Miracle-dz/UniFAR-FTUs huggingface dataset:
A Joint and Domain-Adaptive Approach to Spoken Language Understanding

Spoken Language Understanding (SLU) is composed of two subtasks: intent detection (ID) and slot filling (SF). There are two lines of research on SLU. One jointly tackles these two subtasks to improve their prediction accuracy, and the other focuses on the domain-adaptation ability of one of the subtasks. In this paper, we attempt to bridge these two lines of research and propose a joint and domain adaptive approach to SLU. We formulate SLU as a constrained generation task and utilize a dynamic vocabulary based on domain-specific ontology. We conduct experiments on the ASMixed and MTOD datasets and achieve competitive performance with previous state-of-the-art joint models. Besides, results show that our joint model can be effectively adapted to a new domain.
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning

How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap with little or no real-world data. Consequently, in this research, an adversarial model is put forward to robustify DeepRL-based autonomous vehicles trained in simulation to gradually surfacing harder events, so that the models could readily transfer to the real world.
HK-Sobolev space $W{S^{k,p}}$ on $\mathbb{R}^\infty$ and Bessel Potential

Our goal in this article is to construct HK-Sobolev spaces on R∞ which contains Sobolev spaces as dense embedding. We show that weakly convergent sequences in Sobolev spaces are strongly convergent in HK-Sobolev spaces. Also, we obtain that the Sobolev space through Bessel potential is densely contained in HK-Sobolev spaces. Finally we find sufficient conditions for the solvability of the divergence equation ∇ · F = f, when f is an element of the subspace KSp[RnI ] of the HK-Sobolev space WS k,p[RnI ] with the help of the Fourier transformation.
On some symmetries of the base n expansion of
1∕m : the class number connection

Suppose that $ m\equiv 1\mod 4 $ is a prime and that $ n\equiv 3\mod 4 $ is a primitive root modulo $ m $. In this paper we obtain a relation between the class number of the imaginary quadratic field $ \Q(\sqrt{-nm}) $ and the digits of the base $ n $ expansion of $ 1/m $. Secondly, if $ m\equiv 3\mod 4 $, we study some convoluted sums involving the base $ n $ digits of $ 1/m $ and arrive at certain congruence relations involving the class number of $ \Q(\sqrt{-m}) $ modulo certain primes $ p $ which properly divide $ n+1 $.
Poincar\'e Duality for Hopf algebroids

We prove a twisted Poincaré duality for (full) Hopf algebroids with bijective antipode. As an application, we recover the Hochschild twisted Poincaré duality of Van Den Bergh ([VDB]). We also get a Poisson twisted Poincaré duality, which was already stated for oriented Poisson manifolds in [CLYZ].
Polynomial bounds for chromatic number II: Excluding a star‐forest

The Gyárfás–Sumner conjecture says that for every forest H $H$ , there is a function f H ${f}_{H}$ such that if G $G$ is H $H$ ‐free then χ ( G ) ≤ f H ( ω ( G ) ) $\chi (G)\le {f}_{H}(\omega (G))$ (where χ , ω $\chi ,\omega $ are the chromatic number and the clique number of G $G$ ). Louis Esperet conjectured that, whenever such a statement holds, f H ${f}_{H}$ can be chosen to be a polynomial. The Gyárfás–Sumner conjecture is only known to be true for a modest set of forests H $H$ , and Esperet's conjecture is known to be true for almost no forests. For instance, it is not known when H $H$ is a five‐vertex path. Here we prove Esperet's conjecture when each component of H $H$ is a star.
Cycled Compositional Learning between Images and Text

We present an approach named the Cycled Composition Network that can measure the semantic distance of the composition of image-text embedding. First, the Composition Network transit a reference image to target image in an embedding space using relative caption. Second, the Correction Network calculates a difference between reference and retrieved target images in the embedding space and match it with a relative caption. Our goal is to learn a Composition mapping with the Composition Network. Since this one-way mapping is highly under-constrained, we couple it with an inverse relation learning with the Correction Network and introduce a cycled relation for given Image We participate in Fashion IQ 2020 challenge and have won the first place with the ensemble of our model.
Design and evaluation of ultra‐fast 8‐bit approximate multipliers using novel multicolumn inexact compressors

A multiplier, as a key component in many different applications, is a time‐consuming, energy‐intensive computation block. Approximate computing is a practical design paradigm that attempts to improve hardware efficacy while keeping computation quality satisfactory. A novel multicolumn 3,3:2 inexact compressor is presented in this paper. It takes three partial products from two adjacent columns each for rapid partial product reduction. The proposed inexact compressor and its derivatives enable us to design a high‐speed approximate multiplier. Then, another ultra‐fast, high‐efficient approximate multiplier is achieved by utilizing a systematic truncation strategy. The proposed multipliers accumulate partial products in only two stages, one fewer stage than other approximate multipliers in the literature. Implementation results by the Synopsys Design Compiler and 45 nm technology node demonstrate nearly 11.11% higher speed for the second proposed design over the fastest existing approximate multiplier. Furthermore, the new approximate multipliers are applied to the image processing application of image sharpening, and their performance in this application is highly satisfactory. It is shown in this paper that the error pattern of an approximate multiplier, in addition to the mean error distance and error rate, has a direct effect on the outcomes of the image processing application.
TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos

This paper summarizes the TinyAction challenge which was organized in ActivityNet workshop at CVPR 2021. This challenge focuses on recognizing real-world low-resolution activities present in videos. Action recognition task is currently focused around classifying the actions from high-quality videos where the actors and the action is clearly visible. While various approaches have been shown effective for recognition task in recent works, they often do not deal with videos of lower resolution where the action is happening in a tiny region. However, many real world security videos often have the actual action captured in a small resolution, making action recognition in a tiny region a challenging task. In this work, we propose a benchmark dataset, TinyVIRAT-v2, which is comprised of naturally occuring low-resolution actions. This is an extension of the TinyVIRAT dataset and consists of actions with multiple labels. The videos are extracted from security videos which makes them realistic and more challenging. We use current state-of-the-art action recognition methods on the dataset as a benchmark, and propose the TinyAction Challenge.
Collaborative Problem Solving on a Data Platform Kaggle

Data exchange across different domains has gained much attention as a way of creating new businesses and improving the value of existing services. Data exchange ecosystem is developed by platform services that facilitate data and knowledge exchange and offer co-creation environments for organizations to promote their problem-solving. In this study, we investigate Kaggle, a data analysis competition platform, and discuss the characteristics of data and the ecosystem that contributes to collaborative problem-solving by analyzing the datasets, users, and their relationships.
Local measures of entanglement structure and dynamics in CFTs and black holes

We study the structure and dynamics of entanglement in CFTs and black holes. We use a local entanglement measure, the entanglement contour, which is a spatial density function for von Neumann entropy with additional properties. We calculate the entanglement contour of a state excited by a splitting quench, and find universal results for the entanglement contours of low energy non-equilibrium states in 2d CFTs. We also calculate the contour of a non-gravitational bath coupled to an extremal AdS2 black hole, and find that the contour only has finite support within the bath, due to an island phase transition. The particular entanglement contour proposal we use quantifies how well the bath’s state can be reconstructed from its marginals, through its connection to conditional mutual information, and the vanishing contour is a reflection of the protection of bulk island regions against erasures of the boundary state. ar X iv :2 10 7. 11 38 5v 1 [ he pth ] 2 3 Ju l 2 02 1
Characterizing and tuning exceptional points using Newton polygons

The study of non-Hermitian degeneracies—called exceptional points (EPs)—has become an exciting frontier at the crossroads of optics, photonics, acoustics, and quantum physics. Here, we introduce the Newton polygon method as a general algebraic framework for characterizing and tuning EPs. Newton polygons, first described by Isaac Newton, are conventionally used in algebraic geometry, with deep roots in various topics in modern mathematics. We propose and illustrate how the Newton polygon method can enable the prediction of higher-order EPs, using a recently experimentally realized optical system. Using the paradigmatic Hatano-Nelson model, we demonstrate how our method can predict the presence of the non-Hermitian skin effect. As further application of our framework, we show the presence of tunable EPs of various orders in PT-symmetric one-dimensional models. We further extend our method to study EPs in higher number of variables and demonstrate that it can reveal rich anisotropic behaviour around such degeneracies. Our work provides an analytic recipe to understand exceptional physics.
Exponential stability of linear systems under a class of Desch–Schappacher perturbations

In this paper, we investigate the uniform exponential stability of the system where the unbounded operator is the infinitesimal generator of a linear semigroup of contractions in a Hilbert space and is a Desch–Schappacher operator. Then we give sufficient conditions for exponential stability of the above system. The obtained stability result is then applied to prove the uniform exponential stabilization of some bilinear partial differential equations.
Compact elastic objects in general relativity

We introduce a rigorous and general framework to study systematically self-gravitating elastic materials within general relativity, and apply it to investigate the existence and viability, including radial stability, of spherically symmetric elastic stars. We present the mass-radius ( M − R ) diagram for various families of models, showing that elasticity contributes to increase the maximum mass and the compactness up to ≈ 22%, thus supporting compact stars with mass well above two solar masses. Some of these elastic stars can reach compactness as high as GM/ ( c 2 R ) ≈ 0 . 35 while remaining stable under radial perturbations and satisfying all energy conditions and subluminal wave propagation, thus being physically realizable models of stars with a light ring. We provide numerical evidence that radial instability occurs for central densities larger than that corresponding to the maximum mass, as in the perfect ﬂuid case. Elasticity may be a key ingredient to build consistent models of exotic ultracompact objects and black-hole mimickers, and can also be relevant for a more accurate modelling of the interior of neutron stars.
Experimentally Verifiable U ( 1 ) B − L Symmetric Model with Type-II Seesaw and Dark Matter

In an endeavor to explain the light neutrino masses and dark matter (DM) simultaneously, we study a gauged U(1)B−L extension of the standard model (SM). The neutrino masses are generated through a variant of type-II seesaw mechanism in which one of the scalar triplets has a mass in a scale that is accessible at the present generation colliders. Three right chiral fermions χiR(i = e, μ, τ) with B−L charges -4, -4, +5 are invoked to cancel the B−L gauge anomalies and the lightest one among these three fermions becomes a viable DM candidate as their stability is guaranteed by a remnant Z2 symmetry to which U(1)B−L gauge symmetry gets spontaneously broken. Interestingly in this scenario, the neutrino mass and the co-annihilation of DM are interlinked through the breaking of U(1)B−L symmetry. Apart from giving rise to the observed neutrino mass and dark matter abundance, the model also predicts exciting signals at the colliders especially regarding the discovery of the triplet scalar in presence of the B − L gauge boson. We see a (34 − 54)% enhancement in the production of the TeV scale doubly charged scalar in presence of the ZBL gauge boson in a mass range 2.5 TeV to 4.4 TeV. We discuss all the relevant constraints on model parameters from observed DM abundance and null detection of DM at direct and indirect search experiments as well as the constraints on the B− L gauge boson from recent colliders. ∗Electronic address: purusottamghosh@hri.res.in †Electronic address: ph18resch11001@iith.ac.in ‡Electronic address: nnarendra@prl.res.in §Electronic address: nsahu@phy.iith.ac.in
Universality of free random variables: atoms for non-commutative rational functions

Consider a tuple ( Y 1 , . . . , Y d ) of normal operators in a tracial operator algebra setting with prescribed sizes of the eigenspaces for each Y i . We address the question what one can say about the sizes of the eigenspaces for any non-commutative polynomial P ( Y 1 , . . . , Y d ) in those operators? We show that for each polynomial P there are unavoidable eigenspaces, which occur in P ( Y 1 , . . . , Y d ) for any ( Y 1 , . . . , Y d ) with the prescribed eigenspaces for the marginals. We will describe this minimal situation both in algebraic terms - where it is given by realizations via matrices over the free skew ﬁeld and via rank calculations - and in analytic terms - where it is given by freely independent random variables with prescribed atoms in their distributions. The fact that the latter situation corresponds to this minimal situation allows to draw many new conclusions about atoms in polynomials of free variables. In particular, we give a complete description of atoms in the free commutator and the free anti-commutator. Furthermore, our results do not only apply to polynomials, but much more general also to non-commutative rational functions. Since many random matrix models become asymptotically free in the large N limit, our results allow us to calculate the location and size of atoms in the asymptotic eigenvalue distribution of polynomials and rational functions in randomly rotated matrices. be modelled by an abstract algebraic object, the so-called free ﬁeld. In the next subsection we will recall the basic properties of this object. By realizing marginal distributions with atoms (of rational weights) via matrices over the free ﬁeld we will then be able to get our results on atoms of functions of free variables, not only for polynomials, but also for non-commutative rational functions. Our results show that the polynomial convolutions P (cid:3) , as well as their generalizations to rational functions, can actually also be considered as mappings within the class of discrete sub-probability measures; and in this setting they also make sense not just for selfadjoint polynomials and measures on R , but actually for arbitrary rational functions and discrete sub-probability measures on C . For each non-commutative rational function R , we have a corresponding rational convolution R (cid:3) on discrete sub-probability measures on C , according to R (cid:3) ( µ p X 1 , . . . , µ p X d ) = µ pR ( X 1 ,.,X d ) .
Robotic Occlusion Reasoning for Efficient Object Existence Prediction

Reasoning about potential occlusions is essential for robots to efficiently predict whether an object exists in an environment. Though existing work shows that a robot with active perception can achieve various tasks, it is still unclear if occlusion reasoning can be achieved. To answer this question, we introduce the task of robotic object existence prediction: when being asked about an object, a robot needs to move as few steps as possible around a table with randomly placed objects to predict whether the queried object exists. To address this problem, we propose a novel recurrent neural network model that can be jointly trained with supervised and reinforcement learning methods using a curriculum training strategy. Experimental results show that 1) both active perception and occlusion reasoning are necessary to successfully achieve the task; 2) the proposed model demonstrates a good occlusion reasoning ability by achieving a similar prediction accuracy to an exhaustive exploration baseline while requiring only about 10% of the baseline’s number of movement steps on average; and 3) the model generalizes to novel object combinations with a moderate loss of accuracy.
MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox

We introduce the MuSe-Toolbox - a Python-based open-source toolkit for creating a variety of continuous and discrete emotion gold standards. In a single framework, we unify a wide range of fusion methods and propose the novel Rater Aligned Annotation Weighting (RAAW), which aligns the annotations in a translation-invariant way before weighting and fusing them based on the inter-rater agreements between the annotations. Furthermore, discrete categories tend to be easier for humans to interpret than continuous signals. With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards. To our knowledge, this is the first toolkit that provides a wide selection of state-of-the-art emotional gold standard methods and their transformation to discrete classes. Experimental results indicate that MuSe-Toolbox can provide promising and novel class formations which can be better predicted than hard-coded classes boundaries with minimal human intervention. The implementation is out-of-the-box available with all dependencies using a Docker container.
Image-Based Parking Space Occupancy Classification: Dataset and Baseline

We introduce a new dataset for image-based parking space occupancy classification: ACPDS. Unlike in prior datasets, each image is taken from a unique view, systematically annotated, and the parking lots in the train, validation, and test sets are unique. We use this dataset to propose a simple baseline model for parking space occupancy classification, which achieves 98% accuracy on unseen parking lots, significantly outperforming existing models. We share our dataset, code, and trained models under the MIT license.
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes

Although federated learning (FL) has recently been proposed for efficient distributed training and data privacy protection, it still encounters many obstacles. One of these is the naturally existing statistical heterogeneity among clients, making local data distributions non independently and identically distributed (i.e., non-iid), which poses challenges for model aggregation and personalization. For FL with a deep neural network (DNN), privatizing some layers is a simple yet effective solution for non-iid problems. However, which layers should we privatize to facilitate the learning process? Do different categories of non-iid scenes have preferred privatization ways? Can we automatically learn the most appropriate privatization way during FL? In this paper, we answer these questions via abundant experimental studies on several FL benchmarks. First, we present the detailed statistics of these benchmarks and categorize them into covariate and label shift non-iid scenes. Then, we investigate both coarse-grained and fine-grained network splits and explore whether the preferred privatization ways have any potential relations to the specific category of a non-iid scene. Our findings are exciting, e.g., privatizing the base layers could boost the performances even in label shift non-iid scenes, which are inconsistent with some natural conjectures. We also find that none of these privatization ways could improve the performances on the Shakespeare benchmark, and we guess that Shakespeare may not be a seriously non-iid scene. Finally, we propose several approaches to automatically learn where to aggregate via cross-stitch, soft attention, and hard selection. We advocate the proposed methods could serve as a preliminary try to explore where to privatize for a novel non-iid scene.
Precision cosmology and the stiff-amplified gravitational-wave background from inflation: NANOGrav, Advanced LIGO-Virgo and the Hubble tension

The recent NANOGrav finding of a common-spectrum process has invited interpretations as possible evidence of a primordial stochastic gravitational-wave background (SGWB) stronger than predicted by standard inflation + ΛCDM. Such an SGWB would contribute an extra radiation component to the background Universe which may affect its expansion history. As such, it may help alleviate the current Hubble tension, a novel connection between gravitational waves and cosmology. We demonstrate this by considering a cosmological model, the “standard inflation + stiff amplification” scenario, with two components added to the base-ΛCDM model: a stiff component (w ≡ p/ρ = 1) and the primordial SGWB. Previously, we showed that even for standard inflation, the SGWB may be detectable at the high frequencies probed by laser interferometers, if it is amplified by a possible early stiff era after reheating. Models that boost the SGWB enough to cause significant backreaction, however, must still preserve the well-measured radiation-matter equality, respecting the demands of precision cosmology. For that, we calculate the fully-coupled evolution of the SGWB and expansion history, sampling parameter space (tensor-to-scalar ratio, reheating temperature and temperature at stiff-to-radiation equality). We then perform a joint analysis of the NANOGrav results and latest upper bounds from Planck, big bang nucleosynthesis and Advanced LIGO-Virgo, to constrain the model. The resulting blue-tilted, stiff-amplified SGWB is still too small to explain the NANOGrav results. However, if someday, Advanced LIGO-Virgo detects the SGWB, our model can explain it within standard inflation (without requiring an initial spectral tilt). Meanwhile, this model may bring current high-z measurements of the Hubble constant within 3. 4σ of the low-z measurements by SH0ES (from 4.4σ) and within 2. 6σ of those by H0LiCOW (from 3.1σ), reducing the tension.