Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Resize Parameter in End-to-end Learned Image Compression

We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pair of differentiable downsampling/upsampling layers that sandwich a neural compression model. To determine resize factors for different inputs, we utilize another neural network jointly trained with the compression model, with the end goal of minimizing the rate-distortion objective. Our results suggest that"compression friendly"downsampled representations can be quickly determined during encoding by using an auxiliary network and differentiable image warping. By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines. We also carried out a subjective quality study, the results of which show that our new approach yields favorable compressed images. To facilitate reproducible research in this direction, the implementation used in this paper is being made freely available online at: https://github.com/treammm/ResizeCompression.
Federated Stochastic Primal-dual Learning with Differential Privacy

Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the local data not being exposed to any third party. However, the training of FL is an interactive process between local clients and the parameter server. Such process would cause privacy leakage since adversaries may retrieve sensitive information by analyzing the overheard messages. In this paper, we propose a new federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP). Compared to the existing methods, the proposed FedSPD-DP incorporates local stochastic gradient descent (local SGD) and partial client participation (PCP) for addressing the issues of communication efficiency and straggler effects due to randomly accessed clients. Our analysis shows that the data sampling strategy and PCP can enhance the data privacy whereas the larger number of local SGD steps could increase privacy leakage, revealing a non-trivial tradeoff between algorithm communication efficiency and privacy protection. Specifically, we show that, by guaranteeing $(\epsilon, \delta)$-DP for each client per communication round, the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}), \delta)$-DP after $T$ communication rounds while maintaining an $\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth learning problem, where $Q$ is the number of local SGD steps, $p$ is the client sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data sampling probability of each client under PCP. Experiment results are presented to evaluate the practical performance of the proposed algorithm and comparison with state-of-the-art methods.
The upgraded low-background germanium counting facility Gator for high-sensitivity γ-ray spectrometry

We describe the upgrade and performance of the high-purity germanium counting facility Gator, which is dedicated to low-background γ-ray spectrometry. Gator is operated at the Gran Sasso Underground Laboratory in Italy, at an average depth of 3600 meter water equivalent, and employed for material screening and selection in ultra-low background, rare-event search experiments in astroparticle physics. The detector is equipped with a passive shield made of layers of copper, lead and polyethylene, and the sample cavity is purged with gaseous nitrogen maintained at positive pressure for radon suppression. After upgrading its enclosure, the background rate is (82.0 ± 0.7) counts/(kg·day) in the energy region 100 keV to 2700 keV, a 20% reduction compared to the previously reported rate. We show the stability of various operation parameters as a function of time. We also summarize the sample analysis procedure, and demonstrate Gator's sensitivity by examining one material sample, a candidate photosensor for the DARWIN experiment.
Model-agnostic interpretation of 10 billion years of cosmic evolution traced by BOSS and eBOSS data

We present the first model-agnostic analysis of the complete set of Sloan Digital Sky Survey III (BOSS) and -IV (eBOSS) catalogues of luminous red galaxy and quasar clustering in the redshift range 0.2 ≤ z ≤ 2.2 (10 billion years of cosmic evolution), which consistently includes the baryon acoustic oscillations (BAO), redshift space distortions (RSD) and the shape of the transfer function signatures, from pre- and post-reconstructed catalogues in Fourier space. This approach complements the standard analyses techniques which only focus on the BAO and RSD signatures, and the full-modeling approaches which assume a specific underlying cosmology model to perform the analysis. These model-independent results can then easily be interpreted in the context of the cosmological model of choice. In particular, when combined with z > 2.1 Ly-α BAO measurements, the clustering BAO, RSD and Shape parameters can be interpreted within a flat-ΛCDM model yielding h = 0.6816 ± 0.0067, Ωm = 0.3001 ± 0.0057 and 109 × As = 2.43 ± 0.20 (or σ 8 = 0.858 ± 0.036) with a Big Bang Nucleosynthesis prior on the baryon density. Without any external dataset, the BOSS and eBOSS data alone imply Ωm = 0.2971 ± 0.0061 and 109 × As = 2.39+0.24 -0.43 (or σ 8 = 0.857 ± 0.040). For models beyond ΛCDM, eBOSS data alone (in combination with Planck) constrain the sum of neutrino mass to be Σmν < 0.40 eV with a BBN prior (Σmν < 0.082 eV) at 95% CL, the curvature energy density to Ωk = -0.022+0.032 -0.038 (Ωk = 0.0015 ± 0.0016) and the dark energy equation of state parameter to w = -0.998+0.085 -0.073 (w = -1.093+0.048 0.044) at 68% CL without a BBN prior. These results are the product of a substantial improvement of the state-of-the-art methodologies and represent the most precise model-agnostic cosmological constrains using spectroscopic large-scale data alone.
Hot-lines topology and the fate of the spin resonance mode in three-dimensional unconventional superconductors

In the quasi-two-dimensional (quasi-2D) copper- and iron-based superconductors, the onset of superconductivity is accompanied by a prominent peak in the magnetic spectrum at momenta close to the wave-vector of the nearby antiferromagnetic state. Such a peak is well described in terms of a spin resonance mode, i.e., a spin-1 exciton theoretically predicted for quasi-2D superconductors with a sign-changing gap. The same theories, however, indicate that such a resonance mode should be absent in a three-dimensional (3D) system with a spherical Fermi surface. This raises the question of the fate of the spin resonance mode in layered unconventional superconductors that are not strongly anisotropic, such as certain heavy-fermion compounds and potentially the newly discovered nickelate superconductor NdNiO$_2$. Here, we use the random-phase-approximation to calculate the dynamical spin susceptibility of 3D superconductors with a $d_{x^2-y^2}$-wave gap symmetry and corrugated cylindrical-like Fermi surfaces. By varying the out-of-plane hopping anisotropy $t_z/t$, we demonstrate that the appearance of a spin resonance mode is determined by the topology of the hot lines -- i.e. lines on the Fermi surface that are connected by the magnetic wave-vector. For an in-plane antiferromagnetic wave-vector, the hot lines undergo a topological transition from open lines to closed loops at a critical $t_z/t$ value. The closed hot lines cross the nodal superconducting lines, making the spin resonance mode overdamped and incoherent. In contrast, for an out-of-plane antiferromagnetic wave-vector, the hot lines remain open and the spin resonance mode remains sharp. We discuss the experimental implications of our results for the out-of-plane dispersion of the spin resonance mode and, more generally, for inelastic neutron scattering experiments on unconventional superconductors.
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence

One of the core problems in mean-field control and mean-field games is to solve the corresponding McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs). Most existing methods are tailored to special cases in which the mean-field interaction only depends on expectation or other moments and thus inadequate to solve problems when the mean-field interaction has full distribution dependence. In this paper, we propose a novel deep learning method for computing MV-FBSDEs with a general form of mean-field interactions. Specifically, built on fictitious play, we recast the problem into repeatedly solving standard FBSDEs with explicit coefficient functions. These coefficient functions are used to approximate the MV-FBSDEs' model coefficients with full distribution dependence, and are updated by solving another supervising learning problem using training data simulated from the last iteration's FBSDE solutions. We use deep neural networks to solve standard BSDEs and approximate coefficient functions in order to solve high-dimensional MV-FBSDEs. Under proper assumptions on the learned functions, we prove that the convergence of the proposed method is free of the curse of dimensionality (CoD) by using a class of integral probability metrics previously developed in [Han, Hu and Long, arXiv:2104.12036]. The proved theorem shows the advantage of the method in high dimensions. We present the numerical performance in high-dimensional MV-FBSDE problems, including a mean-field game example of the well-known Cucker-Smale model whose cost depends on the full distribution of the forward process.
Acquiring a Dynamic Light Field through a Single-Shot Coded Image

We propose a method for compressively acquiring a dynamic light field (a 5-D volume) through a single-shot coded image (a 2-D measurement). We designed an imaging model that synchronously applies aperture coding and pixel-wise exposure coding within a single exposure time. This coding scheme enables us to effectively embed the original information into a single observed image. The observed image is then fed to a convolutional neural network (CNN) for light-field reconstruction, which is jointly trained with the camera-side coding patterns. We also developed a hardware prototype to capture a real 3-D scene moving over time. We succeeded in acquiring a dynamic light field with 5x5 viewpoints over 4 temporal sub-frames (100 views in total)from a single observed image. Repeating capture and reconstruction processes over time, we can acquire a dynamic light field at 4x the frame rate of the camera. To our knowledge, our method is the first to achieve a finer temporal resolution than the camera itself in compressive light-field acquisition. Our software is available from our project webpage.11https://www.fujii. nuee.nagoya-u.ac. jp/Research/CompCam2
Ergomagnetosphere, Ejection Disc, Magnetopause in M87. I Global Flow of Mass, Angular Momentum, Energy and Current

We interpret the 1.3mm VLBI observations made by the Event Horizon Telescope of the black hole in M87. It is proposed that, instead of being a torus of accreting gas, the observed annular ring is a rotating, magnetically-dominated ergomagnetosphere that can transmit electromagnetic angular momentum and energy outward to the disc through a combination of large scale magnetic torque and small scale instabilities. It is further proposed that energy can be extracted by magnetic flux threading the ergosphere through the efficient emission of long wavelength electromagnetic disturbances on to negative energy orbits, when the invariant B2 − E2 becomes negative. In this way, the spinning black hole and its ergosphere not only power the jets but also the ejection disc so as to drive away most of the gas supplied near the Bondi radius. This outflow takes the form of a MHD wind, extending over many decades of radius, with a unidirectional magnetic field, that is collimated by the infalling gas across a magnetopause. This wind, in turn, collimates the relativistic jets and the emission observed from the jet sheath may be associated with a return current. A model for the global flow of mass, angular momentum, energy and current, on scales from the horizon to the Bondi radius, is presented and discussed.
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection

We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both maximize reward and maintain an accurate (ideally unbiased) population estimate of the reward. This setting is inherent to many public and private sector applications and often requires handling delayed feedback, small data, and distribution shifts. We demonstrate its importance on real data from the United States Internal Revenue Service (IRS). The IRS performs yearly audits of the tax base. Two of its most important objectives are to identify suspected misreporting and to estimate the "tax gap" -- the global difference between the amount paid and true amount owed. Based on a unique collaboration with the IRS, we cast these two processes as a unified optimize-and-estimate structured bandit. We analyze optimize-and-estimate approaches to the IRS problem and propose a novel mechanism for unbiased population estimation that achieves rewards comparable to baseline approaches. This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap. This has important social consequences given that the current tax gap is estimated at nearly half a trillion dollars. We suggest that this problem setting is fertile ground for further research and we highlight its interesting challenges. The results of this and related research are currently being incorporated into the continual improvement of the IRS audit selection methods.
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval

Dominant pre-training work for video-text retrieval mainly adopt the"dual-encoder"architectures to enable efficient retrieval, where two separate encoders are used to contrast global video and text representations, but ignore detailed local semantics. The recent success of image BERT pre-training with masked visual modeling that promotes the learning of local visual context, motivates a possible solution to address the above limitation. In this work, we for the first time investigate masked visual modeling in video-text pre-training with the"dual-encoder"architecture. We perform Masked visual modeling with Injected LanguagE Semantics (MILES) by employing an extra snapshot video encoder as an evolving"tokenizer"to produce reconstruction targets for masked video patch prediction. Given the corrupted video, the video encoder is trained to recover text-aligned features of the masked patches via reasoning with the visible regions along the spatial and temporal dimensions, which enhances the discriminativeness of local visual features and the fine-grained cross-modality alignment. Our method outperforms state-of-the-art methods for text-to-video retrieval on four datasets with both zero-shot and fine-tune evaluation protocols. Our approach also surpasses the baseline models significantly on zero-shot action recognition, which can be cast as video-to-text retrieval.
Disambiguation of morpho-syntactic features of African American English – the case of habitual be

Recent research has highlighted that natural language processing (NLP) systems exhibit a bias againstAfrican American speakers. These errors are often caused by poor representation of linguistic features unique to African American English (AAE), which is due to the relatively low probability of occurrence for many such features. We present a workflow to overcome this issue in the case of habitual “be”. Habitual “be” is isomorphic, and therefore ambiguous, with other forms of uninflected “be” found in both AAE and General American English (GAE). This creates a clear challenge for bias in NLP technologies. To overcome the scarcity, we employ a combination of rule-based filters and data augmentation that generate a corpus balanced between habitual and non-habitual instances. This balanced corpus trains unbiased machine learning classifiers, as demonstrated on a corpus of AAE transcribed texts, achieving . 65 F_1 score at classifying habitual “be”.
The variance-penalized stochastic shortest path problem

The stochastic shortest path problem (SSPP) asks to resolve the non-deterministic choices in a Markov decision process (MDP) such that the expected accumulated weight before reaching a target state is maximized. This paper addresses the optimization of the variance-penalized expectation (VPE) of the accumulated weight, which is a variant of the SSPP in which a multiple of the variance of accumulated weights is incurred as a penalty. It is shown that the optimal VPE in MDPs with non-negative weights as well as an optimal deterministic finite-memory scheduler can be computed in exponential space. The threshold problem whether the maximal VPE exceeds a given rational is shown to be EXPTIME-hard and to lie in NEXPTIME. Furthermore, a result of interest in its own right obtained on the way is that a variance-minimal scheduler among all expectation-optimal schedulers can be computed in polynomial time.
Novel small band gap double perovskites comprising of silver and thallium: A DFT insight into structural, optoelectronic, and thermoelectric properties

Researchers have addressed the non-traditional power generation schemes as alternatives to the traditional fossil-fuel methods enormously since the scientific community has serious concerns about shortages of energy on our planet for future generations. In this scenario, the innovative materials for photovoltaic and thermoelectric device applications are required by addressing current issues of instability and efficiency. Perovskites are very popular in this regard particularly having higher power conversion efficiency of 25.2% in the case of solar cells. In the current article, we investigated innovative small direct band gap double perovskites (elapsolite) Cs 2 AgTlX 6 (X= Cl, Br) with a comprehensive discussion on structural, electronic, optical, and thermoelectric properties using a first-principles approach. The compounds under investigation are found stable, efficient, and economical with alluring optical and thermoelectric properties. The higher absorption peaks in the visible range, substantial optical conductivities (~10 16 sec -1 ), and a lower percentage of reflection in the visible range make these compounds fascinating for solar cell applications. Whereas large values of Seebeck coefficients, electrical conductivities, the figure of merits (greater than unity), and small values of thermal conductivities suggest the applications of these compounds in thermoelectric generators.
ATST: Audio Representation Learning with Teacher-Student Transformer

Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data. SSL has achieved promising results in various domains. This work addresses the problem of segment-level general audio SSL, and proposes a new transformer-based teacher-student SSL model, named ATST. A transformer encoder is developed on a recently emerged teacher-student baseline scheme, which largely improves the modeling capability of pre-training. In addition, a new strategy for positive pair creation is designed to fully leverage the capability of transformer. Extensive experiments have been conducted, and the proposed model achieves the new state-of-the-art results on almost all of the downstream tasks.
Understanding The Robustness in Vision Transformers

Recent studies show that Vision Transformers(ViTs) exhibit strong robustness against various corruptions. Although this property is partly attributed to the self-attention mechanism, there is still a lack of systematic understanding. In this paper, we examine the role of self-attention in learning robust representations. Our study is motivated by the intriguing properties of the emerging visual grouping in Vision Transformers, which indicates that self-attention may promote robustness through improved mid-level representations. We further propose a family of fully attentional networks (FANs) that strengthen this capability by incorporating an attentional channel processing design. We validate the design comprehensively on various hierarchical backbones. Our model achieves a state-of-the-art 87.1% accuracy and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters. We also demonstrate state-of-the-art accuracy and robustness in two downstream tasks: semantic segmentation and object detection. Code is available at: https://github.com/NVlabs/FAN.
Morest: Model-based RESTful API Testing with Execution Feedback

RESTful APIs are arguably the most popular endpoints for accessing Web services. Blackbox testing is one of the emerging techniques for ensuring the reliability of RESTful APIs. The major challenge in testing RESTful APIs is the need for correct sequences of API operation calls for in-depth testing. To build meaningful operation call sequences, researchers have proposed techniques to learn and utilize the API dependencies based on OpenAPI specifications. However, these techniques either lack the overall awareness of how all the APIs are connected or the flexibility of adaptively fixing the learned knowledge. In this paper, we propose Morest, a model-based RESTful API testing technique that builds and maintains a dynamically updating RESTful-service Property Graph (RPG) to model the behaviors of RESTful-services and guide the call sequence generation. We empirically evaluated Morest and the results demonstrate that Morest can successfully request an average of 152.66%-232.45% more API operations, cover 26.16%-103.24% more lines of code, and detect 40.64%-215.94% more bugs than state-of-the-art techniques. In total, we applied Morest to 6 real-world projects and found 44 bugs (13 of them cannot be detected by existing approaches). Specifically, 2 of the confirmed bugs are from Bitbucket, a famous code management service with more than 6 million users.
The TESS-Keck Survey. XI. Mass Measurements for Four Transiting Sub-Neptunes Orbiting K Dwarf TOI–1246

Multiplanet systems are valuable arenas for investigating exoplanet architectures and comparing planetary siblings. TOI-1246 is one such system, with a moderately bright K dwarf (V = 11.6, K = 9.9) and four transiting sub-Neptunes identified by TESS with orbital periods of 4.31, 5.90, 18.66, and 37.92 days. We collected 130 radial velocity observations with Keck/HIRES and TNG/HARPS-N to measure planet masses. We refit the 14 sectors of TESS photometry to refine planet radii (2.97 ± 0.06 R ⊕, 2.47 ± 0.08 R ⊕, 3.46 ± 0.09 R ⊕, and 3.72 ± 0.16 R ⊕) and confirm the four planets. We find that TOI-1246 e is substantially more massive than the three inner planets (8.1 ± 1.1 M ⊕, 8.8 ± 1.2 M ⊕, 5.3 ± 1.7 M ⊕, and 14.8 ± 2.3 M ⊕). The two outer planets, TOI-1246 d and TOI-1246 e, lie near to the 2:1 resonance (P e/P d = 2.03) and exhibit transit-timing variations. TOI-1246 is one of the brightest four-planet systems, making it amenable for continued observations. It is one of only five systems with measured masses and radii for all four transiting planets. The planet densities range from 0.70 ± 0.24 to 3.21 ± 0.44 g cm−3, implying a range of bulk and atmospheric compositions. We also report a fifth planet candidate found in the RV data with a minimum mass of 25.6 ± 3.6 M ⊕. This planet candidate is exterior to TOI-1246 e, with a candidate period of 93.8 days, and we discuss the implications if it is confirmed to be planetary in nature.