Miracle-dz/UniFAR-FTUs huggingface dataset:
Classifying Superconformal Defects in Diverse Dimensions Part I: Superconformal Lines

We initiate the classification of unitary superconformal defects in unitary superconformal field theories (SCFT) of diverse spacetime dimensions $3\leq d \leq 6$. Our method explores general constraints from the defect superconformal symmetry, unitarity, as well as consistency conditions of local bulk-defect couplings. Such features are common to all superconformal defects regardless of any Lagrangian description. In particular, modified Ward identities of conserved currents in the presence of the defect induce a distinguished set of conformal primary operators on the defect worldvolume, which includes the universal displacement operator associated with broken translations transverse to the defect. Consistency with the preserved superconformal symmetry and unitarity requires that such operators arrange into unitarity multiplets of the defect superconformal algebra, which in turn leads to nontrivial constraints on what kinds of defects are admissible in a given SCFT. We carry out the analysis explicitly for one-dimensional defects, namely superconformal lines, and leave the study of higher dimensional defects to forthcoming work. We also comment on the implications of our results for the deformations of superconformal lines and one-form symmetries in SCFTs.
Remote state preparation of two-component Bose-Einstein condensates

A protocol for remote state preparation is proposed for spin ensembles, where the aim is to prepare a state with a given set of spin expectation values on a remote spin ensemble using entanglement, local spin rotations, and measurements in the Fock basis. The spin ensembles could be realized by thermal atomic ensembles or spinor Bose-Einstein condensates. The protocol works beyond the Holstein-Primakoff approximation, such that spin expectation values for the full Bloch sphere can be prepared. The main practical obstacle is the preparation of the maximally entangled state between the spin ensembles. To overcome this, we examine using states based on the two-axis two-spin (2A2S) Hamiltonian in place of the maximally entangled state and examine its performance. We find that the version of the protocol with 2A2S squeezing well-approximates the maximally entangled state, such that spin averages can be remotely prepared. We evaluate the errors of using 2A2S squeezed states, and find that it decreases with the ensemble size. With post-selection, errors can be systematically decreased further.
Elasticity of a DNA under force dotted with bubbles

The bending elastic modulus and the extension along the direction of the force are shown to be related to the bubble number fluctuation and the average number of bubbles respectively, when the strands of the DNA are subjected to a force along the same direction, here we call a stretching force.The force-temperature phase diagram shows the existence of a tri-critical point (TCP), where the first-order force induced zipping transition becomes continuous. On the other hand, when the forces are being applied in opposite directions, here we call an unzipping force, the transition remains first order, with the possibility of vanishing of the low-temperature re-entrant phase for a sufficiently semi-flexible DNA. Moreover, we found that the bulk elasticity changes only if an external force alter the bubble statistics.
Giant Spin Transfer Torque in Atomically Thin Magnetic Bilayers

In cavity quantum electrodynamics, the multiple reflections of a photon between two mirrors defining a cavity is exploited to enhance the light-coupling of an intra-cavity atom. We show that this paradigm for enhancing the interaction of a flying particle with a localized object can be generalized to spintronics based on van der Waals 2D magnets. Upon tunneling through a magnetic bilayer, we find the spin transfer torques per electron incidence can become orders of magnitude larger than $\hbar/2$, made possible by electron's multi-reflection path through the ferromagnetic monolayers as an intermediate of their angular momentum transfer. Over a broad energy range around the tunneling resonances, the damping-like spin transfer torque per electron tunneling features a universal value of $\frac{\hbar}{2} \tan{\frac{\theta}{2}}$, depending only on the angle $\theta$ between the magnetizations. These findings expand the scope of magnetization manipulations for high-performance and high-density storage based on van der Waals magnets.
3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation

In this paper, we focus on estimating the 6D pose of objects in point clouds. Although the topic has been widely studied, pose estimation in point clouds remains a challenging problem due to the noise and occlusion. To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses. 3DPVNet is comprised of three modules. In particular, a Patch Unification (\textbf{PU}) module is first introduced to normalize the input patch, and also create a standard local coordinate frame on it to generate a reliable vote. We then devise a Weight-guided Neighboring Feature Fusion (\textbf{WNFF}) module in the network, which fuses the neighboring features to yield a semi-global feature for the center patch. WNFF module mines the neighboring information of a local patch, such that the representation capability to local geometric characteristics is significantly enhanced, making the method robust to a certain level of noise. Moreover, we present a Patch-level Voting (\textbf{PV}) module to regress transformations and generates pose votes. After the aggregation of all votes from patches and a refinement step, the final pose of the object can be obtained. Compared to recent voting-based methods, 3DPVNet is patch-level, and directly carried out on point clouds. Therefore, 3DPVNet achieves less computation than point/pixel-level voting scheme, and has robustness to partial data. Experiments on several datasets demonstrate that 3DPVNet achieves the state-of-the-art performance, and is also robust against noise and occlusions.
Microscope Based HER2 Scoring System

The overexpression of human epidermal growth factor receptor 2 (HER2) has been established as a therapeutic target in multiple types of cancers, such as breast and gastric cancers. Immunohistochemistry (IHC) is employed as a basic HER2 test to identify the HER2-positive, borderline, and HER2-negative patients. However, the reliability and accuracy of HER2 scoring are affected by many factors, such as pathologists' experience. Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem. In this paper, we propose a real-time HER2 scoring system, which follows the HER2 scoring guidelines to complete the diagnosis, and thus each step is explainable. Unlike the previous scoring systems based on whole-slide imaging, our HER2 scoring system is integrated into an augmented reality (AR) microscope that can feedback AI results to the pathologists while reading the slide. The pathologists can help select informative fields of view (FOVs), avoiding the confounding regions, such as DCIS. Importantly, we illustrate the intermediate results with membrane staining condition and cell classification results, making it possible to evaluate the reliability of the diagnostic results. Also, we support the interactive modification of selecting regions-of-interest, making our system more flexible in clinical practice. The collaboration of AI and pathologists can significantly improve the robustness of our system. We evaluate our system with 285 breast IHC HER2 slides, and the classification accuracy of 95\% shows the effectiveness of our HER2 scoring system.
Competing AI: How competition feedback affects machine learning

This papers studies how competition affects machine learning (ML) predictors. As ML becomes more ubiquitous, it is often deployed by companies to compete over customers. For example, digital platforms like Yelp use ML to predict user preference and make recommendations. A service that is more often queried by users, perhaps because it more accurately anticipates user preferences, is also more likely to obtain additional user data (e.g. in the form of a Yelp review). Thus, competing predictors cause feedback loops whereby a predictor's performance impacts what training data it receives and biases its predictions over time. We introduce a flexible model of competing ML predictors that enables both rapid experimentation and theoretical tractability. We show with empirical and mathematical analysis that competition causes predictors to specialize for specific sub-populations at the cost of worse performance over the general population. We further analyze the impact of predictor specialization on the overall prediction quality experienced by users. We show that having too few or too many competing predictors in a market can hurt the overall prediction quality. Our theory is complemented by experiments on several real datasets using popular learning algorithms, such as neural networks and nearest neighbor methods.
Quantum no-signalling correlations and non-local games

We introduce and examine three subclasses of the family of quantum no-signalling (QNS) correlations introduced by Duan and Winter: quantum commuting, quantum and local. We formalise the notion of a universal TRO of a block operator isometry, define an operator system, universal for stochastic operator matrices, and realise it as a quotient of a matrix algebra. We describe the classes of QNS correlations in terms of states on the tensor products of two copies of the universal operator system, and specialise the correlation classes and their representations to classical-to-quantum correlations. We study various quantum versions of synchronous no-signalling correlations and show that they possess invariance properties for suitable sets of states. We introduce quantum non-local games as a generalisation of non-local games. We define the operation of quantum game composition and show that the perfect strategies belonging to a certain class are closed under channel composition. We specialise to the case of graph colourings, where we exhibit quantum versions of the orthogonal rank of a graph as the optimal output dimension for which perfect classical-to-quantum strategies of the graph colouring game exist, as well as to non-commutative graph homomorphisms, where we identify quantum versions of non-commutative graph homomorphisms introduced by Stahlke.
Unveiling Sub-pc Supermassive Black Hole Binary Candidates in Active Galactic Nuclei

The elusive supermassive black hole binaries (SMBHBs) are thought to be the penultimate stage of galaxy mergers, preceding a final coalescence phase. SMBHBs are sources of continuous gravitational waves, possibly detectable by pulsar timing arrays; the identification of candidates could help in performing targeted gravitational wave searches. Due to SMBHBs’ origin in the innermost parts of active galactic nuclei (AGN), X-rays are a promising tool for unveiling their presence, by means of either double Fe Kα emission lines or periodicity in their light curve. Here we report on a new method for selecting SMBHBs by means of the presence of a periodic signal in their Swift Burst Alert Telescope (BAT) 105 month light curves. Our technique is based on Fisher’s exact g-test and takes into account the possible presence of colored noise. Among the 553 AGN selected for our investigation, only the Seyfert 1.5 galaxy Mrk 915 emerges as a candidate SMBHB; from subsequent analysis of its light curve we find a period P0 = 35 ± 2 months, and the null hypothesis is rejected at the 3. 7σ confidence level. We also present a detailed analysis of the BAT light curve of the only previously X-ray-selected binary candidate source in the literature, the Seyfert 2 galaxy MCG+11-11-032. We find P0 = 26.3 ± 0.6 months, consistent with the one inferred from previously reported double Fe Kα emission lines.
Hierarchical coarse-grained approach to the duration-dependent spreading dynamics on complex networks

Various coarse-grained models have been proposed to study the spreading dynamics on complex networks. A microscopic theory is needed to connect the spreading dynamics with individual behaviors. In this letter, we unify the description of different spreading dynamics by decomposing the microscopic dynamics into two basic processes, the aging process and the contact process. A hierarchical duration coarse-grained (DCG) approach is proposed to study the duration-dependent processes. Applied to the epidemic spreading, such formalism is feasible to reproduce different epidemic models, e.g., the SIS and the SIR models, and to associate the macroscopic spreading parameters with the microscopic mechanism. The DCG approach enables us to study the steady state of the duration-dependent SIS model. The current hierarchical formalism can also be used to describe the spreading of information and public opinions, or to model a reliability theory on networks.
Interpretable and Interactive Summaries of Actionable Recourses

As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to analyse and interpret a predictive model, and vet it thoroughly to ensure that the recourses it offers are meaningful and non-discriminatory before it is deployed in the real world. To this end, we propose a novel model agnostic framework called Actionable Recourse Summaries (AReS) to construct global counterfactual explanations which provide an interpretable and accurate summary of recourses for the entire population. We formulate a novel objective which simultaneously optimizes for correctness of the recourses and interpretability of the explanations, while minimizing overall recourse costs across the entire population. More specifically, our objective enables us to learn, with optimality guarantees on recourse correctness, a small number of compact rule sets each of which capture recourses for well defined subpopulations within the data. Our framework is also interactive i.e., it allows users to input specific features of interest which will in turn be used to characterize subpopulations when generating recourse summaries. We also demonstrate theoretically that several of the prior approaches proposed to generate recourses for individuals are special cases of our framework. Experimental evaluation with real world datasets and user studies demonstrate that our framework can provide decision makers with a comprehensive overview of recourses corresponding to any black box model, and consequently help detect undesirable model biases and discrimination.
VVV Survey Microlensing: Candidate Events with a Source in the Far Disk

The VVV microlensing search has recently surveyed the region of the Galactic plane at b = 0 within −10.00 ≤ l ≤ 10.44 deg. in the near-infrared (IR) discovering hundreds of microlensing events. In this paper we explore the microlensing events with background sources that might be located in the far disk of the Galaxy, beyond the bulge. We discuss the possible configurations for the microlensing lenses and sources located at different places within the Galactic plane. Then we search for these events using the local red clump centroids of the VVV near-IR color–magnitude diagrams. According to the estimated distances and proper motions, N = 20 events may have sources located in the far disk. The candidates for far-disk sources show on average longer timescales (t E = 49.3 ± 7.9 days) than the mean of the timescale distribution for the bulge red clump sources (t E = 36.4 ± 1.1 days). We conclude that the population of microlensing events in the region −10.00 ≤ l ≤ 10.44, −0.46 ≤ b ≤ 0.65 deg. contains a non-negligible number of events with candidate far-disk sources (∼11%). Our results are relevant in view of future microlensing plans with the Roman Space Telescope (formerly WFIRST) in the near-IR.
Model Light Curves for Type Ib and Ic Supernovae

Using the Monte Carlo code SEDONA, multiband photometry and spectra are calculated for supernovae derived from stripped helium stars with presupernova masses of 2.2 to 10.0 M ⊙. The models are representative of evolution in close binaries and have previously been exploded using a parameterized one-dimensional model for neutrino transport. A subset, those with presupernova masses in the range of 2.2–5.6 M ⊙, have many properties in common with observed Type Ib and Ic supernovae, including a median ejected mass near 2 M ⊙, explosion energies near 1 × 1051 erg, typical 56Ni masses of 0.07–0.09 M ⊙, peak times of about 20 days, and a narrow range for the V − R color index 10 days post-V-maximum near 0.3 mag. The median peak bolometric luminosity, near 1042.3 erg s−1, is fainter, however, than several observational tabulations, and the brightest explosion has a bolometric luminosity of only 1042.50 erg s−1. The brightest absolute B, V, and R magnitudes at peak are −17.2, −17.8, and −18.0. These limits are fainter than some allegedly typical Type Ib and Ic supernovae and could reflect problems in our models or in the observational analysis. Helium stars with lower and higher masses also produce interesting transients that may have been observed, including fast, faint, blue transients and long, red, faint Type Ic supernovae. New models are specifically presented for SN 2007Y, SN 2007gr, SN 2009jf, LSQ 13abf, SN 2008D, and SN 2010X.
OGLE-GAL-ACEP-091: The First Known Multi-mode Anomalous Cepheid

Anomalous Cepheids (ACs) are metal-deficient, core-helium-burning pulsating stars with masses in the range 1.2–2.2 . Until recently, all known ACs were pure single-mode pulsators. The first candidate for an AC pulsating in more than one radial mode—OGLE-GAL-ACEP-091—was recently identified in the Milky Way based on the photometric database of the Optical Gravitational Lensing Experiment (OGLE) survey. We analyze this object showing that it is actually a triple-mode pulsator. Its position in the Petersen diagram, the light-curve morphology quantified by Fourier coefficients, and absolute magnitudes derived from the Gaia parallax are consistent with the assumption that OGLE-GAL-ACEP-091 is an AC. Our grid of linear pulsation models indicates that OGLE-GAL-ACEP-091 is a 1.8 star with a metallicity of about [Fe/H] = −0.5 dex.
CHEMICAL CHARACTERIZATION AND ANTIMICROBIAL ACTIVITY OF HYDROETHANOLIC CRUDE EXTRACT OF EUGENIA FLORIDA DC (MYRTACEAE) LEAVES

Objective: The present study aimed to characterize and quantify the total phenolics, flavonoids and tannins in a hydroethanolic crude extract (70% (v/v) (EB)) of the leaves of E. florida DC, as well as to evaluate the antimicrobial activity of the extract against different species of micro-organisms. Methods: EB was characterized using a mass spectrometer equipped with a direct insertion device for in-stream injection (FIA). Quantitative analyses of major compounds were carried out by spectrophotometry. In addition, we evaluated the sensitivity profiles of different strains of yeast and bacteria against different concentrations of EB. Results: The classes found were in agreement with those described in the literature: flavonoids, tannins, phenolic acids and saponins. EB showed levels of phenolic compounds, flavonoids and tannins equal to 25.82 mg gallic acid equivalents per gram of extract (EAG/g), 8.42 mg quercetin equivalents per gram of extract (EQ/g) and 7.30 mg tannic acid equivalents per gram of extract (AT/g), respectively. In the analysis of antimicrobial activity, EB was more active against yeasts but was not effective against the bacteria used in the test. Conclusion: We can conclude that E. florida DC has antimicrobial potential, due to the presence of bioactive secondary metabolites. Keywords: Phenolics, Micro-organisms, Mass spectrometer, Yeasts, Bacteria, Potential
CoVR: A Large-Scale Force-Feedback Robotic Interface for Non-Deterministic Scenarios in VR

We present CoVR, a novel robotic interface providing strong kinesthetic feedback (100 N) in a room-scale VR arena. It consists of a physical column mounted on a 2D Cartesian ceiling robot (XY displacements) with the capacity of (1) resisting to body-scaled users' actions such as pushing or leaning; (2) acting on the users by pulling or transporting them as well as (3) carrying multiple potentially heavy objects (up to 80kg) that users can freely manipulate or make interact with each other. We describe its implementation and define a trajectory generation algorithm based on a novel user intention model to support non-deterministic scenarios, where the users are free to interact with any virtual object of interest with no regards to the scenarios' progress. A technical evaluation and a user study demonstrate the feasibility and usability of CoVR, as well as the relevance of whole-body interactions involving strong forces, such as being pulled through or transported.
AMRNet: Chips Augmentation in Areial Images Object Detection

Object detection in aerial images is a challenging task due to the following reasons: (1) objects are small and dense relative to images; (2) the object scale varies in a wide range; (3) the number of object in different classes is imbalanced. Many current methods adopt cropping idea: splitting high resolution images into serials subregions (chips) and detecting on them. However, some problems such as scale variation, object sparsity, and class imbalance exist in the process of training network with chips. In this work, three augmentation methods are introduced to relieve these problems. Specifically, we propose a scale adaptive module, which dynamically adjusts chip size to balance object scale, narrowing scale variation in training. In addtion, we introduce mosaic to augment datasets, relieving object sparity problem. To balance catgory, we present mask resampling to paste object in chips with panoramic segmentation. Our model achieves state-of-the-art perfomance on two popular aerial image datasets of VisDrone and UAVDT. Remarkably, three methods can be independently applied to detectiors, increasing performance steady without the sacrifice of inference efficiency.
Switching Gradient Directions for Query-Efficient Black-Box Adversarial Attacks

We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance under $\ell_2$ and $\ell_\infty$ norms in the score-based setting. In the black box attack setting, designing query-efficient attacks remains an open problem. The high query efficiency of the proposed approach stems from the combination of transfer-based attacks and random-search-based ones. The surrogate model's gradient $\hat{\mathbf{g}}$ is exploited for the guidance, which is then switched if our algorithm detects that it does not point to the adversarial region by using a query, thereby keeping the objective loss function of the target model rising as much as possible. Two switch operations are available, i.e., SWITCH$_\text{neg}$ and SWITCH$_\text{rnd}$. SWITCH$_\text{neg}$ takes $-\hat{\mathbf{g}}$ as the new direction, which is reasonable under an approximate local linearity assumption. SWITCH$_\text{rnd}$ computes the gradient from another model, which is randomly selected from a large model set, to help bypass the potential obstacle in optimization. Experimental results show that these strategies boost the optimization process whereas following the original surrogate gradients does not work. In SWITCH, no query is used to estimate the gradient, and all the queries aim to determine whether to switch directions, resulting in unprecedented query efficiency. We demonstrate that our approach outperforms 10 state-of-the-art attacks on CIFAR-10, CIFAR-100 and TinyImageNet datasets. SWITCH can serve as a strong baseline for future black-box attacks. The PyTorch source code is released in this https URL .