Miracle-dz/UniFAR-FTUs huggingface dataset:
Quadrupolar spin liquid, octupolar Kondo coupling, and odd-frequency superconductivity in an exactly solvable model

We propose an exactly solvable model for $j_{\text{eff}}=\frac32$ local moments on the honeycomb lattice. Our construction is guided by a symmetry analysis and by the requirement of an exact solution in terms of a Majorana fermion representation for multipole operators. The main interaction in the model can be interpreted as a bond-dependent quadrupole-quadrupole interaction. When time reversal symmetry is explicitly broken, we obtain a gapped spin liquid with a single chiral Majorana edge mode. We also investigate another solvable model in which the time-reversal-invariant spin liquid is coupled to conduction electrons in a superconductor. In the presence of a Kondo-like coupling that involves the octupole moment of the localized spins, the itinerant electrons hybridize with the emergent Majorana fermions in the spin liquid. This leads to spontaneous time reversal symmetry breaking and generates odd-frequency pairing. Our results suggest that $j_{\text{eff}}=\frac32$ systems with strong quadrupole-quadrupole interactions may provide a route towards non-Abelian quantum spin liquids and unconventional superconductivity.
ManifoldNorm: Extending normalizations on Riemannian Manifolds

Many measurements in computer vision and machine learning manifest as non-Euclidean data samples. Several researchers recently extended a number of deep neural network architectures for manifold valued data samples. Researchers have proposed models for manifold valued spatial data which are common in medical image processing including processing of diffusion tensor imaging (DTI) where images are fields of $3\times 3$ symmetric positive definite matrices or representation in terms of orientation distribution field (ODF) where the identification is in terms of field on hypersphere. There are other sequential models for manifold valued data that recently researchers have shown to be effective for group difference analysis in study for neuro-degenerative diseases. Although, several of these methods are effective to deal with manifold valued data, the bottleneck includes the instability in optimization for deeper networks. In order to deal with these instabilities, researchers have proposed residual connections for manifold valued data. One of the other remedies to deal with the instabilities including gradient explosion is to use normalization techniques including {\it batch norm} and {\it group norm} etc. But, so far there is no normalization techniques applicable for manifold valued data. In this work, we propose a general normalization techniques for manifold valued data. We show that our proposed manifold normalization technique have special cases including popular batch norm and group norm techniques. On the experimental side, we focus on two types of manifold valued data including manifold of symmetric positive definite matrices and hypersphere. We show the performance gain in one synthetic experiment for moving MNIST dataset and one real brain image dataset where the representation is in terms of orientation distribution field (ODF).
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models

Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting. In this paper we define and examine these two nonidentifiabilities, dubbed subspace nonidentifiability and model-based nonidentifiability, in the context of random graph inference. We give examples where each type of nonidentifiability comes into play, and we show how in certain settings one need worry about one or the other type of nonidentifiability. Then, we characterize the limit for model-based nonidentifiability both with and without subspace nonidentifiability. We further obtain additional limiting results for covariances and $U$-statistics of stochastic block models and generalized random dot product graphs.
Retrograde-rotating Exoplanets Experience Obliquity Excitations in an Eccentricity-enabled Resonance

Previous studies have shown that planets that rotate retrograde (backward with respect to their orbital motion) generally experience less severe obliquity variations than those that rotate prograde (the same direction as their orbital motion). Here, we examine retrograde-rotating planets on eccentric orbits and find a previously unknown secular spin–orbit resonance that can drive significant obliquity variations. This resonance occurs when the frequency of the planet’s rotation axis precession becomes commensurate with an orbital eigenfrequency of the planetary system. The planet’s eccentricity enables a participating orbital frequency through an interaction in which the apsidal precession of the planet’s orbit causes a cyclic nutation of the planet’s orbital angular momentum vector. The resulting orbital frequency follows the relationship , where and are the rates of the planet’s changing longitude of periapsis and longitude of ascending node, respectively. We test this mechanism by simulating cases of a simple Earth–Jupiter system and confirm the predicted resonance. Over the course of 100 Myr, the test Earths with rotation axis precession rates near the predicted resonant frequency experienced pronounced obliquity variations of the order of 10°–30°. These variations can be significant and suggest that while retrograde rotation is a stabilizing influence most of the time, retrograde rotators can experience large obliquity variations if they are on eccentric orbits and enter this spin–orbit resonance.
Regularizing Class-Wise Predictions via Self-Knowledge Distillation

Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of the same label during training. This results in regularizing the dark knowledge (i.e., the knowledge on wrong predictions) of a single network (i.e., a self-knowledge distillation) by forcing it to produce more meaningful and consistent predictions in a class-wise manner. Consequently, it mitigates overconfident predictions and reduces intra-class variations. Our experimental results on various image classification tasks demonstrate that the simple yet powerful method can significantly improve not only the generalization ability but also the calibration performance of modern convolutional neural networks.
Low Resource Neural Machine Translation: A Benchmark for Five African Languages

Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evaluate the current state of NMT for LRLs. Our evaluation, comparing a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling, shows significant performance improvements both in the En-LRL and LRL-En directions. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions. To demonstrate the generalization capability of each model, we also report results on multi-domain test sets. We release the standardized experimental data and the test sets for future works addressing the challenges of NMT in under-resourced settings, in particular for the SATOS languages.
The Propagation of the Economic Impact through Supply Chains: The Case of a Mega-City Lockdown against the Spread of COVID-19

This study quantifies the economic effect of a possible lockdown of Tokyo to prevent spread of COVID-19. The negative effect of the lockdown may propagate to other regions through supply chains because of shortage of supply and demand. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo when production activities that are not essential to citizens' survival in Tokyo were shut down for a certain period. We find that when Tokyo is locked down for a month, the indirect effect on other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan, or 5.3% of its annual GDP. Although the production shut down in Tokyo accounts for 21% of the total production in Japan, the lockdown would result in a reduction of the daily production in Japan by 86% in a month.
Angle deficit and nonlocal gravitoelectromagnetism around a slowly spinning cosmic string

Cosmic strings, as remnants of the symmetry breaking phase in the Early universe, may be susceptible to nonlocal physics. Here, we show that the presence of a Poincaré-invariant nonlocality—parametrized by a factor [Formula: see text]—regularizes the gravitational field and thereby changes the properties of spacetime: it is now simply connected and the angle deficit around the cosmic string becomes a function of the radial distance. Similar changes occur for the nonlocal gravitomagnetic field of a rotating cosmic string, and we translate these mathematical facts into the language of nonlocal gravitoelectromagnetism and thereby provide a physical interpretation. We hope that these insights might provide a helpful perspective in the search for traces of nonlocal physics in our universe.
Leverage the Average: an Analysis of Regularization in RL

Building upon the formalism of regularized Markov decision processes, we study the effect of Kullback-Leibler (KL) and entropy regularization in reinforcement learning. Through an equivalent formulation of the related approximate dynamic programming (ADP) scheme, we show that a KL penalty amounts to averaging q-values. This equivalence allows drawing connections between a priori disconnected methods from the literature, and proving that a KL regularization indeed leads to averaging errors made at each iteration of value function update. With the proposed theoretical analysis, we also study the interplay between KL and entropy regularization. When the considered ADP scheme is combined with neural-network-based stochastic approximations, the equivalence is lost, which suggests a number of different ways to do regularization. Because this goes beyond what we can analyse theoretically, we extensively study this aspect empirically.
Eccentric grazing envelope evolution towards Type IIb supernova progenitors

We simulate the evolution of eccentric binary systems in the frame of the grazing envelope evolution (GEE) channel for the formation of Type IIb supernovae (SNe IIb), and find that extra mass removal by jets increases the parameter space for the formation of SNe IIb in this channel. To explore the role of eccentricity and the extra mass removal by jets we use the stellar evolutionary code MESA binary. The initial primary and secondary masses are M1i=15Mo and M2i=2.5Mo. We examine initial semi-major axes of 600-1000Ro, and eccentricities of e=0-0.9. Both Roche lobe overflow (RLOF) and mass removal by jets, followed by a wind, leave a hydrogen mass in the exploding star of M(H,f)=0.05Mo, compatible with a SN IIb progenitor. When the initial orbit is not circular the final orbit might have a very high eccentricity. In many cases, with and without the extra mass removal by jets, the system can enter a common envelope evolution (CEE) phase, and then gets out from it. For some ranges of eccentricities the jets are more efficient in preventing the CEE. Despite the large uncertainties, extra mass removal by jets substantially increases the likelihood of the system to get out from a CEE. This strengthens earlier conclusions for circular orbits. In some cases RLOF alone, without mass removal by jets, can form SN IIb progenitors. We estimate that the extra mass removal by jets in the GEE channel increases the number of progenitors relative to that by RLOF alone by about a factor of two.
Data-driven Stabilization of SISO Feedback Linearizable Systems

In this paper we propose a methodology for stabilizing single-input single-output feedback linearizable systems when no system model is known and no prior data is available to identify a model. Conceptually, we have been greatly inspired by the work of Fliess and Join on intelligent PID controllers and the results in this paper provide sufficient conditions under which a modified version of their approach is guaranteed to result in asymptotically stable behavior. One of the key advantages of the proposed results is that, contrary to other approaches to controlling systems without a model (or with a partial model), such as reinforcement learning, there is no need for extensive training neither large amounts of data. Technically, our results draw heavily from the work of Nesic and co-workers on observer and controller design based on approximate models. Along the way we also make connections with other well established results such as high-gain observers and adaptive control. Although we focus on the simple setting of single-input single-output feedback linearizable systems we believe the presented results are already theoretically insightful and practically useful, the last point being substantiated by experimental evidence.
DISIR: Deep Image Segmentation with Interactive Refinement

Abstract. This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. Importantly, user annotations modify the inputs of the network – not its weights – enabling a fast and smooth process. Through experiments on two public aerial datasets, we show that user annotations are extremely rewarding: each click corrects roughly 5000 pixels. We analyze the impact of different aspects of our framework such as the representation of the annotations, the volume of training data or the network architecture. Code is available at this address.
Exact Controllability and Stabilization of Locally Coupled Wave Equations: Theoretical Results

In this paper, we study the exact controllability and stabilization of a system of two wave equations coupled by velocities with an internal, local control acting on only one equation. We distinguish two cases. In the first one, when the waves propagate at the same speed: using a frequency domain approach combined with multiplier technique, we prove that the system is exponentially stable when the coupling region satisfies the geometric control condition GCC. Following a result of Haraux ([11]), we establish the main indirect observability inequality. This results leads, by the HUM method, to prove that the total system is exactly controllable by means of locally distributed control. In the second case, when the waves propagate at different speed, we establish an exponential decay rate in the weak energy space. Consequently, the system is exactly controllable using a result of [11]. Finally, numerically, we provide results that insure the theoretical results of [13].
Modeling and forecasting the Covid-19 pandemic in Brazil

We model and forecast the evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February, 25, 2020 to March, 28, 2020. We use two variations of the SIR model and we include a parameter in this model that accounts for the effects of confinement measures. We do not calibrate our models parameters, but we estimate all of them based on a clear hierarchical procedure of squared error minimization. The estimated parameters of the ratio between symptomatic and asymptomatic individuals, the proportion of infected individuals that die and the usual epidemiological parameters have a great match with the ones provided by the literature. Our final models provide precise forecasts of the number of infected individuals. We use these models to discuss different scenarios of public policies. Long terms forecasts show that the confinement policy imposed by the government is able to flatten the pattern of infection of the COVID-19 and we are able to find the optimal date to end the policy. However, our results show that if this policy does not last enough time, it is only able to shift the peak of infection into the future keeping the value of the peak in almost the same value.
Massive star formation in the Carina nebula complex and Gum 31. I. the Carina nebula complex

Herein, we present results from observations of the 12CO (J=1-0), 13CO (J=1-0), and 12CO (J=2-1) emission lines toward the Carina nebula complex (CNC) obtained with the Mopra and NANTEN2 telescopes. We focused on massive-star-forming regions associated with the CNC including the three star clusters Tr14, Tr15, and Tr16, and the isolated WR-star HD92740. We found that the molecular clouds in the CNC are separated into mainly four clouds at velocities -27, -20, -14, and -8 km/s. Their masses are 0.7x10^4Msun, 5.0x10^4 Msun, 1.6x10^4 Msun, and 0.7x10^4 Msun, respectively. Most are likely associated with the star clusters, because of their high 12CO (J=2-1)/12CO (J=1-0) intensity ratios and their correspondence to the Spitzer 8 micron distributions. In addition, these clouds show the observational signatures of cloud--cloud collisions. In particular, there is a V-shaped structure in the position--velocity diagram and a complementary spatial distribution between the -20 km/s cloud and the -14 km/s cloud. Based on these observational signatures, we propose a scenario wherein the formation of massive stars in the clusters was triggered by a collision between the two clouds. By using the path length of the collision and the assumed velocity separation, we estimate the timescale of the collision to be ~1 Myr. This is comparable to the ages of the clusters estimated in previous studies.
Adaptive Group Sparse Regularization for Continual Learning

We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties. Our method selectively employs the two penalties when learning each node based its the importance, which is adaptively updated after learning each new task. By utilizing the proximal gradient descent method for learning, the exact sparsity and freezing of the model is guaranteed, and thus, the learner can explicitly control the model capacity as the learning continues. Furthermore, as a critical detail, we re-initialize the weights associated with unimportant nodes after learning each task in order to prevent the negative transfer that causes the catastrophic forgetting and facilitate efﬁcient learning of new tasks. Throughout the extensive experimental results, we show that our AGS-CL uses much less additional memory space for storing the regularization parameters, and it signiﬁcantly outperforms several state-of-the-art baselines on representative continual learning benchmarks for both supervised and reinforcement learning tasks.
Hemispheric Handedness in the Galactic Synchrotron Polarization Foreground

The large-scale magnetic field of the Milky Way is thought to be created by an αΩ dynamo, which implies that it should have opposite handedness north and south of the Galactic midplane. Here we attempt to detect a variation in handedness using polarization data from the Wilkinson Microwave Anisotropy Probe. Previous analyzes of the parity-even and parity-odd parts of linear polarization of the global dust and synchrotron emission have focused on quadratic correlations in spectral space of, and between, these two components. Here, by contrast, we analyze the parity-odd polarization itself and show that it has, on average, opposite signs in northern and southern Galactic hemispheres. Comparison with a Galactic mean-field dynamo model shows broad qualitative agreement and reveals that the sign of the observed hemispheric dependence of the azimuthally averaged parity-odd polarization is not determined by the sign of α, but by the sense of differential rotation.
Attack of the Genes: Finding Keys and Parameters of Locked Analog ICs Using Genetic Algorithm

Hardware intellectual property (IP) theft is a major issue in today’s globalized supply chain. To address it, numerous logic locking and obfuscation techniques have been proposed. While locking initially focused on digital integrated circuits (ICs), there have been recent attempts to extend it to analog ICs, which are easier to reverse engineer and to copy than digital ICs. In this paper, we use algorithms based on evolutionary strategies to investigate the security of analog obfuscation/locking techniques. We present a genetic algorithm (GA) approach which is capable of completely breaking a locked analog circuit by finding either its obfuscation key or its obfuscated parameters. We implement both the GA attack as well as a more naïve satisfiability modulo theory (SMT)-based attack on common analog benchmark circuits obfuscated by combinational locking and parameter biasing. We find that GA attack can unlock all the circuits using only the locked netlist and an unlocked chip in minutes. On the other hand, while the SMT attack converges faster, it requires circuit specification to execute and it also returns multiple keys that need to be brute-forced by a post-processing step. We also discuss how the GA attack can generalize to other recent analog locking techniques not tested in the paper.
Fish Oil Supplements and Symptoms of the Common Cold in Healthy Young Women

Background: Research in animal models suggests that fish oil ingestion may impair immunity and increase risk for infection. To date there are no studies examining this relationship between fish oil ingestion and risk for infection in humans. Objective: The primary aim of this randomized, placebo-controlled, double-blind, parallel-arm study was to examine the effect of 400 mg of EPA and 200 mg of DHA, the main components of fish oil (FO) supplements, on the incidence of symptoms related to upper respiratory tract infections in healthy young females, at a large southwestern university. Design: Healthy young women between 18 and 38 years of age who were non-obese (mean BMI 23.7 ± 0.6 kg/m2) were recruited from an urban southwestern university campus. Subjects were non-vegetarians, non-smokers, and reported consuming less than one serving (3.5 oz) of fish per week. Participants (n=26) were randomized according to age, body weight, BMI, and daily n-3 fatty acid (FA) intake into two groups: FO (one gel capsule of 600 mg EPA/DHA per day) or CO (one placebo gel capsule of 1000 mg coconut oil per day). Participants completed a validated daily cold symptom survey, the Wisconsin Upper Respiratory Symptom Survey-21 for 8 weeks. Fasting blood samples measuring TNFα concentrations were taken at weeks 1 and 8, when 24-hour dietary
Fashion Meets Computer Vision

Fashion is the way we present ourselves to the world and has become one of the world’s largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this article provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval; (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction; (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation; and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.