Miracle-dz/UniFAR-FTUs huggingface dataset:
Summarizing Differences between Text Distributions with Natural Language

How do two distributions of text differ? Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples. We propose to automatically summarize the differences by “learning a natural language hypothesis”: given two distributions D 0 and D 1 , we search for a description that is more often true for D 1 , e.g., “ is military-related. ” To tackle this problem, we ﬁne-tune GPT-3 to pro-pose descriptions with the prompt: “[samples of D 0 ] + [samples of D 1 ] + the difference between them is ”. We then re-rank the descriptions by checking how often they hold on a larger set of samples with a learned veriﬁer. On a benchmark of 54 real-world binary classiﬁcation tasks, while GPT-3 Curie (13B) only generates a description similar to human annotation 7% of the time, the performance reaches 61% with ﬁne-tuning and re-ranking, and our best system using GPT-3 Davinci (175B) reaches 76%. We apply our system to describe distribution shifts, debug dataset shortcuts, summarize unknown tasks, and label text clusters, and present analyses based on automatically generated descriptions. 1
Indicative Image Retrieval: Turning Blackbox Learning into Grey

Deep learning became the game changer for image retrieval soon after it was introduced. It promotes the feature extraction (by representation learning) as the core of image retrieval, with the relevance/matching evaluation being degenerated into simple similarity metrics. In many applications, we need the matching evidence to be indicated rather than just have the ranked list (e.g., the locations of the target proteins/cells/lesions in medical images). It is like the matched words need to be highlighted in search engines. However, this is not easy to implement without explicit relevance/matching modeling. The deep representation learning models are not feasible because of their blackbox nature. In this paper, we revisit the importance of relevance/matching modeling in deep learning era with an indicative retrieval setting. The study shows that it is possible to skip the representation learning and model the matching evidence directly. By removing the dependency on the pre-trained models, it has avoided a lot of related issues (e.g., the domain gap between classification and retrieval, the detail-diffusion caused by convolution, and so on). More importantly, the study demonstrates that the matching can be explicitly modeled and backtracked later for generating the matching evidence indications. It can improve the explainability of deep inference. Our method obtains a best performance in literature on both Oxford-5k and Paris-6k, and sets a new record of 97.77% on Oxford-5k (97.81% on Paris-6k) without extracting any deep features.
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP.
Quantitative Resilience of Linear Systems*

Actuator malfunctions may have disastrous con-sequences for systems not designed to mitigate them. We focus on the loss of control authority over actuators, where some actuators are uncontrolled but remain fully capable. To counter-act the undesirable outputs of these malfunctioning actuators, we use real-time measurements and redundant actuators. In this setting, a system that can still reach its target is deemed resilient. To quantify the resilience of a system, we compare the shortest time for the undamaged system to reach the target with the worst-case shortest time for the malfunctioning system to reach the same target, i.e., when the malfunction makes that time the longest. Contrary to prior work on driftless linear systems, the absence of analytical expression for time-optimal controls of general linear systems prevents an exact calculation of quantitative resilience. Instead, relying on Lyapunov theory we derive analytical bounds on the nominal and malfunctioning reach times in order to bound quantitative resilience. We illustrate our work on a temperature control system.
Agent-based modeling and simulation for malware spreading in D2D networks

This paper presents a new multi-agent model for simulating malware propagation in device-to-device (D2D) 5G networks. This model allows to understand and analyze mobile malware-spreading dynamics in such highly dynamical networks. Additionally, we present a theoretical study to validate and benchmark our proposed approach for some basic scenarios that are less complicated to model mathematically and also to highlight the key parameters of the model. Our simulations identify critical thresholds for"no propagation"and for"maximum malware propagation"and make predictions on the malware-spread velocity as well as device-infection rates. To the best of our knowledge, this paper is the first study applying agent-based simulations for malware propagation in D2D.
Gravitational radiation from a binary system in odd-dimensional spacetime

We explore possible manifestations of an odd number of extra dimensions in gravitational radiation, which are associated with violation of Huygens' principle in flat odd-dimensional spacetime. Our setup can be regarded as the limit of an infinite compactification radius in ADD model and is not viable as realistic cosmology, but it still may be useful as a simple analytically solvable model catching certain features of more realistic scenarios. The model consists of two point masses moving inside a flat three-dimensional brane, embedded in a five-dimensional Minkowski space and interacting only through a massless scalar field localized on the same brane, while gravitational radiation is emitted into the bulk. This setup avoids the difficulties associated with taking into account the gravitational stresses binding the system, which require the cubic terms in the perturbative gravitational Lagrangian, and permits to limit ourselves to linearized theory. We calculate radiation in a linearized five-dimensional gravity generalizing the Rohrlich-Teitelboim approach to extract the emitted part of the retarded gravitational field. The source term consists of a local contribution from point particles and a non-local contribution from scalar field stresses, which is calculated using the DIRE approach to post-Newtonian expansions. In the nonrelativistic limit, we find an analog of the quadrupole formula containing an integral over the history of the particles' motion preceding the retarded time. We also show that, for an observer on the brane, the radiation contains a third polarization: the breathing mode.
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. In this paper, we present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions. Our evaluation shows a significant gap between the performances on ModelNet40 and ModelNet40-C for state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but effective method by combining PointCutMix-R and TENT after evaluating a wide range of augmentation and test-time adaptation strategies. We identify a number of critical insights for future studies on corruption robustness in point cloud recognition. For instance, we unveil that Transformer-based architectures with proper training recipes achieve the strongest robustness. We hope our in-depth analysis will motivate the development of robust training strategies or architecture designs in the 3D point cloud domain. Our codebase and dataset are included in https://github.com/jiachens/ModelNet40-C
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits

In this paper, we generalize the concept of heavy-tailed multi-armed bandits to adversarial environments, and develop robust best-of-both-worlds algorithms for heavy-tailed multi-armed bandits (MAB), where losses have $\alpha$-th ($1<\alpha\le 2$) moments bounded by $\sigma^\alpha$, while the variances may not exist. Specifically, we design an algorithm \texttt{HTINF}, when the heavy-tail parameters $\alpha$ and $\sigma$ are known to the agent, \texttt{HTINF} simultaneously achieves the optimal regret for both stochastic and adversarial environments, without knowing the actual environment type a-priori. When $\alpha,\sigma$ are unknown, \texttt{HTINF} achieves a $\log T$-style instance-dependent regret in stochastic cases and $o(T)$ no-regret guarantee in adversarial cases. We further develop an algorithm \texttt{AdaTINF}, achieving $\mathcal O(\sigma K^{1-\nicefrac 1\alpha}T^{\nicefrac{1}{\alpha}})$ minimax optimal regret even in adversarial settings, without prior knowledge on $\alpha$ and $\sigma$. This result matches the known regret lower-bound (Bubeck et al., 2013), which assumed a stochastic environment and $\alpha$ and $\sigma$ are both known. To our knowledge, the proposed \texttt{HTINF} algorithm is the first to enjoy a best-of-both-worlds regret guarantee, and \texttt{AdaTINF} is the first algorithm that can adapt to both $\alpha$ and $\sigma$ to achieve optimal gap-indepedent regret bound in classical heavy-tailed stochastic MAB setting and our novel adversarial formulation.
The Complex Infrared Dust Continuum Emission of NGC 1068: Ground-based N- and Q-band Spectroscopy and New Radiative Transfer Models

Thanks to ground-based infrared and submillimeter observations the study of the dusty torus of nearby active galactic nuclei has greatly advanced in the last years. With the aim of further investigating the nuclear mid-infrared emission of the archetypal Seyfert 2 galaxy NGC 1068, here we present a fitting to the N- and Q-band Michelle/Gemini spectra. We initially test several available spectral energy distribution (SED) libraries, including smooth, clumpy and two-phase torus models, and a clumpy disk+wind model. We find that the spectra of NGC 1068 cannot be reproduced with any of these models. Although, the smooth torus models describe the spectra of NGC 1068 if we allow variation of some model parameters among the two spectral bands. Motivated by this result, we produced new SEDs using the radiative transfer code SKIRT. We use two concentric tori that allow us to test a more complex geometry. We test different values for the inner and outer radii, half-opening angle, radial, and polar exponent of the power-law density profile, opacity, and viewing angle. Furthermore, we also test the dust grains’ size and different optical and calorimetric properties of silicate grains. The best-fitting model consists of two concentric components with outer radii of 1.8 and 28 pc, respectively. We find that the size and the optical and calorimetric properties of graphite and silicate grains in the dust structure are key to reproducing the spectra of NGC 1068. A maximum grain size of 1 μm leads to a significant improvement in the fit. We conclude that the dust in NGC 1068 reaches different scales, where the highest contribution to the mid-infrared is given by a central and compact component. A less dense and extended component is present, which can be either part of the same torus (conforming a flared disk) or can represent the emission of a polar dust component, as already suggested from interferometric observations.
BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs

Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal effects between variables also from pure observational data. In this setting, the process of inferring the DAG structure from the data is referred to as causal structure learning or causal discovery. We introduce BCDAG, an R package for Bayesian causal discovery and causal effect estimation from Gaussian observational data, implementing the Markov chain Monte Carlo (MCMC) scheme proposed by Castelletti&Mascaro (2021). Our implementation scales efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, with the number of variables in the dataset. The package also provides functions for convergence diagnostics and for visualizing and summarizing posterior inference. In this paper, we present the key features of the underlying methodology along with its implementation in BCDAG. We then illustrate the main functions and algorithms on both real and simulated datasets.
Bioinspired Cortex-based Fast Codebook Generation

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by sensory cortical networks in the brain. Dubbed as bioinspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced, seconds versus hours, encoding distortions remain essentially the same in the new algorithm providing a basis for better generalization. Although we show herein the superior performance of the cortex model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization

Second-order optimizers are thought to hold the potential to speed up neural network training, but due to the enormous size of the curvature matrix, they typically require approximations to be computationally tractable. The most successful family of approximations are Kronecker-Factored, block-diagonal curvature estimates (KFAC). Here, we combine tools from prior work to evaluate exact second-order updates with careful ablations to establish a surprising result: Due to its approximations, KFAC is not closely related to second-order updates, and in particular, it significantly outperforms true second-order updates. This challenges widely held believes and immediately raises the question why KFAC performs so well. Towards answering this question we present evidence strongly suggesting that KFAC approximates a first-order algorithm, which performs gradient descent on neurons rather than weights. Finally, we show that this optimizer often improves over KFAC in terms of computational cost and data-efficiency.
Minotaur: Multi-Resource Blockchain Consensus

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, some times referred to as PoX in the literature) raises the fundamental question whether it is possible to utilize many of them in tandem and build multi-resource consensus protocols. The challenge in combining different resources is to achieve fungibility between them, in the sense that security would hold as long as the cumulative adversarial power across all resources is bounded. In this work, we put forth Minotaur, a multi-resource blockchain consensus protocol that combines proof of work (PoW) and proof-of-stake (PoS), and we prove it optimallyfungible. At the core of our design, Minotaur operates in epochs while continuously sampling the active computational power to provide a fair exchange between the two resources, work and stake. Further, we demonstrate the ability of Minotaur to handle a higher degree of work fluctuation as compared to the Bitcoin blockchain; we also generalize Minotaur to any number of resources. We demonstrate the simplicity of Minotaur via implementing a full stack client in Rust (available open source[24]). We use the client to test the robustness of Minotaur to variable mining power and combined work/stake attacks and demonstrate concrete empirical evidence towards the suitability of Minotaur to serve as the consensus layer of a real-world blockchain.
Quantum regression beyond the Born-Markov approximation for generalized spin-boson models

The quantum regression formula for an open quantum system consists in an inﬁnite hierarchy of conditions for its multi-time correlation functions, thus requiring full access to the total “sys-tem+environment” evolution, and providing a stronger requirement than CP-divisibility. Here, we analyze CP-divisibility and check the validity of quantum regression beyond the Born-Markov approximation (e.g. weak coupling limit) for a class of generalized spin-boson (GSB) models giving rise to a multi-level amplitude-damping evolution; in all cases, it is possible to engineer the system-bath coupling in such a way that quantum regression is exactly satisﬁed.
Chain of Thought Prompting Elicits Reasoning in Large Language Models

We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier.
On nonexistence of solutions to some time-space fractional evolution equations with transformed space argument

Some results on nonexistence of nontrivial solutions to some time and space fractional differential evolution equations with transformed space argument are obtained via the nonlinear capacity method. The analysis is then used for a 2× 2 system of equations with transformed space arguments. MSC[2020]: 35A01, 26A33
Boosting of Head Pose Estimation by Knowledge Distillation

We propose a response-based method of knowledge distillation (KD) for the head pose estimation problem. A student model trained by the proposed KD achieves results better than a teacher model, which is atypical for the response-based method. Our method consists of two stages. In the first stage, we trained the base neural network (NN), which has one regression head and four regression via classification (RvC) heads. We build the convolutional ensemble over the base NN using offsets of face bounding boxes over a regular grid. In the second stage, we perform KD from the convolutional ensemble into the final NN with one RvC head. The KD improves the results by an average of 7.7\% compared to base NN. This feature makes it possible to use KD as a booster and effectively train deeper NNs. NNs trained by our KD method partially improved the state-of-the-art results. KD-ResNet152 has the best results, and KD-ResNet18 has a better result on the AFLW2000 dataset than any previous method.We have made publicly available trained NNs and face bounding boxes for the 300W-LP, AFLW, AFLW2000, and BIWI datasets.Our method potentially can be effective for other regression problems.
Kernel Interpolation as a Bayes Point Machine

A Bayes point machine is a single classifier that approximates the majority decision of an ensemble of classifiers. This paper observes that kernel interpolation is a Bayes point machine for Gaussian process classification. This observation facilitates the transfer of results from both ensemble theory as well as an area of convex geometry known as Brunn-Minkowski theory to derive PAC-Bayes risk bounds for kernel interpolation. Since large margin, infinite width neural networks are kernel interpolators, the paper's findings may help to explain generalisation in neural networks more broadly. Supporting this idea, the paper finds evidence that large margin, finite width neural networks behave like Bayes point machines too.
A3COSMOS: A census on the molecular gas mass and extent of main-sequence galaxies across cosmic time

To constrain for the first time the mean mass and extent of the molecular gas of a mass-complete sample of $>10^{10}$M$_{\odot}$ main-sequence (MS) galaxies at $0.4<z<3.6$. We apply an innovative $uv$-based stacking analysis to a large set of archival Atacama Large Millimeter/submillimeter Array (ALMA) observations. This stacking analysis provides measurements of the mean mass and extent of the molecular gas of galaxy populations. The molecular gas mass of MS galaxies evolves with redshift and stellar mass. At all stellar masses, the molecular gas fraction decreases by a factor of 24 from $z\sim3.2$ to $z\sim0$. At a given redshift, the molecular gas fraction of MS galaxies decreases with stellar mass, at roughly the same rate as their specific star formation rate decreases. The molecular gas depletion time of MS galaxies remains roughly constant at $z>0.5$ with a value of 300--500 Myr, but increases by a factor of 3 from $z\sim0.5$ to $z\sim0$. This evolution of the molecular gas depletion time of MS galaxies can be predicted from the evolution of their molecular gas surface density and a seemingly universal MS-only $\Sigma_{M_{\rm mol}}-\Sigma_{\rm SFR}$ relation with an inferred slope of 1.13, i.e., the so-called KS relation. The far-infrared size of MS galaxies shows no significant evolution with redshift or stellar mass, with a mean circularized half-light radius of 2.2 kpc. Finally, our mean molecular gas masses are lower than previous estimates, likely caused by the fact that literature studies were biased towards individually-detected MS galaxies with massive gas reservoirs. To first order, the molecular gas content of MS galaxies regulates their star formation across cosmic time, while variation of their star formation efficiency plays a secondary role. Despite a large evolution of their gas content and SFRs, MS galaxies evolved along a seemingly universal MS-only KS relation.