Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Networks and the Chomsky Hierarchy

Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (20'910 models, 15 tasks) to investigate whether insights from the theory of computation can predict the limits of neural network generalization in practice. We demonstrate that grouping tasks according to the Chomsky hierarchy allows us to forecast whether certain architectures will be able to generalize to out-of-distribution inputs. This includes negative results where even extensive amounts of data and training time never lead to any non-trivial generalization, despite models having sufficient capacity to fit the training data perfectly. Our results show that, for our subset of tasks, RNNs and Transformers fail to generalize on non-regular tasks, LSTMs can solve regular and counter-language tasks, and only networks augmented with structured memory (such as a stack or memory tape) can successfully generalize on context-free and context-sensitive tasks.
Trion-phonon interaction in atomically thin semiconductors

Optical and transport properties of doped monolayer semiconductors are dominated by trions, which are three-particle compounds formed by two electrons and one hole or vice versa. In this work, we investigate the trion-phonon interaction on a microscopic footing and apply our model to the exemplary case of a molybdenum diselenide (MoSe2) monolayer. We determine the trion series of states and their internal quantum structure by solving the trion Schr\"odinger equation. Transforming the system into a trion basis and solving equations of motion, including the trion-phonon interaction within the second-order Born-Markov approximation, provides a microscopic access to the trion dynamics. In particular, we investigate trion propagation and compute the diffusion coefficient and mobility. In the low density limit, we find that trions propagate less efficiently than excitons and electrons due to their stronger coupling with phonons and their larger mass. For increasing densities, we predict a drastic enhancement of diffusion caused by the build-up of a large pressure by the degenerate trion gas, which is a direct consequence of the fermionic character of trions. Our work provides microscopic insights into the trion-phonon interaction and its impact on the diffusion behaviour in atomically thin semiconductors.
Revisiting the Carrollian scalar field

We inv estigate the (conformally coupled) scalar ﬁeld on a general Carrollian spacetime in arbitrary dimension. The analysis discloses electric and magnetic dynamics. For both, we provide the energy and the momenta of the ﬁeld, accompanied by their conservation equations. We discuss the conservation and non-conservation properties resulting from the existence of conformal isometries and the associated charges. We illustrate those results for a scalar ﬁeld propagating on the null boundary of four-dimensional Ricci-ﬂat Robinson–Trautman spacetimes.
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN

Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have incorporated some reading capability into a ranker to account for answerability, the ranker is still hindered by the noisy nature of the training data typically available in this area, which considers any passage containing an answer entity as a positive sample. However, the answer entity in a passage is not necessarily mentioned in relation with the given question. To address the problem, we propose an approach called \ttt{PReGAN} for Passage Reranking based on Generative Adversarial Neural networks, which incorporates a discriminator on answerability, in addition to a discriminator on topical relevance. The goal is to force the generator to rank higher a passage that is topically relevant and contains an answer. Experiments on five public datasets show that \ttt{PReGAN} can better rank appropriate passages, which in turn, boosts the effectiveness of QA systems, and outperforms the existing approaches without using external data.
Relating the fundamental frequency of speech with EEG using a dilated convolutional network

To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG). Usually, linear models are used in regression tasks. Either EEG is predicted, or speech is reconstructed, and the correlation between predicted and actual signal is used to measure the brain’s decoding ability. However, given the nonlinear nature of the brain, the modeling ability of linear models is limited. Recent studies introduced nonlinear models to relate the speech envelope to EEG. We set out to include other features of speech that are not coded in the envelope, notably the fundamental frequency of the voice (f0). F0 is a higher-frequency feature primarily coded at the brainstem to midbrain level. We present a dilated-convolutional model to provide evidence of neural tracking of the f0. We show that a combination of f0 and the speech envelope improves the performance of a state-of-the-art envelope-based model. This suggests the dilated-convolutional model can extract non-redundant information from both f0 and the envelope. We also show the ability of the dilated-convolutional model to generalize to subjects not included during training. This latter ﬁnding will accelerate f0-based hearing diagnosis.
Cryptography with Certified Deletion

We propose a new, unifying framework that yields an array of cryptographic primitives with certified deletion. These primitives enable a party in possession of a quantum ciphertext to generate a classical certificate that the encrypted plaintext has been information-theoretically deleted, and cannot be recovered even given unbounded computational resources. - For X \in {public-key, attribute-based, fully-homomorphic, witness, timed-release}, our compiler converts any (post-quantum) X encryption to X encryption with certified deletion. In addition, we compile statistically-binding commitments to statistically-binding commitments with certified everlasting hiding. As a corollary, we also obtain statistically-sound zero-knowledge proofs for QMA with certified everlasting zero-knowledge assuming statistically-binding commitments. - We also obtain a strong form of everlasting security for two-party and multi-party computation in the dishonest majority setting. While simultaneously achieving everlasting security against all parties in this setting is known to be impossible, we introduce everlasting security transfer (EST). This enables any one party (or a subset of parties) to dynamically and certifiably information-theoretically delete other participants' data after protocol execution. We construct general-purpose secure computation with EST assuming statistically-binding commitments, which can be based on one-way functions or pseudorandom quantum states. We obtain our results by developing a novel proof technique to argue that a bit b has been information-theoretically deleted from an adversary's view once they output a valid deletion certificate, despite having been previously information-theoretically determined by the ciphertext they held in their view. This technique may be of independent interest.
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs. Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks. To address the limitations, we propose"CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL). Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor. During inference, we introduce a new generation procedure with a critical sampling strategy that allows a model to automatically regenerate programs based on feedback from example unit tests and critic scores. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives, larger model sizes, and better pretraining data. Our method not only achieves new SOTA results on the challenging APPS benchmark, but also shows strong zero-shot transfer capability with new SOTA results on the simpler MBPP benchmark.
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing

—Integer programming (IP) is an important but challenging problem. Approximate methods have shown promising performance on solving the IP problem. However, we observed that a large fraction of variables solved by some iterative approximate methods ﬂuctuate around their ﬁnal converged discrete states in very long iterations. It implies that these approximate methods could be signiﬁcantly accelerated by early ﬁxing these ﬂuctuated variables to their converged states, while not signiﬁcantly harming the solution quality. To this end, we propose an innovative framework of learning to early ﬁx variables along with the approximate method. Speciﬁcally, we formulate the early ﬁxing process as a Markov decision process, and train it using imitation learning, where a policy network evaluates the posterior probability of each free variable concerning its discrete candidate states in each block of iterations. Extensive experiments on three typical IP applications are conducted, including constrained linear programming, MRF energy minimization and sparse adversarial attack, covering moderate and large-scale IP problems. The results demonstrate that our method could not only signiﬁcantly accelerate the previous approximate method up to over 10 times in most cases, but also produce similar or even better solutions. The implementation of our method is publicly available at https://github.com/SCLBD/Accelerated-Lpbox-ADMM. ﬁxing, learning.
Proposal for spin squeezing in rare-earth-ion-doped crystals with a four-color scheme

Achieving spin squeezing within solid-state devices is a long standing research goal, due to the promise of their particularities, for instance their long coherence times, the possibility of low-temperature experiments or integration of entanglement-assisted sensors on-chip. In this work, we investigate an interferometer-free four-color scheme to achieve spin squeezing of rare-earth ion-doped crystals. The proposal relies on an analytic derivation that starts from a Tavis-Cummings model for light-matter interaction, providing microscopic insights onto spin-squeezing generation. We evidence spin squeezing signature in the light intensity variance. We consider the two particular cases of europium- and praseodymium-doped yttrium orthosilicates, workhorses of quantum technology developments. We show that up to 8 dB of spin squeezing can be obtained with readily accessible experimental resources, including noise due to photon scattering. Our results for rare-earth ion-doped crystals add to promising properties of these platforms for manipulating many-body entangled states and for high-precision measurements.
Warp and blur model for imaging through turbulence

: Point Spread Function (PSF) for imaging through inhomogeneous refractive medium, such as atmospheric turbulence is bounded by three constraints [Charnotskii, Opt. Eng., 52 , 04600, (2013)]. PSF is non-negative, band-limited, and the third constraint, related to the energy conservation principle, warrants the absence of fluctuations in the image of a uniformly bright object. We develop a version of the common warp and blur model for the anisoplanatic image distortions by turbulence that satisfies these three constraints. undisturbed of practical turbulence imaging function two arguments: position vectors in the object and image planes.
RepMix: Representation Mixing for Robust Attribution of Synthesized Images

Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a challenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at https://github.com/TuBui/image_attribution.
Pushing the Limit of Phase Shift Feedback Compression for Intelligent Reﬂecting Surface-Assisted Wireless Systems by Exploiting Global Attention

—Intelligent reﬂecting surface (IRS) has recently ap- peared as a potential technology for 6G, and received much attention from academia and industry. However, most of existing works on IRS focus on how to compute the phase shift for performance enhancement, and the problem on how to obtain the computed phase shift at the IRS side is generally neglected. In this paper, we consider compressing the computed phase shift at the receiver side to the IRS through a bandwidth-limited feedback channel. In particular, we propose and investigate a novel attention mechanism named as global attention by exploiting the attention map over both spatial and channel dimensions. This allows us to to push the limit of phase shift feedback compression by utilizing the two-dimensional information, which is in sharp contrast to exiting works that only consider either the spatial or channel dimension. Besides, to cope with the problem of mismatched distribution of the phase shift, we introduce the generalized divisive normalization (GDN) layer and inverse generalized divisive normalization (IGDN) layer to the proposed global attention phase shift compression network (GAPSCN). Furthermore, due to practical constraints on the IRS, it is desirable to consider a simpliﬁed GAPSCN (S-GAPSCN), where a lightweight multi-scale simpliﬁed global attention module (MSSGAM) is proposed in the decoder located at the IRS side to compensate for the performance degradation due to the simpliﬁed structure. Simulation results show that the proposed GAPSCN is able to achieve a reconstruction accuracy close to 1 and performs much better than existing algorithms. The performance of the proposed S-GAPSCN can approach that of the GAPSCN but with a much lower computational load.
Force-Free Models of Filament Channels

A fast multigrid method to calculate the linear force-free field for a prescribed photospheric flux distribution is outlined. This is used to examine an idealized model of a filament channel. The magnetic fields, for a number of different field strengths and positions, are calcu lated and the height up to which field lines connect along the channel is examined. This is shown to strongly depend on the value of the helicity of the system. A possible explanation, in terms of the global helicity of the system, is suggested for the dextral/sinistral hemispheric pattern observed in filament channels.
On Trapped Modes in Variable White Dwarfs as Probes of the 12C(α, γ)16O Reaction Rate

We seek signatures of the current experimental 12C α,γ16O reaction rate probability distribution function in the pulsation periods of carbon–oxygen white dwarf (WD) models. We find that adiabatic g-modes trapped by the interior carbon-rich layer offer potentially useful signatures of this reaction rate probability distribution function. Probing the carbon-rich region is relevant because it forms during the evolution of low-mass stars under radiative helium-burning conditions, mitigating the impact of convective mixing processes. We make direct quantitative connections between the pulsation periods of the identified trapped g-modes in variable WD models and the current experimental 12C α,γ16O reaction rate probability distribution function. We find an average spread in relative period shifts of ΔP/P ≃ ±2% for the identified trapped g-modes over the ±3σ uncertainty in the 12C α,γ16O reaction rate probability distribution function—across the effective temperature range of observed DAV and DBV WDs and for different WD masses, helium shell masses, and hydrogen shell masses. The g-mode pulsation periods of observed WDs are typically given to six to seven significant figures of precision. This suggests that an astrophysical constraint on the 12C α,γ16O reaction rate could, in principle, be extractable from the period spectrum of observed variable WDs.
Deterministic Decoupling of Global Features and its Application to Data Analysis

We introduce a method for deterministic decoupling of global features and show its applicability to improve data analysis performance, as well as to open new venues for feature transfer. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features gradients. Through these transformations we define a normalization that, we demonstrate, allows for decoupling differentiable features. By applying this to sampling moments, we obtain a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We apply this method in the original data domain and at the output of a filter bank to regression and classification problems based on global descriptors, obtaining a consistent and significant improvement in performance as compared to using classical (non-decoupled) descriptors.
Multi-modal Robustness Analysis Against Language and Visual Perturbations

Joint visual and language modeling on large-scale datasets has recently shown a good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In this work, we perform the ﬁrst extensive robustness study of such models against various real-world perturbations focusing on video and language. We focus on text-to-video retrieval and propose two large-scale benchmark datasets, MSRVTT-P and YouCook2-P , which utilize 90 different visual and 35 different textual perturbations. The study reveals some interesting ﬁndings: 1) The studied models are more robust when text is perturbed versus when video is perturbed 2) The transformer text encoder is more robust on non-semantic changing text perturbations and visual perturbations compared to word embedding approaches. 3) Using two-branch encoders in isolation is typically more robust than when architectures use cross-attention. We hope this study will serve as a benchmark and guide future research in robust multimodal learning 2 .
Small subgraphs with large average degree

In this paper we study the fundamental problem of finding small dense subgraphs in a given graph. For a real number $s>2$, we prove that every graph on $n$ vertices with average degree at least $d$ contains a subgraph of average degree at least $s$ on at most $nd^{-\frac{s}{s-2}}(\log d)^{O_s(1)}$ vertices. This is optimal up to the polylogarithmic factor, and resolves a conjecture of Feige and Wagner. In addition, we show that every graph with $n$ vertices and average degree at least $n^{1-\frac{2}{s}+\varepsilon}$ contains a subgraph of average degree at least $s$ on $O_{\varepsilon,s}(1)$ vertices, which is also optimal up to the constant hidden in the $O(.)$ notation, and resolves a conjecture of Verstra\"ete.
Federated Self-supervised Learning for Video Understanding

The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge. Although various self-supervised learning (SSL) methods have been proposed to harvest their latent spatio-temporal representations for task-specific training, practical challenges including privacy concerns and communication costs prevent SSL from being deployed at large scales. To mitigate these issues, we propose the use of Federated Learning (FL) to the task of video SSL. In this work, we evaluate the performance of current state-of-the-art (SOTA) video-SSL techniques and identify their shortcomings when integrated into the large-scale FL setting simulated with kinetics-400 dataset. We follow by proposing a novel federated SSL framework for video, dubbed FedVSSL, that integrates different aggregation strategies and partial weight updating. Extensive experiments demonstrate the effectiveness and significance of FedVSSL as it outperforms the centralized SOTA for the downstream retrieval task by 6.66% on UCF-101 and 5.13% on HMDB-51.
Implicit step-truncation integration of nonlinear PDEs on low-rank tensor manifolds

Explicit step-truncation tensor methods have recently proven successful in integrating initial value problems for high-dimensional partial diﬀerential equations (PDEs). However, the combination of non-linearity and stiﬀness may introduce time-step restrictions which could make explicit integration computationally infeasible. To overcome this problem, in this paper we develop a new class of implicit rank-adaptive algorithms for temporal integration of nonlinear evolution equations on tensor manifolds. These algorithms are based on performing one time step with a conventional time-stepping scheme, followed by an implicit ﬁxed point iteration step involving a rank-adaptive truncation operation onto a tensor manifold. Implicit step truncation methods are straightforward to implement as they rely only on arithmetic operations between tensors, which can be performed by eﬃcient and scalable parallel algorithms. Numerical applications demonstrating the eﬀective-ness of implicit step-truncation tensor integrators are presented and discussed for the Allen-Cahn equation, the Fokker-Planck equation, and the nonlinear Schr¨odinger equation.
Which states can be reached from a given state by unital completely positive maps?

Abstract For a state $\omega$ on a C$^{*}$-algebra $A$, we characterize all states $\rho$ in the weak* closure of the set of all states of the form $\omega \circ \varphi$, where $\varphi$ is a map on $A$ of the form $\varphi (x)=\sum \nolimits _{i=1}^{n}a_i^{*}xa_i,$ $\sum \nolimits _{i=1}^{n}a_i^{*}a_i=1$ ($a_i\in A$, $n\in \mathbb {N}$). These are precisely the states $\rho$ that satisfy $\|\rho |J\|\leq \|\omega |J\|$ for each ideal $J$ of $A$. The corresponding question for normal states on a von Neumann algebra $\mathcal {R}$ (with the weak* closure replaced by the norm closure) is also considered. All normal states of the form $\omega \circ \psi$, where $\psi$ is a quantum channel on $\mathcal {R}$ (that is, a map of the form $\psi (x)=\sum \nolimits _ja_j^{*}xa_j$, where $a_j\in \mathcal {R}$ are such that the sum $\sum \nolimits _ja_j^{*}a_j$ converge to $1$ in the weak operator topology) are characterized. A variant of this topic for hermitian functionals instead of states is investigated. Maximally mixed states are shown to vanish on the strong radical of a C$^{*}$-algebra and for properly infinite von Neumann algebras the converse also holds.