topological insulators, transition metals, Kondo insulators, semimetals, semiconductors, oxide interfaces, etc., however, there is no unifying physical framework for understanding the physics and therefore designing a material system and devices with the desired properties. We present a model that binds together the experimental data observed on the wide variety of materials in a unified manner. We show that, in a material with a given spin orbit coupling, the density of states plays a key role in determining the spin-charge interconversion efficiency and a simple inverse relationship can be obtained. Remarkably, experimental data on spin voltage, obtained over the last decade on many different material systems closely follow such inverse relationship. Based on such analytical relationship, we further deduce two figure of merits of great current interest: the spin-orbit torque efficiency (for the direct effect) and the inverse Rashba-Edelstein effect length (for the inverse effect), which statistically show good agreement with the existing experimental data on wide varieties of materials.
On the Universality of the Double Descent Peak in Ridgeless Regression

We prove a non-asymptotic distribution-independent lower bound for the expected mean squared generalization error caused by label noise in ridgeless linear regression. Our lower bound generalizes a similar known result to the overparameterized (interpolating) regime. In contrast to most previous works, our analysis applies to a broad class of input distributions with almost surely full-rank feature matrices, which allows us to cover various types of deterministic or random feature maps. Our lower bound is asymptotically sharp and implies that in the presence of label noise, ridgeless linear regression does not perform well around the interpolation threshold for any of these feature maps. We analyze the imposed assumptions in detail and provide a theory for analytic (random) feature maps. Using this theory, we can show that our assumptions are satisfied for input distributions with a (Lebesgue) density and feature maps given by random deep neural networks with analytic activation functions like sigmoid, tanh, softplus or GELU. As further examples, we show that feature maps from random Fourier features and polynomial kernels also satisfy our assumptions. We complement our theory with further experimental and analytic results.
A Fully Hyperbolic Neural Model for Hierarchical Multi-class Classification

Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. However, it is not clear how to integrate hyperbolic components into downstream tasks. This is the first work that proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space. We evaluate the proposed model on two challenging datasets and compare to different baselines that operate under Euclidean assumptions. Our hyperbolic model infers the latent hierarchy from the class distribution, captures implicit hyponymic relations in the inventory, and shows performance on par with state-of-the-art methods on fine-grained classification with remarkable reduction of the parameter size. A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers.
Photon-Driven Neural Path Guiding

Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the input for sampling density reconstruction, which is highly effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for highly efficient path guiding for any path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding method can generalize well on diverse challenging testing scenes that are not seen in training. Our approach achieves significantly better rendering results of testing scenes than previous state-of-the-art path guiding methods.
Deep Reinforcement Learning for the Electric Vehicle Routing Problem With Time Windows

The past decade has seen a rapid penetration of electric vehicles (EVs) as more and more logistics and transportation companies start to deploy electric vehicles (EVs) for service provision. In order to model the operations of a commercial EV fleet, we utilize the EV routing problem with time windows (EVRPTW). In this paper, we propose an end-to-end deep reinforcement learning framework to solve the EVRPTW. In particular, we develop an attention model incorporating the pointer network and a graph embedding layer to parameterize a stochastic policy for solving the EVRPTW. The model is then trained using policy gradient with rollout baseline. Our numerical studies show that the proposed model is able to efficiently solve EVRPTW instances of large sizes that are not solvable with current existing approaches.
Gold Standard Pairs Trading Rules: Are They Valid?

Pairs trading is a strategy based on exploiting mean reversion in prices of securities. It has been shown to generate significant excess returns, but its profitability has dropped significantly in recent periods. We employ the most common distance and cointegration methods on US equities from 1990 to 2020 including the Covid-19 crisis. The strategy overall fails to outperform the market benchmark even with hyperparameter tuning, but it performs very strongly during bear markets. Furthermore, we demonstrate that market factors have a strong relationship with the optimal parametrization for the strategy, and adjustments are appropriate for modern market conditions.
Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.
A Convex Approach to Data-Driven Optimal Control via Perron–Frobenius and Koopman Operators

This article is about the data-driven computation of optimal control for a class of control affine deterministic nonlinear systems. We assume that the control dynamical system model is not available, and the only information about the system dynamics is available in the form of time-series data. We provide a convex formulation for the optimal control problem (OCP) of the nonlinear system. The convex formulation relies on the duality result in the dynamical system’s stability theory involving density function and Perron–Frobenius operator. We formulate the OCP as an infinite-dimensional convex optimization program. The finite-dimensional approximation of the optimization problem relies on the recent advances made in the Koopman operator’s data-driven computation, which is dual to the Perron–Frobenius operator. Simulation results are presented to demonstrate the application of the developed framework.
Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy

During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (users broadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software, we assess the reputation of the pieces of news exchanged. By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem. Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem.
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

It has been demonstrated that hidden representation learned by deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy. To address this issue, we propose a novel approach called Differentially Private Neural Representation (DPNR) to preserve privacy of the extracted representation from text. DPNR utilises Differential Privacy (DP) to provide formal privacy guarantee. Further, we show that masking words via dropout can further enhance privacy. To maintain utility of the learned representation, we integrate DP-noisy representation into a robust training process to derive a robust target model, which also helps for model fairness over various demographic variables. Experimental results on benchmark datasets under various parameter settings demonstrate that DPNR largely reduces privacy leakage without significantly sacrificing the main task performance.
D3Net: Densely connected multidilated DenseNet for music source separation

Music source separation involves a large input field to model a long-term dependence of an audio signal. Previous convolutional neural network (CNN) -based approaches address the large input field modeling using sequentially down- and up-sampling feature maps or dilated convolution. In this paper, we claim the importance of a rapid growth of a receptive field and a simultaneous modeling of multi-resolution data in a single convolution layer, and propose a novel CNN architecture called densely connected dilated DenseNet (D3Net). D3Net involves a novel multi-dilated convolution that has different dilation factors in a single layer to model different resolutions simultaneously. By combining the multi-dilated convolution with DenseNet architecture, D3Net avoids the aliasing problem that exists when we naively incorporate the dilated convolution in DenseNet. Experimental results on MUSDB18 dataset show that D3Net achieves state-of-the-art performance with an average signal to distortion ratio (SDR) of 6.01 dB.
Linguistic Profiling of a Neural Language Model

In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT’s capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning

Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algorithms have been proposed with different pre-determined divergences to quantify the discrepancy. This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency? In this work, we propose $f$-GAIL, a new generative adversarial imitation learning (GAIL) model, that automatically learns a discrepancy measure from the $f$-divergence family as well as a policy capable of producing expert-like behaviors. Compared with IL baselines with various predefined divergence measures, $f$-GAIL learns better policies with higher data efficiency in six physics-based control tasks.
Spawning grounds of Atlantic cod ( Gadus morhua ) in the North Sea

Theprotectionofspawningﬁshfromanthropogenicimpactsrequiresinformationonthelocation,timing,andinterannualpersistenceofspawningaggregations.Inthisstudy,generalizedadditivemodelswereusedtopredictthespawninghabitatofNorthSeacod,basedontheabundanceofspawningﬁshwithinthreepopulationsubareasandnineenvironmentallayers. Thelengthofspawningcoddifferedamongpopulationsubareas,consistentwithpublishedevidenceofmaturationdifferences.InthenortheastNorthSea(Viking),thepeakinthespawningseasonwasestimatedtooccurafterthesurveywhichmadethepredictionofspawninggroundsforthisarealesscertain.Codwerefoundtopreferareaswithtemperaturesaround5–7 8 Cfor spawning andtherewasageneral preferencefor highsalinity waters.Persistence ofcod spawninggroundsoverthe studyperiod was related to interannual stability in temperature, with high variability in the use of Southern Bight spawning grounds. As such, cod appear to minimize interannual variability in the initial environmental conditions affecting offspring survival by selecting suitable cold spawning grounds. Seabed conditions also affected spawning distribution with cod selecting coarse sand and avoiding areas of very high tidal ﬂow. The model prediction was compared with the distribution of cod aggregations during the spawning season reported by ﬁshing boats. Seventy per cent of the aggregations was located in areas classiﬁed as occasional or recurrent spawning grounds. The predicted distribution conﬁrmed the widespread occurrence of spawning in the North Sea and showed good agreement with recent and past studies of cod egg distribution, suggesting that nearly all major historical areas of spawning still appear in use today. However, the study also found that the recent recovery of spawning-stock biomass was not uniform across the stock, being centred in the northwest subarea.
Codes for Distributed Storage

This chapter deals with the topic of designing reliable and efficient codes for the storage and retrieval of large quantities of data over storage devices that are prone to failure. For long, the traditional objective has been one of ensuring reliability against data loss while minimizing storage overhead. More recently, a third concern has surfaced, namely of the need to efficiently recover from the failure of a single storage unit, corresponding to recovery from the erasure of a single code symbol. We explain here, how coding theory has evolved to tackle this fresh challenge.