Miracle-dz/UniFAR-FTUs huggingface dataset:
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Influence functions approximate the “influences” of training data-points for test predictions and have a wide variety of applications. Despite the popularity, their computational cost does not scale well with model and training data size. We present FastIF, a set of simple modifications to influence functions that significantly improves their run-time. We use k-Nearest Neighbors (kNN) to narrow the search space down to a subset of good candidate data points, identify the configurations that best balance the speed-quality trade-off in estimating the inverse Hessian-vector product, and introduce a fast parallel variant. Our proposed method achieves about 80X speedup while being highly correlated with the original influence values. With the availability of the fast influence functions, we demonstrate their usefulness in four applications. First, we examine whether influential data-points can “explain” test time behavior using the framework of simulatability. Second, we visualize the influence interactions between training and test data-points. Third, we show that we can correct model errors by additional fine-tuning on certain influential data-points, improving the accuracy of a trained MultiNLI model by 2.5% on the HANS dataset. Finally, we experiment with a similar setup but fine-tuning on datapoints not seen during training, improving the model accuracy by 2.8% and 1.7% on HANS and ANLI datasets respectively. Overall, our fast influence functions can be efficiently applied to large models and datasets, and our experiments demonstrate the potential of influence functions in model interpretation and correcting model errors.
SharpGAN: Receptive Field Block Net for Dynamic Scene Deblurring

When sailing at sea, the smart ship will inevitably produce swaying motion due to the action of wind, wave and current, which makes the image collected by the visual sensor appear motion blur. This will have an adverse effect on the object detection algorithm based on the vision sensor, thereby affect the navigation safety of the smart ship. In order to remove the motion blur in the images during the navigation of the smart ship, we propose SharpGAN, a new image deblurring method based on the generative adversarial network. First of all, the Receptive Field Block Net (RFBNet) is introduced to the deblurring network to strengthen the network's ability to extract the features of blurred image. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp image. Finally, we propose to use the lightweight RFB-s module to improve the real-time performance of deblurring network. Compared with the existing deblurring methods on large-scale real sea image datasets and large-scale deblurring datasets, the proposed method not only has better deblurring performance in visual perception and quantitative criteria, but also has higher deblurring efficiency.
Incoherent transport in a classical spin liquid

We study the energy and spin transport of the classical spin liquid hosted by the pyrochlore Heisenberg antiferromagnet in the large $S$ limit. Molecular dynamics calculation suggests that both the energy and spin diffusion constants approach finite limits as the temperature tends to zero. We explain our results in terms of an effective disorder model, where the energy/spin-carrying normal modes propagate in a quasistatic disordered spin background. The finite zero temperature limits of the diffusion constants are then naturally understood as a result of the finite mean free path of the normal modes due to the effective disorder.
Learning Adversarial Markov Decision Processes with Delayed Feedback

Reinforcement learning typically assumes that agents observe feedback for their actions immediately, but in many real-world applications (like recommendation systems) feedback is observed in delay. This paper studies online learning in episodic Markov decision processes (MDPs) with unknown transitions, adversarially changing costs and unrestricted delayed feedback. That is, the costs and trajectory of episode k are revealed to the learner only in the end of episode k+dᵏ, where the delays dᵏ are neither identical nor bounded, and are chosen by an oblivious adversary. We present novel algorithms based on policy optimization that achieve near-optimal high-probability regret of (K+D)¹ᐟ² under full-information feedback, where K is the number of episodes and D=∑ₖ dᵏ is the total delay. Under bandit feedback, we prove similar (K+D)¹ᐟ² regret assuming the costs are stochastic, and (K+D)²ᐟ³ regret in the general case. We are the first to consider regret minimization in the important setting of MDPs with delayed feedback.
Deficiency indices of Jacobi matrices and Dirac operators with point interactions on a discrete set

The paper concerns with infinite block Jacobi matrices J with p × p-matrix entries. We present new conditions for these block matrices to be selfadjoint and have discrete spectrum. In our previous papers there was established a close relation between a class of such matrices and 2p×2p Dirac operators with point interactions in L(R;C). For block Jacobi matrices J of this class we present several conditions ensuring either maximality or intermediatory of their deficiency indices. Applications to Dirac and matrix Schrodinger operators are given. It is worth mentioning that the above mentioned connection is employed here in both directions for the first time. In particular, the property of J to have maximal deficiency indices was firstly established for Dirac operators.
A classification of invertible phases of bosonic quantum lattice systems in one dimension

We study invertible states of 1d bosonic quantum lattice systems. We show that every invertible 1d state is in a trivial phase: after tensoring with some unentangled ancillas it can be disentangled by a fuzzy analog of a finite-depth quantum circuit. If an invertible state has symmetries, it may be impossible to disentangle it in a way that preserves the symmetries, even after adding unentagled ancillas. We show that in the case of a finite unitary symmetry G the only obstruction is an index valued in degree-2 cohomology of $G$. We show that two invertible $G$-invariant states are in the same phase if and only if their indices coincide.
The birational Lalanne–Kreweras involution

The Lalanne-Kreweras involution is an involution on the set of Dyck paths which combinatorially exhibits the symmetry of the number of valleys and major index statistics. We define piecewise-linear and birational extensions of the Lalanne-Kreweras involution. Actually, we show that the Lalanne-Kreweras involution is a special case of a more general operator, called rowvacuation, which acts on the antichains of any graded poset. Rowvacuation, like the closely related and more studied rowmotion operator, is a composition of toggles. We obtain the piecewise-linear and birational lifts of the Lalanne-Kreweras involution by using the piecewise-linear and birational toggles of Einstein and Propp. We show that the symmetry properties of the Lalanne-Kreweras involution extend to these piecewise-linear and birational lifts.
Refined Nonlinear Rectenna Modeling and Optimal Waveform Design for Multi-User Multi-Antenna Wireless Power Transfer

In this paper, we study the optimal waveform design for wireless power transfer (WPT) from a multi-antenna energy transmitter (ET) to multiple single-antenna energy receivers (ERs) simultaneously in multi-path frequency-selective channels. First, we propose a refined nonlinear current-voltage model of the diode in the ER rectifier, and accordingly derive new expressions for the output direct current (DC) voltage and corresponding harvested power at the ER. Leveraging this new rectenna model, we first consider the single-ER case and study the multisine-based power waveform design based on the wireless channel to maximize the harvested power at the ER. We propose two efficient algorithms for finding high-quality suboptimal solutions to this non-convex optimization problem. Next, we extend our formulated waveform design problem to the general multi-ER case for maximizing the weighted sum of the harvested powers by all ERs, and propose an efficient difference-of-convex-functions programming (DCP)-based algorithm for solving this problem. Finally, we demonstrate the superior performance of our proposed waveform designs based on the new rectenna model over existing schemes/models via simulations.
Excitation spectra of quantum matter without quasiparticles. II. Random 
t−J
 models

We present numerical solutions of the spectral functions of large dimension $t\text{\ensuremath{-}}J$ models with random nearest-neighbor exchange and global $\mathrm{SU}(M)$ spin rotation symmetry. The solutions are obtained from the saddle-point equations of the large dimension limit, followed by the large $M$ limit. The same saddle point equations also apply to the model with all-to-all and random hopping and exchange. Such a $t\text{\ensuremath{-}}J$ model realizes a deconfined critical state proposed as a model for the optimally doped cuprates. The large $M$ theory involves Green's functions for fractionalized spinons and holons carrying emergent U(1) gauge charges, obeying relations similar to those of the Sachdev-Ye-Kitaev (SYK) models. The low frequency spectral functions are compared with an analytic analysis of the operator scaling dimensions with good agreement. We also compute the low frequency and temperature behavior of experimentally observable gauge-invariant observables: the electron Green's function, the local spin susceptibility and the optical conductivity, along with the temperature dependence of the d.c. resistivity. The time reparameterization soft mode (equivalent to the boundary graviton in holographically dual models of two-dimensional quantum gravity) makes important contributions to all observables and provides a linear-in-temperature contribution to the d.c. resistivity.
Topological two-dimensional Su–Schrieffer–Heeger analog acoustic networks: Total reflection at corners and corner induced modes

In this work, we investigate some aspects of an acoustic analog of the two-dimensional Su–Schrieffer–Heeger model. The system is composed of alternating cross-sectional tubes connected in a square network, which in the limit of narrow tubes is described by a discrete model coinciding with the two-dimensional Su–Schrieffer–Heeger model. This model is known to host topological edge waves, and we develop a scattering theory to analyze how these waves scatter on edge structure changes. We show that these edge waves undergo a perfect reflection when scattering on a corner, incidentally leading to a new way of constructing corner modes. It is shown that reflection is high for a broad class of edge changes such as steps or defects. We then study the consequences of this high reflectivity on finite networks. Globally, it appears that each straight part of the edges, separated by corners or defects, hosts localized edge modes isolated from their neighborhood.
Radiation Hydrodynamics Simulations of Protoplanetary Disks: Stellar Mass Dependence of the Disk Photoevaporation Rate

Recent multiwavelength observations suggest that inner parts of protoplanetary disks (PPDs) have shorter lifetimes for heavier host stars. Since PPDs around high-mass stars are irradiated by strong ultraviolet radiation, photoevaporation may provide an explanation for the observed trend. We perform radiation hydrodynamics simulations of photoevaporation of PPDs for a wide range of host star mass of M * = 0.5–7.0 M ⊙. We derive disk mass-loss rate Ṁ , which has strong stellar dependence as Ṁ≈7.30×10−9(M*/M⊙)2M⊙yr−1 . The absolute value of Ṁ scales with the adopted far-ultraviolet and X-ray luminosities. We derive the surface mass-loss rates and provide polynomial function fits to them. We also develop a semianalytic model that well reproduces the derived mass-loss rates. The estimated inner-disk lifetime decreases as the host star mass increases, in agreement with the observational trend. We thus argue that photoevaporation is a major physical mechanism for PPD dispersal for a wide range of the stellar mass and can account for the observed stellar mass dependence of the inner-disk lifetime.
Isogeometric discretizations of the Stokes problem on trimmed geometries

The isogeometric approximation of the Stokes problem in a trimmed domain is studied. This setting is characterized by an underlying mesh unfitted with the boundary of the physical domain making the imposition of the essential boundary conditions a challenging problem. A very popular strategy is to rely on the so-called Nitsche method [22]. We show with numerically examples that in some degenerate trimmed domain configurations there is a lack of stability of the formulation, potentially polluting the computed solutions. After extending the stabilization procedure of [17] to incompressible flow problems, we theoretically prove that, combined with the Raviart-Thomas isogeometric element, we are able to recover the well-posedness of the formulation and, consequently, optimal a priori error estimates. Numerical results corroborating the theory and extending it for the case of the isogeometric N\'ed\'elec and Taylor-Hood elements are provided.
Kamino: Constraint-Aware Differentially Private Data Synthesis

Organizations are increasingly relying on data to support decisions. When data contains private and sensitive information, the data owner often desires to publish a synthetic database instance that is similarly useful as the true data, while ensuring the privacy of individual data records. Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data --- the underlying correlations and dependencies among tuples and attributes (i.e., the structure of the data). This structure is often expressed as integrity and schema constraints, or with a probabilistic generative process. As a result, the synthesized data is not useful for any downstream tasks that require this structure to be preserved. This work presents KAMINO, a data synthesis system to ensure differential privacy and to preserve the structure and correlations present in the original dataset. KAMINO takes as input of a database instance, along with its schema (including integrity constraints), and produces a synthetic database instance with differential privacy and structure preservation guarantees. We empirically show that while preserving the structure of the data, KAMINO achieves comparable and even better usefulness in applications of training classification models and answering marginal queries than the state-of-the-art methods of differentially private data synthesis.
Dialogue Graph Modeling for Conversational Machine Reading

Conversational Machine Reading (CMR) aims at answering questions in a complicated manner. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and ask questions to clarify if necessary. In this paper, we propose a dialogue graph modeling framework to improve the understanding and reasoning ability of machine on CMR task. There are three types of graph in total. Specifically, Discourse Graph is designed to learn explicitly and extract the discourse relation among rule texts as well as the extra knowledge of scenario; Decoupling Graph is used for understanding local and contextualized connection within rule texts. And finally a global graph for fusing the information together and reply to the user with our final decision being either"Yes/No/Irrelevant"or to ask a follow-up question to clarify.
Studying the onset of deconfinement with multi‐messenger astronomy of neutron stars

With the first multi-messenger observation of a binary neutron star merger (GW170817) new constraints became available for masses and radii of neutron stars. We introduce a class of hybrid EoS that fulfils all these constraints and predicts a region in the mass-radius diagram that could be populated only by hybrid neutron stars with quark matter cores. A confirmation of this conjecture would be provided when the NICER radius measurement for the high-mass pulsar PSR J0740+6620 yields a radius less than 11 km. Would this radius measurement yield a result in excess of 12 km, this would allow for both, a purely hadronic and a hybrid nature of this star. In the latter case the maximum mass could reach $2.6~M_\odot$ so that the lighter object in the asymmetric binary merger GW190814 could have been a hybrid star. We demonstrate that this high mass can be compatible with an early onset of deconfinement at star masses below $1~M_\odot$ and the occurrence of low-mass twin stars. In such a case the remnant of GW170817 could be a long-lived hypermassive pulsar.
Identification of Urinary Stone Components by X‐Ray Photoelectron Spectroscopy

Abstract X‐ray photoelectron spectroscopy (XPS) has been used for the first time to study the composition of calcium oxalate (CaOxa) stones and uric acid stones. This technique allows for the identification and location of various inorganic and organic species at the same time. In CaOxa stones, there were less than 10% of phosphates. Sectional analyses of these stones indicated that the content of phosphorus in the stone center is higher than that in stone crust. In uric acid stones, CaOxa was rarely found. XPS is able to detect differences in chemical functionality. In uric acid stones, the binding energy (E b) for nitrogen atoms were about 399.5 ± 0.2 eV, which are largely characteristic of organic nitrogens N (−3). In comparison, the E b values of N (+5) in inorganic compounds are about 401 eV.
TransTrack: Multiple-Object Tracking with Transformer

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \url{https://github.com/PeizeSun/TransTrack}.
Evolution of Subsurface Zonal and Meridional Flows in Solar Cycle 24 from Helioseismological Data

The results of determinations of the azimuthal and meridional velocities by time–distance helioseismology from Helioseismic and Magnetic Imager on board Solar Dynamics Observatory from 2010 May to 2020 September at latitudes and Stonyhurst longitudes from − 60° to + 60° and depths to about 19 Mm below the photosphere are used to analyze spatiotemporal variations of the solar differential rotation and meridional circulation. The pattern of torsional oscillations, or latitudinal belts of alternating “fast” and “slow” zonal flows migrating from high latitudes toward the equator, is found to extend in the time–latitude diagrams over the whole time interval. The oscillation period is comparable with a doubled solar-activity-cycle and can be described as an extended solar cycle. The zonal-velocity variations are related to the solar-activity level, the local-velocity increases corresponding to the sunspot-number increases and being localized at latitudes where the strongest magnetic fields are recorded. The dramatic growth of the zonal velocities in 2018 appears to be a precursor of the beginning of Solar Cycle 25. The strong symmetrization of the zonal-velocity field by 2020 can be considered another precursor. The general pattern of poleward meridional flows is modulated by latitudinal variations similar to the extended-solar-cycle behavior of the zonal flows. During the activity maximum, these variations are superposed with a higher harmonic corresponding to meridional flows converging to the spot-formation latitudes. Our results indicate that variations of both the zonal and meridional flows exhibit the extended-solar-cycle behavior, which is an intrinsic feature of the solar dynamo.
Requirements and sensitivity analysis of RANS-free wall-modeled LES

We study the sensitivity of wall model input variables to the modeling choices of the outer LES. This work is motivated by sensitivities observed in dynamic slip wall models. These dynamic wall models use variables from the near-wall LES solution as inputs to predict the wall stress without relying on a priori coefficients or equilibrium assumption. Mitigating the sensitivities in the wall model inputs allows development of robust dynamic wall models. The effects of SGS model, boundary condition type, numerics, and mesh topology are assessed through a series of WMLES calculations of turbulent channels. Probability density functions are computed from planes of the WMLES solutions at a wall-normal sampling height and are used as a metric for sensitivity. Sensitivity to SGS model is alleviated when the fraction of total wall stress carried by the SGS model is held constant. Use of hexagonal close-packed grids mitigated numerical sensitivities.
UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking

Logic locking aims to protect the intellectual property (IP) of integrated circuit (IC) designs throughout the globalized supply chain. The SAIL attack, based on tailored machine learning (ML) models, circumvents combinational logic locking with high accuracy and is amongst the most potent attacks as it does not require a functional IC acting as an oracle. In this work, we propose <italic>UNSAIL</italic>, a logic locking technique that inserts key-gate structures with the specific aim to confuse ML models like those used in SAIL. More specifically, <italic>UNSAIL</italic> serves to prevent attacks seeking to resolve the structural transformations of synthesis-induced obfuscation, which is an essential step for logic locking. Our approach is generic; it can protect any local structure of key-gates against such ML-based attacks in an <italic>oracle-less</italic> setting. We develop a reference implementation for the SAIL attack and launch it on both traditionally locked and <italic>UNSAIL</italic>-locked designs. For SAIL, two ML models have been proposed (which we implement accordingly), namely a change-prediction model and a reconstruction model; the change-prediction model is used to determine which key-gate structures to restore using the reconstruction model. Our study on benchmarks ranging from the ISCAS-85 and ITC-99 suites to the OpenRISC Reference Platform System-on-Chip (ORPSoC) confirms that <italic>UNSAIL</italic> degrades the accuracy of the change-prediction model and the reconstruction model by an average of <monospace>20.13</monospace> and <monospace>17</monospace> percentage points (<monospace>pp</monospace>), respectively. When the aforementioned models are combined, which is the most powerful scenario for SAIL, <italic>UNSAIL</italic> reduces the attack accuracy of SAIL by an average of <monospace>11pp</monospace>. We further demonstrate that <italic>UNSAIL</italic> thwarts other <italic>oracle-less</italic> attacks, i.e., SWEEP and the redundancy attack, indicating the generic nature and strength of our approach. Detailed layout-level evaluations illustrate that <italic>UNSAIL</italic> incurs minimal area and power overheads of <monospace>0.26%</monospace> and <monospace>0.61%</monospace>, respectively, on the million-gate ORPSoC design.