Miracle-dz/UniFAR-FTUs huggingface dataset:
Keyphrase Generation: A Text Summarization Struggle

Authors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.
Med3D: Transfer Learning for 3D Medical Image Analysis

The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and pathologies. To extract general medical three-dimension (3D) features, we design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models. We transfer Med3D pre-trained models to lung segmentation in LIDC dataset, pulmonary nodule classification in LIDC dataset and liver segmentation on LiTS challenge. Experiments show that the Med3D can accelerate the training convergence speed of target 3D medical tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times compared with training from scratch as well as improve accuracy ranging from 3% to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation network, in case of single model, we achieve 94.6\% Dice coefficient which approaches the result of top-ranged algorithms on the LiTS challenge.
Defectnet: Multi-Class Fault Detection on Highly-Imbalanced Datasets

As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias toward larger classes, which tend to be the background in the semantic segmentation task. This becomes a major problem for fault detection, where the targets appear very small on the images and vary in both types and sizes. In this paper we propose a new network architecture, DefectNet, that offers multi-class (including but not limited to) defect detection on highly-imbalanced datasets. DefectNet consists of two parallel paths, which are a fully convolutional network and a dilated convolutional network to detect large and small objects respectively. We propose a hybrid loss maximising the usefulness of a dice loss and a cross entropy loss, and we also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence of some targets in training batches. The prediction results show that our DefectNet outperforms state-of-the-art networks for detecting multi-class defects with the average accuracy improvement of approximately 10% on a wind turbine.
Joining Sound Event Detection and Localization Through Spatial Segregation

Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This article presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions.
News versus information

We consider the relative entropy between the vacuum state and a coherent state in linearized quantum gravity around an asymptotically flat stationary black hole spacetime. Combining recent results by Casini et al and Longo with the Raychaudhuri equation, the following result is obtained: let be the algebra of observables assoiciated with a region that is the causal future of some compact set in the interior of the spacetime. Let S be the relative entropy with respect to this algebra, A the area of the horizon cross section defined by the region, computed to second order in the gravitational perturbation. If the region is time-translated by the Killing parameter t, then , with F the flux of the gravitational/matter radiation (integrated squared news tensor) emitted towards the future of the region.
Phonon and electron transport in Janus monolayers based on InSe

We systematically investigated the phonon and electron transport properties of monolayer InSe and its Janus derivatives including monolayer In2SSe and In2SeTe by first-principles calculations. The breaking of mirror symmetry produces a distinguishable A1 peak in the Raman spectra of monolayer In2SSe and In2SeTe. The long-range harmonic and anharmonic interactions play an important role in the heat transport of the group-III chalcogenides. The room-temperature thermal conductivity () of monolayer InSe, In2SSe and In2SeTe are 44.6, 46.9, and 29.9 W (mK)−1, respectively. There is a competition effect between atomic mass, phonon group velocity and phonon lifetime. The can be further effectively modulated by sample size for the purpose of thermoelectric applications. Meanwhile, monolayer In2SeTe exhibits a direct band gap of 1.8 eV and a higher electron mobility than that of monolayer InSe, due to the smaller electron effective mass caused by tensile strain on the Se side and smaller deformation potential. These results indicate that 2D Janus group-III chalcogenides can provide a platform to design the new electronic, optoelectronic and thermoelectric devices.
Adaptive Bayesian Linear Regression for Automated Machine Learning

To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine learning (AutoML) is to design methods that can automatically perform model selection and hyperparameter optimization without human interventions for a given dataset. In this paper, we propose a meta-learning method that can search for a high-performance machine learning pipeline from the predefined set of candidate pipelines for supervised classification datasets in an efficient way by leveraging meta-data collected from previous experiments. More specifically, our method combines an adaptive Bayesian regression model with a neural network basis function and the acquisition function from Bayesian optimization. The adaptive Bayesian regression model is able to capture knowledge from previous meta-data and thus make predictions of the performances of machine learning pipelines on a new dataset. The acquisition function is then used to guide the search of possible pipelines based on the predictions.The experiments demonstrate that our approach can quickly identify high-performance pipelines for a range of test datasets and outperforms the baseline methods.
Person-in-WiFi: Fine-Grained Person Perception Using WiFi

Fine-grained person perception such as body segmentation and pose estimation has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars (e.g. RF-Pose), and LiDARs. These solutions require 2D images, depth maps or 3D point clouds of person bodies as input. In this paper, we take one step forward to show that fine-grained person perception is possible even with 1D sensors: WiFi antennas. Specifically, we used two sets of WiFi antennas to acquire signals, i.e., one transmitter set and one receiver set. Each set contains three antennas horizontally lined-up as a regular household WiFi router. The WiFi signal generated by a transmitter antenna, penetrates through and reflects on human bodies, furniture, and walls, and then superposes at a receiver antenna as 1D signal samples. We developed a deep learning approach that uses annotations on 2D images, takes the received 1D WiFi signals as input, and performs body segmentation and pose estimation in an end-to-end manner. To our knowledge, our solution is the first work based on off-the-shelf WiFi antennas and standard IEEE 802.11n WiFi signals. Demonstrating comparable results to image-based solutions, our WiFi-based person perception solution is cheaper and more ubiquitous than radars and LiDARs, while invariant to illumination and has little privacy concern comparing to cameras.
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle

We introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack –i.e. a data structure with linear-time sequential access– the proposed system uses a set of parsing items, with constant-time random access. This change makes it possible to construct any discontinuous constituency tree in exactly 4n–2 transitions for a sentence of length n. At each parsing step, the parser considers every item in the set to be combined with a focus item and to construct a new constituent in a bottom-up fashion. The parsing strategy is based on the assumption that most syntactic structures can be parsed incrementally and that the set –the memory of the parser– remains reasonably small on average. Moreover, we introduce a provably correct dynamic oracle for the new transition system, and present the first experiments in discontinuous constituency parsing using a dynamic oracle. Our parser obtains state-of-the-art results on three English and German discontinuous treebanks.
Unifying Modular and Core-Periphery Structure in Functional Brain Networks over Development.

At rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core-periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core-periphery structure. Here we leverage a recently-developed model-based approach -- the weighted stochastic block model -- that simultaneously uncovers modular and core-periphery structure, and we apply it to fMRI data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich meso-scale organization beyond that sought by modularity maximization techniques. Moreover, we show that this meso-scale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core-periphery structure in functional brain networks, providing novel insights into their development and implications for behavior.
Using Similarity Measures to Select Pretraining Data for NER

Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks. However, the measure and impact of similarity between pretraining data and target task data are left to intuition. We propose three cost-effective measures to quantify different aspects of similarity between source pretraining and target task data. We demonstrate that these measures are good predictors of the usefulness of pretrained models for Named Entity Recognition (NER) over 30 data pairs. Results also suggest that pretrained LMs are more effective and more predictable than pretrained word vectors, but pretrained word vectors are better when pretraining data is dissimilar.
Notes on the benthic marine algae of Puerto Rico, XI: new records including new Meredithia (Kallymeniaceae, Rhodophyta) species

Abstract Continued collection of algae around the island of Puerto Rico principally focused on deep and shallow coral reef habitats led to the additional recognition of nine species previously unknown for the flora, including five Rhodophyta: Liagora tsengii, Crouania elisiae, Crouania pumila, Augophyllum wysori, Chrysymenia ventricosa; one Heterokontophyta (Phaeophyceae): Zonaria tournefortii; and three Chlorophyta: Bolbocoleon piliferum, Rhipocephalus oblongus, and Udotea caribaea. Kallymenia limminghei as previously reported from Puerto Rico is now recognized to be a distinct entity, Meredithia puchella, and Meredithia? caribaea is also described as a new species.
When Does the Onset of Multiple Stellar Populations in Star Clusters Occur? Detection of Enriched Stellar Populations in NGC 2121

Star-to-star light-element abundance variations, known as multiple stellar populations (MPs), are common in almost all Galactic globular clusters. Recently, MPs have also been detected in a number of massive clusters with ages in excess of 2 Gyr in the Large Magellanic Cloud (LMC), thus indicating that age is likely a control parameter for the presence of MPs. However, to conclusively confirm this notion, additional studies of star clusters in the LMC’s “age gap” of 3–6 Gyr are required. Here, we use Hubble Space Telescope observations to study the 3 Gyr old cluster NGC 2121. Compared with so-called “simple” stellar population models, the cluster’s red-giant branch exhibits an apparent spread in a specific color index that is sensitive to intrinsic chemical spreads. The observed spread can be explained by an intrinsic spread in nitrogen abundance of ∼0.5–1.0 dex. NGC 2121 has a comparable mass to its younger counterparts without MPs, indicating that cluster mass might not be the dominant parameter controlling the presence of MPs. The transition phase between the occurrence of clusters with or without MPs seems to occur at ages of 2–3 Gyr, indicating that cluster age may play a dominant role in the establishment of MPs.
Spin-Orbit Torque Devices for Hardware Security: From Deterministic to Probabilistic Regime

Protecting intellectual property (IP) has become a serious challenge for chip designers. Most countermeasures are tailored for CMOS integration and tend to incur excessive overheads, resulting from additional circuitry or device-level modifications. On the other hand, power density is a critical concern for sub-50 nm nodes, necessitating alternate design concepts. Although initially tailored for error-tolerant applications, imprecise computing has gained traction as a general-purpose design technique. Emerging devices are currently being explored to implement ultralow-power circuits for inexact computing applications. In this paper, we quantify the security threats of imprecise computing using emerging devices. More specifically, we leverage the innate polymorphism and tunable stochastic behavior of spin-orbit torque (SOT) devices, particularly, the giant spin-Hall effect (GSHE) switch. We enable IP protection (by means of logic locking and camouflaging) simultaneously for deterministic and probabilistic computing, directly at the GSHE device level. We conduct a comprehensive security analysis using state-of-the-art Boolean satisfiability (SAT) attacks; this paper demonstrates the superior resilience of our GSHE primitive when tailored for deterministic computing. We also demonstrate how probabilistic computing can thwart most, if not all, existing SAT attacks. Based on this finding, we propose an attack scheme called probabilistic SAT (PSAT) which can bypass the defense offered by logic locking and camouflaging for imprecise computing schemes. Further, we illustrate how careful application of our GSHE primitive can remain secure even on the application of the PSAT attack. Finally, we also discuss side-channel attacks and invasive monitoring, which are arguably even more concerning threats than SAT attacks.
X-ray spectroscopy of current-induced spin-orbit torques and spin accumulation in 
Pt/3d
-transition-metal bilayers

An electric current flowing in Pt, a material with strong spin-orbit coupling, leads to spins accumulating at the interfaces by virtue of the spin Hall effect and interfacial charge-spin conversion. We measure the influence of these interfacial magnetic moments onto adjacent $3d$ transition-metal layers by x-ray absorption spectroscopy and x-ray magnetic circular dichroism in a quantitative and element-selective way, with sensitivity below ${10}^{\ensuremath{-}5}{\ensuremath{\mu}}_{B}$ per atom. In Pt(6 nm)/Co(2.5 nm), the accumulated spins cause a deviation of the Co magnetization direction, which corresponds to an effective spin Hall angle of 0.08. The spin and orbital magnetic moments of Co are affected in equal proportion by the absorption of the spin current, showing that the transfer of orbital momentum from the recently predicted orbital Hall effect is either below our detection limit, or not directed to the $3d$ states of Co. For Pt/NM (NM = Ti, Cr, Cu), we find upper limits for the amount of injected spins corresponding to about $3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}{\ensuremath{\mu}}_{B}$ per atom.
Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK

Disengagement and disenchantment with the Parliamentary process is an important concern in today’s Western democracies. Members of Parliament (MPs) in the UK are therefore seeking new ways to engage with citizens, including being on digital platforms such as Twitter. In recent years, nearly all (579 out of 650) MPs have created Twitter accounts, and have amassed huge followings comparable to a sizable fraction of the country’s population. This paper seeks to shed light on this phenomenon by examining the volume and nature of the interaction between MPs and citizens. We find that although there is an information overload on MPs, attention on individual MPs is focused during small time windows when something topical may be happening relating to them. MPs manage their interaction strategically, replying selectively to UKbased citizens and thereby serving in their role as elected representatives, and using retweets to spread their party’s message. Most promisingly, we find that Twitter opens up new avenues with substantial volumes of cross-party interaction, between MPs of one party and citizens who support (follow) MPs of other parties.
Anticipating the Sun’s heavy-element abundance

Much of our understanding of the internal structure of the Sun derives from so-called standard theoretical solar models. Unfortunately, none of those models agrees completely with observation. The discrepancy is commonly associated with chemical abundance, and has led to what is now called the solar abundance problem, the resolution of which has previously been out of sight. But now the Borexino Collaboration, who recently announced measurements of the pp-chain solar neutrinos, are optimistic that %in future they will be able to measure the flux $\Phi_{\rm CNO}$ of the neutrinos emitted by the relatively weak CNO cycle. Since C, N and O constitute the majority of the heavy elements, that measurement will permit a crucial determination of the heavy-element abundance $Z_{\rm c}$ in the Sun's energy-generating core, thereby shedding important light on the problem. To accomplish that determination, a robust relation between $Z_{\rm c}$ and $\Phi_{\rm CNO}$ will be required. That relation is $Z_{\rm c}= 0.400\, \Phi_{\rm CNO}$, where $\Phi_{\rm CNO}$ is in units of $10^{10} {\rm cm}^{-2}{\rm s}^{-1}$.
Generation of stable oscillations in uncertain nonlinear systems with matched and unmatched uncertainties

ABSTRACT In this paper, a robust limit cycle control technique is proposed for generation of stable oscillations in a class of uncertain nonlinear systems with both matched and unmatched uncertainties. For this purpose, first, the modified Lyapunov function is introduced which is appropriate for stability analysis of invariant sets (instead of equilibrium points). The structure of the proposed Lyapunov function is related to the shape of the desirable limit cycle. Next, in order to design the robust limit cycle control input, the backstepping and Lyapunov redesign methods are employed, simultaneously. The The classical Lyapunov redesign controller is discontinuous and robust with respect to matched uncertainties. To overcome unmatched uncertainties, a modified version of the Lyapunov redesign controller is suggested in each step of backstepping which results in a continuous robust control law. Furthermore, the convergence of the phase trajectories of the uncertain closed-loop system to the target limit cycle is proved using the extended Lyapunov stability theorem. Finally, computer simulations are performed to show the applicability of the given approach. In this regard, two uncertain nonlinear practical systems are considered and robust stable oscillations are generated in these systems via the proposed controller. Simulation results confirm the effectiveness of the proposed technique.
Storage capacity in symmetric binary perceptrons

We study the problem of determining the capacity of the binary perceptron for two variants of the problem where the corresponding constraint is symmetric. We call these variants the rectangle-binary-perceptron (RPB) and the u-function-binary-perceptron (UBP). We show that, unlike for the usual step-function-binary-perceptron, the critical capacity in these symmetric cases is given by the annealed computation in a large region of parameter space (for all rectangular constraints and for narrow enough u-function constraints, K < K*). We prove this fact (under two natural assumptions) using the first and second moment methods. We further use the second moment method to conjecture that solutions of the symmetric binary perceptrons are organized in a so-called frozen-1RSB structure, without using the replica method. We then use the replica method to estimate the capacity threshold for the UBP case when the u-function is wide K > K*. We conclude that full-step-replica-symmetry breaking would have to be evaluated in order to obtain the exact capacity in this case.