Miracle-dz/UniFAR-FTUs huggingface dataset:
Where is the photon in an interferometer?

In a paper titled “Asking photons where they have been? ” [2] Danan, Farfurnik, Bar-Ad and Vaidman describe an experiment with pre and post selected photons going through nested Mach-Zehnder interferometers. They ﬁnd that some of the mirrors leave no footprints on the signal and interpret this as evidence that the photon skipped these mirrors. They argue that the experiment supports Aharonov-Vaidman’s formulation of quantum mechanics [1] where post-selected particles are assigned disconnected trajectories. I review the experiment and analyze it within the orthodox framework of quantum mechanics. The standard view of interfering trajectories accounts for the experimental ﬁndings.
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics

Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented-reality devices due to their emerging use of eye-tracking sensors to enable foveated rendering techniques. However, existing EMB models have yet to demonstrate levels of performance that would be acceptable for real-world use. Deep learning approaches to EMB have largely employed plain convolutional neural networks (CNNs), but there have been many milestone improvements to convolutional architectures over the years including residual networks (ResNets) and densely connected convolutional networks (DenseNets). The present study employs a DenseNet architecture for end-to-end EMB and compares the proposed model against the most relevant prior works. The proposed technique not only outperforms the previous state of the art, but is also the first to approach a level of authentication performance that would be acceptable for real-world use.
Adapting Coreference Resolution Models through Active Learning

Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.
Asteroid Polarimetric Phase Behavior in the Near-infrared: S- and C-complex Objects

We present the first results of our survey of asteroid polarization phase curves in the near-infrared J and H bands using the WIRC+Pol instrument on the Palomar 200-inch telescope. We confirm through observations of standard stars that WIRC+Pol can reach the 0.1% precision needed for asteroid phase curve characterization, and we show that C-complex asteroids could act as an alternate calibration source, as they show less wavelength variation than stellar polarized standards. Initial polarization phase curve results for S-complex asteroids show a shift in behavior as a function of wavelength from visible to near-infrared bands, extending previously observed trends. Full near-infrared polarization phase curve characterization of individual asteroids will provide a unique constraint on surface composition of these objects by probing the wavelength dependence of albedo and index of refraction of the surface material.
Accurate Electronic Properties and Intercalation Voltages of Olivine-Type Li-Ion Cathode Materials from Extended Hubbard Functionals

The design of novel cathode materials for Li-ion batteries requires accurate ﬁrst-principles predictions of structural, electronic, and magnetic properties as well as intercalation voltages in compounds containing transition-metal (TM) elements. For such systems, density-functional theory (DFT) with standard (semi-)local exchange-correlation functionals is of no use as it fails dramatically due to strong self-interaction (delocalization) errors for the partially ﬁlled d shells of the TMs. Here, we perform the ﬁrst comparative study of the phospho-olivine cathode materials Li x MnPO 4 , Li x FePO 4 , and mixed-TM Li x Mn 1 / 2 Fe 1 / 2 PO 4 ( x = 0 , 1 / 4 , 1 / 2 , 3 / 4 , 1 ) using four electronic structure methods: DFT, DFT+ U , DFT+ U + V , and HSE06. We show that DFT+ U + V outperforms the other three methods, provided that the on-site U and intersite V Hubbard parameters are determined from ﬁrst-principles and self-consistently with respect to the structural parameters by means of density-functional perturbation theory (linear response). In particular, we demonstrate that DFT+ U + V is the only method that correctly predicts the digital change in oxidation states of the TM ions in all compounds for the mixed-valence phases occurring at intermediate Li concentrations, leading to voltages in remarkable agreement with experiments. We thus show that the inclusion of intersite Hubbard interactions is essential for the accurate prediction of thermodynamic quantities when electronic localization occurs while in the presence of inter-atomic orbital hybridization. At variance with the other methods, DFT+ U + V alone is capable to describe such localization-hybridization interplay, and thus opens the door for the study of more complex cathode materials as well as for a reliable exploration of the chemical space of compounds for Li-ion batteries.
SepViT: Separable Vision Transformer

Vision Transformers have witnessed prevailing success in a series of vision tasks. However, these Transformers often rely on extensive computational costs to achieve high performance, which is burdensome to deploy on resource-constrained devices. To alleviate this issue, we draw lessons from depthwise separable convolution and imitate its ideology to design an efficient Transformer backbone, i.e., Separable Vision Transformer, abbreviated as SepViT. SepViT helps to carry out the local-global information interaction within and among the windows in sequential order via a depthwise separable self-attention. The novel window token embedding and grouped self-attention are employed to compute the attention relationship among windows with negligible cost and establish long-range visual interactions across multiple windows, respectively. Extensive experiments on general-purpose vision benchmarks demonstrate that SepViT can achieve a state-of-the-art trade-off between performance and latency. Among them, SepViT achieves 84.2% top-1 accuracy on ImageNet-1K classification while decreasing the latency by 40%, compared to the ones with similar accuracy (e.g., CSWin). Furthermore, SepViT achieves 51.0% mIoU on ADE20K semantic segmentation task, 47.9 AP on the RetinaNet-based COCO detection task, 49.4 box AP and 44.6 mask AP on Mask R-CNN-based COCO object detection and instance segmentation tasks.
Initial stability estimates for Ricci flow and three dimensional Ricci-pinched manifolds

This paper investigates the question of stability for a class of Ricci ﬂows which start at possibly non-smooth metric spaces. We show that if the initial metric space is Reifenberg and locally bi-Lipschitz to Euclidean space, then two solutions to the Ricci ﬂow whose Ricci curvature is uniformly bounded from below and whose curvature is bounded by c · t − 1 converge to one another at an exponential rate once they have been appropriately gauged. As an application, we show that smooth three dimensional, complete, uniformly Ricci-pinched Riemannian manifolds with bounded curvature are either compact or ﬂat, thus conﬁrming a conjecture of Hamilton and Lott.
Earnings-22: A Practical Benchmark for Accents in the Wild

Modern automatic speech recognition (ASR) systems have achieved superhuman Word Error Rate (WER) on many common corpora despite lacking adequate performance on speech in the wild. Beyond that, there is a lack of real-world, accented corpora to properly benchmark academic and commercial models. To ensure this type of speech is represented in ASR bench-marking, we present Earnings-22, a 125 ﬁle, 119 hour corpus of English-language earnings calls gathered from global compa-nies. We run a comparison across 4 commercial models showing the variation in performance when taking country of origin into consideration. Looking at hypothesis transcriptions, we explore errors common to all ASR systems tested. By examining Individual Word Error Rate (IWER), we ﬁnd that key speech features impact model performance more for certain accents than others. Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.
Case-based reasoning for better generalization in textual reinforcement learning

Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency. Several deep reinforcement learning (RL) methods with varying architectures and learning schemes have been proposed for TBGs. However, these methods fail to generalize efficiently, especially under distributional shifts. In a departure from deep RL approaches, in this paper, we propose a general method inspired by case-based reasoning to train agents and generalize out of the training distribution. The case-based reasoner collects instances of positive experiences from the agent's interaction with the world in the past and later reuses the collected experiences to act efficiently. The method can be applied in conjunction with any existing on-policy neural agent in the literature for TBGs. Our experiments show that the proposed approach consistently improves existing methods, obtains good out-of-distribution generalization, and achieves new state-of-the-art results on widely used environments.
CVD-based small-angle twisted bilayer graphene

To realize the applicative potential of 2D materials twistronics, scalable synthesis and assembly techniques need to meet stringent requirements in terms of interface cleanness and twist-angle homogeneity. Here, we show that small-angle twisted bilayer graphene can be assembled from separated graphene single-crystals grown by chemical vapour deposition, ensuring electronic properties dominated by a device-scale-uniform moiré potential. Via low-temperature dual-gated magnetotransport we demonstrate the hallmarks of a 2.4°-twisted superlattice, including tunable regimes of interlayer coupling, reduced Fermi velocity, large interlayer capacitance, and density-independent Brown-Zak oscillations. The observation of these features in the electrical transport characteristics establishes CVD-based twisted bilayer
Learning Where to Learn in Cross-View Self-Supervised Learning

Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with the supervised ones, where the representation learning is mainly guided by a projection into an embedding space. During the projection, current methods simply adopt uniform aggregation of pixels for embedding; however, this risks involving object-irrelevant nuisances and spatial misalignment for different augmentations. In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better. Concretely, we reinterpret the projection head in SSL as a per-pixel projection and predict a set of spatial alignment maps from the original features by this weight-sharing projection head. A spectrum of aligned embeddings is thus obtained by aggregating the features with spatial weighting according to these alignment maps. As a result of this adaptive alignment, we observe substantial improvements on both image-level prediction and dense prediction at the same time: LEWEL improves MoCov2 [15] by 1.6%/1.3%/0.5%/0.4% points, improves BYOL [14] by 1.3%/1.3%/0.7%/0.6% points, on ImageNet linear/semi-supervised classification, Pascal VOC semantic segmentation, and object detection, respectively. ††Code: https://t.1y/ZI0A.
Tightly Seal Your Sensitive Pointers with PACTight

ARM is becoming more popular in desktops and data centers, opening a new realm in terms of security attacks against ARM. ARM has released Pointer Authentication, a new hardware security feature that is intended to ensure pointer integrity with cryptographic primitives. In this paper, we utilize Pointer Authentication (PA) to build a novel scheme to completely prevent any misuse of security-sensitive pointers. We propose PACTight to tightly seal these pointers. PACTight utilizes a strong and unique modifier that addresses the current issues with the state-of-the-art PA defense mechanisms. We implement four defenses based on the PACTight mechanism. Our security and performance evaluation results show that PACTight defenses are more efficient and secure. Using real PA instructions, we evaluated PACTight on 30 different applications, including NGINX web server, with an average performance overhead of 4.07% even when enforcing our strongest defense. PACTight demonstrates its effectiveness and efficiency with real PA instructions on real hardware.
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e.g., between risk factors and disease outcomes) that may otherwise be too weak to detect. When there is only a single source of variability (e.g., different scanners), domain adaptation and matching the distributions of representations may suffice in many scenarios. But in the presence of more than one nuisance variable which concurrently influence the measurements, pooling datasets poses unique challenges, e.g., variations in the data can come from both the acquisition method as well as the demographics of participants (gender, age). Invariant representation learning, by itself, is illsuited to fully model the data generation process. In this paper, we show how bringing recent results on equivariant representation learning (for studying symmetries in neural networks) instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. In particular, we demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples. Our code is available on https://github.com/vsingh-group/DatasetPooling.
Evaluating the ILC SUSY reach in the most challenging scenario: $\widetilde{\tau}$ NLSP, low $\Delta M$ , lowest cross-section

Pair-production of the superpartner of the $\tau$-lepton, the $\tilde{\tau}$, is one of the most interesting channels to search for SUSY in: The $\tilde{\tau}$ is likely to be the lightest scalar lepton, and the signature of $\tilde{\tau}$ pair production is one of the experimentally most difficult ones, thereby constituting the"worst"possible scenario for SUSY searches. The current model-independent $\tilde{\tau}$ limits comes from analyses performed at LEP but they suffer from the limited energy of this facility. Limits obtained at the LHC do extend to higher masses, but they are only valid under strong assumptions. The International Linear Collider, the ILC, is a future electron-positron collider, to operate initially at an energy of 250 GeV, then to be upgraded to 500 GeV, and possibly to 1 TeV at a later stage. ILC will be a powerful facility for SUSY searches. The capability of the ILC for determining exclusion/discovery limits for the $\tilde{\tau}$ in a model-independent way is shown in this paper. A detailed study of the"worst"scenario for $\tilde{\tau}$ exclusion/discovery, taking into account the effect of the $\tilde{\tau}$ mixing on $\tilde{\tau}$ production cross-section and detection efficiency, is presented. The study also includes an analysis of the effect of the overlay particles in the $\tilde{\tau}$ searches. The conclusion is that both the exclusion and discovery reaches for this"worst"case would extend to only a few GeV below the kinematic limit at the ILC. Also scenarios with the $\tilde{\tau}$ and the Lightest SUSY Particle (the LSP) quite close in mass can be discovered or excluded at most $\tilde{\tau}$ masses. The studies were done using detailed detector simulation of the ILD concept at the ILC. For signal, the fast detector simulation SGV was used, while the full Geant4 based DDSim was used for the standard model backgrounds.
Regularity of extremal solutions of semilinear elliptic equations with m-convex nonlinearities

We consider the Gelfand problem in a bounded smooth domain Ω ⊂ R N with the Dirichlet boundary condition. We are interested in the boundedness of the extremal solution u ∗ . When the dimension N ≥ 10, it is known that a singular extremal solution can be constructed for the nonlinearity f ( u ) = e u and Ω = B 1 . When 3 ≤ N ≤ 9, Cabr´e, Figalli, Ros-Oton, and Serra (2020) proved the following surprising result: the extremal solution u ∗ is bounded if the nonlinearity f is positive, nondecreasing, and convex. In this paper, we succeed in generalizing their result to general m -convex nonlinearities. Moreover, we give a uniﬁed viewpoint on the results of previous studies by considering m -convexity. We provide a closedness result for stable solutions with m -convex nonlinearities. As a consequence, we provide a Liouville-type result and by using a blow-up argument, we prove the boundedness of extremal solutions.
Multiresolution analysis and Zygmund dilations

A BSTRACT . Zygmund dilations are a group of dilations lying in between the standard product theory and the one-parameter setting – in R 3 = R × R × R they are the dilations ( x 1 , x 2 , x 3 ) 7→ ( δ 1 x 1 , δ 2 x 2 , δ 1 δ 2 x 3 ) . The dyadic multiresolution analysis and the related dyadic-probabilistic methods have been very impactful in the modern product singular integral theory. However, multiresolution analysis has not been understood in the Zygmund dilation setting or in other modiﬁed product space settings. In this paper we develop this missing dyadic multiresolution analysis of Zygmund type, and justify its usefulness by bounding, on weighted spaces, a general class of singular integrals that are invariant under Zygmund dilations. We provide novel examples of Zygmund A p weights and Zygmund kernels showcasing the optimality of our kernel assumptions for weighted estimates.
Nearly Minimax Algorithms for Linear Bandits with Shared Representation

We give novel algorithms for multi-task and lifelong linear bandits with shared representation. Specifically, we consider the setting where we play $M$ linear bandits with dimension $d$, each for $T$ rounds, and these $M$ bandit tasks share a common $k(\ll d)$ dimensional linear representation. For both the multi-task setting where we play the tasks concurrently, and the lifelong setting where we play tasks sequentially, we come up with novel algorithms that achieve $\widetilde{O}\left(d\sqrt{kMT} + kM\sqrt{T}\right)$ regret bounds, which matches the known minimax regret lower bound up to logarithmic factors and closes the gap in existing results [Yang et al., 2021]. Our main technique include a more efficient estimator for the low-rank linear feature extractor and an accompanied novel analysis for this estimator.
On Euler characteristic and Hitchin-Thorpe inequality for four-dimensional compact Ricci solitons

In this article, we investigate the geometry of 4 4 -dimensional compact gradient Ricci solitons. We prove that, under an upper bound condition on the range of the potential function, a 4 4 -dimensional compact gradient Ricci soliton must satisfy the classical Hitchin-Thorpe inequality. In addition, some volume estimates are also obtained.
Rich Feature Construction for the Optimization-Generalization Dilemma

There often is a dilemma between ease of optimization and robust out-of-distribution (OoD) generalization. For instance, many OoD methods rely on penalty terms whose optimization is challenging. They are either too strong to optimize reliably or too weak to achieve their goals. We propose to initialize the networks with a rich representation containing a palette of potentially useful features, ready to be used by even simple models. On the one hand, a rich representation provides a good initialization for the optimizer. On the other hand, it also provides an inductive bias that helps OoD generalization. Such a representation is constructed with the Rich Feature Construction (RFC) algorithm, also called the Bonsai algorithm, which consists of a succession of training episodes. During discovery episodes, we craft a multi-objective optimization criterion and its associated datasets in a manner that prevents the network from using the features constructed in the previous iterations. During synthesis episodes, we use knowledge distillation to force the network to simultaneously represent all the previously discovered features. Initializing the networks with Bonsai representations consistently helps six OoD methods achieve top performance on ColoredMNIST benchmark. The same technique substantially outperforms comparable results on the Wilds Camelyon17 task, eliminates the high result variance that plagues other methods, and makes hyperparameter tuning and model selection more reliable.
The Master Equation in a Bounded Domain with Absorption

We analyze the Master Equation and the convergence problem within Mean Field Games theory considering a bounded domain with homogeneous Dirichlet conditions. This framework characterizes N-players differential games where each player's dynamic ends when touching the boundary, leading to two Dirichlet boundary conditions in the Master Equation. In the first part of the paper we analyze both the well-posedness of the Master Equation and the regularity of its solutions studying the global regularity at the boundary of solutions for a suitable class of parabolic equations. Such results are then exploited to consider the convergence problem of the Nash equilibria, towards the Mean Field Games system strategies, in the N-players system, also proving optimal trajectories convergence. Eventually, we apply our findings to solve a toy model related to an optimal liquidation scenario.
Galactic diffuse gamma rays meet the PeV frontier

The Tibet AS$\gamma$ and LHAASO collaborations recently reported the observation of a $\gamma$-ray diffuse emission with energy up to the PeV from the Galactic plane. We discuss the relevance of non-uniform cosmic-ray transport scenarios and the implications of these results for cosmic-ray physics. We use the {\tt DRAGON} and {\tt HERMES} codes to build high-resolution maps and spectral distributions of that emission for several representative models under the condition that they reproduce a wide set of local cosmic-ray data up to 100 PeV. We show that the energy spectra measured by Tibet AS$\gamma$, LHAASO, ARGO-YBJ and Fermi-LAT in several regions of interest in the sky can all be consistently described in terms of the emission arising by the Galactic cosmic-ray"sea". We also show that all our models are compatible with IceTop $\gamma$-ray upper limits. Our results favor transport models characterized by spatial-dependent diffusion although some degeneracy remains between the choice of the transport scenario and that of the cosmic-ray spectral shape above 10 TeV. We discuss the role of forthcoming measurements in resolving that ambiguity.