Miracle-dz/UniFAR-FTUs huggingface dataset:
On short GRBs similar to GRB 170817A detected by Fermi-GBM

Von Kienlin et al. (2019) selected 11 short gamma-ray bursts (sGRBs) whose characteristics are similar to GRB 170817A. These bursts, like GRB 170817A, have a hard spike followed by a soft thermal tail. However, as their redshifts are unknown it is not clear if their luminosities are as low as that of GRB 170817A. Comparing the positions in the ϵp–Eγ,iso (spectral peak energy – isotropic-equivalent energy) plane and using compactness arguments to estimate the minimal Lorentz factor, Γ, we find that all the bursts in this sample are consistent with being regular sGRBs if they are located at $z$ ≃ 0.3–3. They are also consistent with being similar to GRB 170817A if they are located at $z$ ≲ 0.1. Even in the latter case, the events must involve at least mildly relativistic (Γ ≳ 2) motion within the sources. We, further, find that at most one or two bursts in the sample are consistent with the cocoon shock-breakout model. Finally, we calculate the event rate of off-axis emission either from a jet core or from a jet wing (surrounding the core). We find that the off-axis emission model as an origin of the sample is rejected as it predicts too small event rate. The wing model can be consistent with the observed rate but the model parameters cannot be constrained by the current observations.
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems

Automatic speech recognition (ASR) systems can be fooled via targeted adversarial examples, which induce the ASR to produce arbitrary transcriptions in response to altered audio signals. However, state-of-the-art adversarial examples typically have to be fed into the ASR system directly, and are not successful when played in a room. Previously published over-the-air adversarial examples fall into one of three categories: they are either handcrafted examples, they are so conspicuous that human listeners can easily recognize the target transcription once they are alerted to its content, or they require precise information about the room where the attack takes place, and are hence not transferable to other rooms. In this paper, we demonstrate the first algorithm that produces generic adversarial examples against hybrid ASR systems, which remain robust in an over-the-air attack that is not adapted to the specific environment. Hence, no prior knowledge of the room characteristics is required. Instead, we use room impulse responses (RIRs) to compute robust adversarial examples for arbitrary room characteristics and employ the ASR system Kaldi to demonstrate the attack. Further, our algorithm can utilize psychoacoustic methods to hide changes of the original audio signal below the human thresholds of hearing. In practical experiments, we show that the adversarial examples work for varying room setups, and that no direct line-of-sight between speaker and microphone is necessary. As a result, an attacker can create inconspicuous adversarial examples for any target transcription and apply these to arbitrary room setups without any prior knowledge.
Understanding Bias in Machine Learning

Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.
Very long baseline interferometry observation of the triple AGN candidate J0849+1114

Context. In the hierarchical structure formation model, galaxies grow through various merging events. Numerical simulations indicate that mergers can enhance the activity of central supermassive black holes in galaxies. Aims. A system of three interacting galaxies, called J0849+1114, has recently been identified and multi-wavelength evidence of all three galaxies containing active galactic nuclei has recently been found. The system has substantial radio emission; we aim to investigate the origin of this radio emission with a high-resolution radio interferometric observation and to discover whether it is related to star formation or to one or more of the active galactic nuclei in the system. Methods. We performed high-resolution continuum observation of J0849+1114 with the European Very Long Baseline Interferometry Network at 1.7 GHz. Results. We detected one compact radio emitting source at the position of the easternmost nucleus. Its high brightness temperature and radio power indicate that the radio emission originates from a radio-emitting active galactic nucleus. Additionally, we found that significant amount of flux density is contained in ∼100 milliarcsec-scale feature related to the active nucleus.
Compact Star-forming Galaxies as Old Starbursts Becoming Quiescent

Optically compact star-forming galaxies (SFGs) have been proposed as immediate progenitors of quiescent galaxies, although their origin and nature are debated. Were they formed in slow secular processes or in rapid merger-driven starbursts? Answering this question would provide fundamental insight into how quenching occurs. We explore the location of the general population of galaxies with respect to fundamental star-forming and structural relations, identify compact SFGs based on their stellar core densities, and study three diagnostics of the burstiness of star formation: (1) star formation efficiency, (2) interstellar medium (ISM), and (3) radio emission. The overall distribution of galaxies in the fundamental relations points toward a smooth transition toward quiescence while galaxies grow their stellar cores, although some galaxies suddenly increase their specific star formation rate when they become compact. From their star formation efficiencies compact and extended SFGs appear similar. In relation to the ISM diagnostic, study of the CO excitation, the density of the neutral gas, and the strength of the ultraviolet radiation field shows that compact SFGs resemble galaxies located in the upper envelope of the main sequence of SFGs, although this is based on a small sample size. Regarding the radio emission diagnostic, we find that galaxies become increasingly compact as the starburst ages, implying that at least some compact SFGs are old starbursts. We suggest that compact SFGs could be starbursts winding down and eventually crossing the main sequence toward quiescence.
Constraints on the mass–concentration relation of cold dark matter haloes with 11 strong gravitational lenses

The mass–concentration relation of dark matter haloes reflects the assembly history of objects in hierarchical structure formation scenarios and depends on fundamental quantities in cosmology such as the slope of the primordial matter power spectrum. This relation is unconstrained by observations on sub-galactic scales. We derive the first measurement of the mass–concentration relation using the image positions and flux ratios from 11 quadruple-image strong gravitational lenses (quads) in the mass range $10^{6}\! -\! 10^{10} {\, \mathrm{M}_{\odot }}$, assuming cold dark matter. We model both subhaloes and line-of-sight haloes, finite-size background sources, and marginalize over nuisance parameters describing the lens macromodel. We also marginalize over the logarithmic slope and redshift evolution of the mass–concentration relation, using flat priors that encompass the range of theoretical uncertainty in the literature. At z = 0, we constrain the concentration of $10^{8} \, \mathrm{M}_{\odot }$ haloes $c=12_{-5}^{+6}$ at $68 {{\ \rm per\ cent}}$ CI, and $c=12_{-9}^{+15}$ at $95 {{\ \rm per\ cent}}$ CI. For a $10^{7} {\, \mathrm{M}_{\odot }}$ halo, we obtain $68 {{\ \rm per\ cent}}$ ($95 {{\ \rm per\ cent}}$) constraints $c=15_{-8}^{+9}$ ($c=15_{-11}^{+18}$), while for $10^{9} \, \mathrm{M}_{\odot }$ haloes $c=10_{-4}^{+7}$ ($c=10_{-7}^{+14}$). These results are consistent with the theoretical predictions from mass–concentration relations in the literature and establish strong lensing by galaxies as a powerful probe of halo concentrations on sub-galactic scales across cosmological distance.
31. SWITCHING BIOLOGICS AFTER INITIAL ANTI-TUMOUR NECROSIS FACTOR-α FAILURE; SUCCESS WITH FURTHER TNF-α ANTAGONISM BUT FAILURE ON SUBSEQUENT INTERLEUKIN-1 RECEPTOR ANTAGONIST

11 , R. Pedersen 11 Background: The purpose was to evaluate the effect of concurrent start of E, MTX, and E+MTX on structural joint damage assessed by radiography in patients with active RA with disease duration up to 20 years. Methods: Double-blind, parallel-group global study in 686 patients, 642 of whom were included in radiographic analysis, were randomly assigned to 1 of 3 treatment groups: E 25 mg twice weekly, MTX up to 20 mg, or E+MTX. Primary radiographic endpoint was change from baseline in van der Heijde- modified total Sharp scores (TSS) at 52 weeks. All films were scored by 2 observers blinded for sequence of films, treatment, and patient identity. Results: Primary endpoints of the study were achieved. Eighty-five per- cent (85%), 69%, and 43% of patients on E+MTX therapy achieved ACR20, ACR50, and ACR70, respectively, compared with 75%, 43%, and 19% on MTX and 76%, 48%, and 24% on E therapy, respectively. Change from base- line in TSS at week 52 was significantly lower for patients in the E+MTX and E groups compared with patients in the MTX group. Patients treated with E+MTX had less progression than patients treated with either monotherapy alone. Therapy with E+MTX resulted in statistically significant lowering of TSS compared with baseline with the entire 95% confidence interval below zero. Mean 1.12 0.32 -0.23b,c Median 0.00 0.00 0.00 % of Patients with no progression (TSS <=0.5) 57.1 67.9a 79.8b,c a=p<0.05 E vs M; b=p<0.01 C vs M; c=p<0.01 C vs E
Expert-Level Atari Imitation Learning from Demonstrations Only

One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space. This problem is much more severe in high-dimensional state environments, such as game playing with raw pixel inputs. Under this situation, even state-of-the-art adversary based imitation learning algorithms fail. Through theoretical and empirical studies, we find that the main cause lies in the failure of training a powerful discriminator to generate meaningful rewards in high-dimensional environments. Theoretical results are provided to suggest the necessity of dimensionality reduction. However, since preserving important discriminative information via feature transformation is a non-trivial task, a straightforward application of off-the-shelf methods cannot achieve desirable performance. To address the above issues, we propose HashReward, which is a novel imitation learning algorithm utilizing the idea of supervised hashing to realize effective training of the discriminator. As far as we are aware, HashReward is the first pure imitation learning approach to achieve expert comparable performance in Atari game environments with raw pixel inputs.
An Evaluation of BBR and its variants

The congestion control algorithm bring such importance that it avoids the network link into severe congestion and guarantees network normal operation. Since The loss based algorithms introduce high transmission delay, to design new algorithm simultaneously achieving high throughout and low buffer occupation is a new working direction. The bottleneck bandwidth and round trip time (BBR) belongs such kind, and it has drawn much attention since its release. There are other algorithms modified from BBR to gain better performance. And the implementation of BBR v2.0 is released recently. We implement a framework to compare the performance of these algorithms in simulated environment.
WDVV-type relations for Welschinger invariants: Applications

We first recall Solomon's relations for Welschinger's invariants counting real curves in real symplectic fourfolds, announced in \cite{Jake2} and established in \cite{RealWDVV}, and the WDVV-style relations for Welschinger's invariants counting real curves in real symplectic sixfolds with some symmetry established in \cite{RealWDVV3}. We then explicitly demonstrate that in some important cases (projective spaces with standard conjugations, real blowups of the projective plane, and two- and three-fold products of the one-dimensional projective space with two involutions each), these relations provide complete recursions determining all Welschinger's invariants from basic input. We include extensive tables of Welschinger's invariants in low degrees obtained from these recursions with {\it Mathematica}. These invariants provide lower bounds for counts of real rational curves, including with curve insertions in smooth algebraic threefolds.
Virtual patient simulation: Promotion of clinical reasoning abilities of medical students

Objectives: Virtual patient simulation (VPS) is used in the education of health care professionals. This method brings an opportunity for the learner to examine necessary diagnostic and therapeutic skills. The aim of this study was to assess the efficacy of VPS on clinical reasoning abilities of medical students and to evaluate their attitude towards VPS in clinical endocrinology course in a teacher centered educational environment. Methods: Fifty-one medical students in their 6th academic year were simply randomized in two groups, the simulation and the control. The students in the simulation group were provided by an application which presented them virtual case scenarios on diagnosis and management of thyroid nodules and osteomalacia. All the students sat for a diagnostic test at the beginning and at the end of the course. The test comprised a series of essay questions matched for their academic level and closely related to the case scenarios. They were also asked to complete a questionnaire to assess their attitude towards the application. Results: Participants in both groups did not have any statistical differences in scientific background in basic sciences (P=0.672) and prior clinical examinations (P=0.376). At completion of the course the mean score of the students in the simulation group improved significantly compared to the students in the control group by 3.5 vs. 1.0 points (P=0.001). The students found the application worthful and showed a positive attitude towards it. Conclusions: Virtual patient simulation improved clinical reasoning abilities of medical students in the context of a traditional teaching/learning environment. https://doi.org/10.34105/j.kmel. 2012.04. 037
Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively learn a global model by sharing local updates on the model parameters rather than their datasets, with the help of a mobile base station (MBS). We optimize the resource allocation among MUs to reduce the communication latency in learning iterations. Observing that the performance in this centralized setting is limited due to the distance of the cell-edge users to the MBS, we introduce small cell base stations (SBSs) orchestrating FEEL among MUs within their cells, and periodically exchanging model updates with the MBS for global consensus. We show that this hierarchical federated learning (HFL) scheme significantly reduces the communication latency without sacrificing the accuracy.
A Private and Unlinkable Message Exchange Using a Public bulletin board in Opportunistic Networks

We plan to simulate a private and unlinkable exchange of messages by using a Public bulletin board and Mix networks in Opportunistic networks. This Opportunistic network uses a secure and privacy-friendly asynchronous unidirectional message transmission protocol. By using this protocol, we create a Public bulletin board in a network that makes individuals send or receive events unlinkable to one another . With the design of a Public bulletin board in an Opportunistic network, the clients can use the benefits of this Public bulletin board in a safe environment. When this Opportunistic network uses the protocol, it can guarantee an unlinkable communication based on the Mix networks. The protocol can work with the Public bulletin board exclusively with acceptable performance. Also, this simulation can be used for hiding metadata in the bidirectional message exchange in some messengers such as WhatsApp. As we know, one of the main goals of a messenger like WhatsApp is to protect the social graph. By using this protocol, a messenger can protect social graph and a central Public bulletin board.
Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network

Enriching existing medical terminology knowledge bases (KBs) is an important and never-ending work for clinical research because new terminology alias may be continually added and standard terminologies may be newly renamed. In this paper, we propose a novel automatic terminology enriching approach to supplement a set of terminologies to KBs. Specifically, terminology and entity characters are first fed into pre-trained language model to obtain semantic embedding. The pre-trained model is used again to initialize the terminology and entity representations, then they are further embedded through graph convolutional network to gain structure embedding. Afterwards, both semantic and structure embeddings are combined to measure the relevancy between the terminology and the entity. Finally, the optimal alignment is achieved based on the order of relevancy between the terminology and all the entities in the KB. Experimental results on clinical indicator terminology KB, collected from 38 top-class hospitals of Shanghai Hospital Development Center, show that our proposed approach outperforms baseline methods and can effectively enrich the KB.
The Quantum Null Energy Condition and Entanglement Entropy in Quenches

The Quantum Null Energy Condition (QNEC) relates energy to the second variation of entropy in relativistic quantum field theory. We use the QNEC inequality to bound entanglement entropy in quenches. At early times the entanglement entropy grows quadratically in time, and the QNEC provides an upper bound on the prefactor. We demonstrate that the bound is tight, by showing that it is saturated in certain quench protocols: boundary state quenches in conformal field theories in any dimensions. In higher than two dimensions we compute entanglement entropy using AdS/CFT. Our results are the first purely field theoretic applications of the QNEC.
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. And, unfortunately, unsupervised topic models often fail to capture the aspects of interest. In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest. First, we show that current weakly supervised approaches fail to leverage the predictive power of seed words for aspect detection. Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings). Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models. Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews.
An Improved Neural Baseline for Temporal Relation Extraction

Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data. Consequently, neural approaches have not been widely used on it, or showed only moderate improvements. This paper proposes a new neural system that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets. The proposed system is trained on the state-of-the-art MATRES dataset and applies contextualized word embeddings, a Siamese encoder of a temporal common sense knowledge base, and global inference via integer linear programming (ILP). We suggest that the new approach could serve as a strong baseline for future research in this area.
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems

The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computational imaging is the optimal transport framework. Its ability to model geometric displacements across an image's support gives it attractive qualities similar to those of optical flow methods which are effective at capturing visual motion, but are restricted to operate in significantly smaller state-spaces. Despite this advantage, two major drawbacks make it unsuitable for general deployment: (i) it suffers from exorbitant computational costs due to a quadratic optimization-variable complexity, and (ii) it has a mass-balancing assumption that limits applications with natural images. We tackle these issues simultaneously by proposing a novel formulation for an unbalanced optimal transport regularizer that has linear optimization-variable complexity. In addition, we present a general parallelizable proximal method for this regularizer, and demonstrate superior empirical performance on novel dynamical tracking applications in synthetic and real video.
Sub-60 mV/decade switching with a cold metal as the injection source

Power dissipation is a great challenge for the continuous scaling down and performance improvement of CMOS technology, due to thermionic current switching limit of conventional MOSFETs. In this work, we show that this problem can be overcome by using cold metals as the transistor's injection source, which are different from conventional metals and can filter high energy electrons to break the Boltzmann tyranny. It is proved that the subthreshold swing (SS) of thermionic current of transistor using cold metal contact can be extremely smaller than 60 mV/decade at room temperature. Specifically, two-dimensional (2D) transition metal chalcogenide (TMD) cold metals of NbX$_2$ and TaX$_2$(X=S, Se, Te) are proposed as the injection source of FETs. Quantum transport simulations indicate that promising switching efficiency and on-state current can be achieved using TMD cold metal injection source, which is beneficial for energy efficient applications.