Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Advise Humans in High-Stakes Settings

Expert decision-makers (DMs) in high-stakes AI-assisted decision-making (AIaDM) settings receive and reconcile recommendations from AI systems before making their final decisions. We identify distinct properties of these settings which are key to developing AIaDM models that effectively benefit team performance. First, DMs incur reconciliation costs from exerting decision-making resources (e.g., time and effort) when reconciling AI recommendations that contradict their own judgment. Second, DMs in AIaDM settings exhibit algorithm discretion behavior (ADB), i.e., an idiosyncratic tendency to imperfectly accept or reject algorithmic recommendations for any given decision task. The human's reconciliation costs and imperfect discretion behavior introduce the need to develop AI systems which (1) provide recommendations selectively, (2) leverage the human partner's ADB to maximize the team's decision accuracy while regularizing for reconciliation costs, and (3) are inherently interpretable. We refer to the task of developing AI to advise humans in AIaDM settings as learning to advise and we address this task by first introducing the AI-assisted Team (AIaT)-Learning Framework. We instantiate our framework to develop TeamRules (TR): an algorithm that produces rule-based models and recommendations for AIaDM settings. TR is optimized to selectively advise a human and to trade-off reconciliation costs and team accuracy for a given environment by leveraging the human partner's ADB. Evaluations on synthetic and real-world benchmark datasets with a variety of simulated human accuracy and discretion behaviors show that TR robustly improves the team's objective across settings over interpretable, rule-based alternatives.
The role of upper hybrid waves in magnetic reconnection

Wind observations of upper hybrid (UH) waves adjacent to a magnetic X‐line are compared with simultaneous Wind electron observations to clarify wave‐electron interactions. Electron beams and “football” (T∥ > T⟂) distributions accompany the UH waves. The energy density of the inward‐directed beams, at 10−14 J/m3, is sufficient to supply the free energy for the waves. The electron beams may be part of the larger Hall current system, and the UH wave/electron instability represent a Hall current dissipation process (i.e., an effective resistance) which removes energy as the currents travels along the separatrix.
Koopman-based generalization bound: New aspect for full-rank weights

We propose a new bound for generalization of neural networks using Koopman operators. Whereas most of existing works focus on low-rank weight matrices, we focus on full-rank weight matrices. Our bound is tighter than existing norm-based bounds when the condition numbers of weight matrices are small. Especially, it is completely independent of the width of the network if the weight matrices are orthogonal. Our bound does not contradict to the existing bounds but is a complement to the existing bounds. As supported by several existing empirical results, low-rankness is not the only reason for generalization. Furthermore, our bound can be combined with the existing bounds to obtain a tighter bound. Our result sheds new light on understanding generalization of neural networks with full-rank weight matrices, and it provides a connection between operator-theoretic analysis and generalization of neural networks.
TOI-2525 b and c: A Pair of Massive Warm Giant Planets with Strong Transit Timing Variations Revealed by TESS

The K-type star TOI-2525 has an estimated mass of M = 0.849−0.033+0.024 M ⊙ and radius of R = 0.785−0.007+0.007 R ⊙ observed by the TESS mission in 22 sectors (within sectors 1 and 39). The TESS light curves yield significant transit events of two companions, which show strong transit timing variations (TTVs) with a semiamplitude of ∼6 hr. We performed TTV dynamical and photodynamical light-curve analysis of the TESS data combined with radial velocity measurements from FEROS and PFS, and we confirmed the planetary nature of these companions. The TOI-2525 system consists of a transiting pair of planets comparable to Neptune and Jupiter with estimated dynamical masses of m b = 0.088−0.004+0.005 and m c = 0.709−0.033+0.034 M Jup, radii of r b = 0.88−0.02+0.02 and r c = 0.98−0.02+0.02 R Jup, and orbital periods of P b = 23.288−0.002+0.001 and P c = 49.260−0.001+0.001 days for the inner and outer planet, respectively. The period ratio is close to the 2:1 period commensurability, but the dynamical simulations of the system suggest that it is outside the mean-motion resonance (MMR) dynamical configuration. Object TOI-2525 b is among the lowest-density Neptune-mass planets known to date, with an estimated median density of ρ b = 0.174−0.015+0.016 g cm−3. The TOI-2525 system is very similar to the other K dwarf systems discovered by TESS, TOI-2202 and TOI-216, which are composed of almost identical K dwarf primaries and two warm giant planets near the 2:1 MMR.
Hamilton--Jacobi equations for controlled gradient flows: cylindrical test functions

This work is the second part of a program initiated in arXiv:2111.13258 aiming at the development of an intrinsic geometric well-posedness theory for Hamilton-Jacobi equations related to controlled gradient flow problems in metric spaces. Our main contribution is that of showing that the comparison principle proven therein implies a comparison principle for viscosity solutions relative to smoother Hamiltonians, acting on test functions that are mere cylindrical functions of the underling squared metric distance and whose rigorous definition is achieved from the Evolutional Variational Inequality formulation of gradient flows (EVI). In particular, the new Hamiltonians no longer require to work with test functions containing Tataru's distance. This substantial simplification paves the way for the development of a comprehensive existence theory.
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model

Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind source separation tasks. Leveraging recent progress on deep learning techniques, we design a new neural NILM model {\em Multi-State Dual CNN} (MSDC). Different from previous models, MSDC explicitly extracts information about the appliance's multiple states and state transitions, which in turn regulates the prediction of signals for appliances. More specifically, we employ a dual-CNN architecture: one CNN for outputting state distributions and the other for predicting the power of each state. A new technique is invented that utilizes conditional random fields (CRF) to capture state transitions. Experiments on two real-world datasets REDD and UK-DALE demonstrate that our model significantly outperform state-of-the-art models while having good generalization capacity, achieving 6%-10% MAE gain and 33%-51% SAE gain to unseen appliances.
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks

Recent advances in instruction-following large language models (LLMs) have led to dramatic improvements in a range of NLP tasks. Unfortunately, we find that the same improved capabilities amplify the dual-use risks for malicious purposes of these models. Dual-use is difficult to prevent as instruction-following capabilities now enable standard attacks from computer security. The capabilities of these instruction-following LLMs provide strong economic incentives for dual-use by malicious actors. In particular, we show that instruction-following LLMs can produce targeted malicious content, including hate speech and scams, bypassing in-the-wild defenses implemented by LLM API vendors. Our analysis shows that this content can be generated economically and at cost likely lower than with human effort alone. Together, our findings suggest that LLMs will increasingly attract more sophisticated adversaries and attacks, and addressing these attacks may require new approaches to mitigations.
Maximum Coverage in Sublinear Space, Faster

Given a collection of $m$ sets from a universe $\mathcal{U}$, the Maximum Set Coverage problem consists of finding $k$ sets whose union has largest cardinality. This problem is NP-Hard, but the solution can be approximated by a polynomial time algorithm up to a factor $1-1/e$. However, this algorithm does not scale well with the input size. In a streaming context, practical high-quality solutions are found, but with space complexity that scales linearly with respect to the size of the universe $|\mathcal{U}|$. However, one randomized streaming algorithm has been shown to produce a $1-1/e-\varepsilon$ approximation of the optimal solution with a space complexity that scales only poly-logarithmically with respect to $m$ and $|\mathcal{U}|$. In order to achieve such a low space complexity, the authors used a technique called subsampling, based on independent-wise hash functions, and $F_0$-sketching. This article focuses on this sublinear-space algorithm and introduces methods to reduce the time cost of subsampling. Firstly, we give some optimizations that do not alter the space complexity, number of passes and approximation quality of the original algorithm. In particular, we reanalyze the error bounds to show that the original independence factor of $\Omega(\varepsilon^{-2} k \log m)$ can be fine-tuned to $\Omega(k \log m)$. Secondly we show that $F_0$-sketching can be replaced by a much more simple mechanism. Finally, our experimental results show that even a pairwise-independent hash-function sampler does not produce worse solution than the original algorithm, while running significantly faster by several orders of magnitude.
One-shot Learning of Surrogates in PDE-constrained Optimization Under Uncertainty

We propose a general framework for machine learning based optimization under uncertainty. Our approach replaces the complex forward model by a surrogate, which is learned simultaneously in a one-shot sense when solving the optimal control problem. Our approach relies on a reformulation of the problem as a penalized empirical risk minimization problem for which we provide a consistency analysis in terms of large data and increasing penalty parameter. To solve the resulting problem, we suggest a stochastic gradient method with adaptive control of the penalty parameter and prove convergence under suitable assumptions on the surrogate model. Numerical experiments illustrate the results for linear and nonlinear surrogate models.
Compressed quantum error mitigation

We introduce a quantum error mitigation technique based on probabilistic error cancellation to eliminate errors which have accumulated during the application of a quantum circuit. Our approach is based on applying an optimal"denoiser"after the action of a noisy circuit and can be performed with an arbitrary number of extra gates. The denoiser is given by an ensemble of circuits distributed with a quasiprobability distribution. For a simple noise model, we show that efficient, local denoisers can be found, and we demonstrate their effectiveness for the digital quantum simulation of the time evolution of simple spin chains.
Tighter PAC-Bayes Bounds Through Coin-Betting

We consider the problem of estimating the mean of a sequence of random elements $f(X_1, \theta)$ $, \ldots, $ $f(X_n, \theta)$ where $f$ is a fixed scalar function, $S=(X_1, \ldots, X_n)$ are independent random variables, and $\theta$ is a possibly $S$-dependent parameter. An example of such a problem would be to estimate the generalization error of a neural network trained on $n$ examples where $f$ is a loss function. Classically, this problem is approached through concentration inequalities holding uniformly over compact parameter sets of functions $f$, for example as in Rademacher or VC type analysis. However, in many problems, such inequalities often yield numerically vacuous estimates. Recently, the \emph{PAC-Bayes} framework has been proposed as a better alternative for this class of problems for its ability to often give numerically non-vacuous bounds. In this paper, we show that we can do even better: we show how to refine the proof strategy of the PAC-Bayes bounds and achieve \emph{even tighter} guarantees. Our approach is based on the \emph{coin-betting} framework that derives the numerically tightest known time-uniform concentration inequalities from the regret guarantees of online gambling algorithms. In particular, we derive the first PAC-Bayes concentration inequality based on the coin-betting approach that holds simultaneously for all sample sizes. We demonstrate its tightness showing that by \emph{relaxing} it we obtain a number of previous results in a closed form including Bernoulli-KL and empirical Bernstein inequalities. Finally, we propose an efficient algorithm to numerically calculate confidence sequences from our bound, which often generates nonvacuous confidence bounds even with one sample, unlike the state-of-the-art PAC-Bayes bounds.
Scaffolding Progress: How Structured Editors Shape Novice Errors When Transitioning from Blocks to Text

Transitioning from block-based programming environments to text-based programming environments can be challenging as it requires students to learn new programming language concepts. In this paper, we identify and classify the issues encountered when transitioning from block-based to text-based programming. In particular, we investigate differences that emerge in learners when using a structured editor compared to an unstructured editor. We followed 26 high school students (ages 12-16; M=14 years) as they transitioned from Scratch to Python in three phases: (i) learning Scratch, (ii) transitioning from Scratch to Python using either a structured or unstructured editor, and (iii) evaluating Python coding skills using an unstructured editor. We identify 27 distinct types of issues and show that learners who used a structured editor during the transition phase had 4.6x less syntax issues and 1.9x less data-type issues compared to those who did not. When these learners switched to an unstructured editor for evaluation, they kept a lower rate on data-type issues but faced 4x more syntax errors.
Design and Performance Analysis of Wireless Legitimate Surveillance Systems With Radar Function

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability.
Hausdorff Dimension of Cantor Intersections and Robust Heterodimensional Cycles for Heterochaos Horseshoe Maps

As a model to provide a hands-on, elementary understanding of chaotic dynamics in dimension three, we introduce a $C^2$-open set of diffeomorphisms of $\mathbb R^3$ having two horseshoes with different dimensions of instability. We prove that: the unstable set of one horseshoe and the stable set of the other are of Hausdorff dimension nearly $2$ whose cross sections are Cantor sets; the intersection of the unstable and stable sets contains a fractal set of Hausdorff dimension nearly $1$. As a corollary we detect $C^2$-robust heterodimensional cycles. Our proof employs the theory of normally hyperbolic invariant manifolds and the thicknesses of Cantor sets.
Moment bounds for Gaussian multiplicative chaos with higher-dimensional singularities

We determine the exact threshold of the extended Seiberg bound for the existence of correlation functions in the boundary Liouville conformal field theory in the unit disk. In probabilistic terms, our result is a toolbox yielding the threshold for the existence of positive moments of Gaussian multiplicative chaos measure, appliable to the case where singularities of arbitrary (co-)dimension in the background metric are present. We improve previous results of this type for 0-dimensional singularities in [DKRV16] and a sufficient condition for the 1-dimensional singularity in an unpublished appendix of [HRV18]. In particular, we prove the optimality of the moment bound threshold for boundary Gaussian multiplicative chaos conjectured in [HRV18], which is equivalent to the so-called unit volume Seiberg bound of the boundary Liouville conformal field theory.
Local controllability of the Korteweg-de Vries equation with the right Dirichlet control

The Korteweg-de Vries (KdV) equation with the right Dirichlet control was initially investigated more than twenty years ago. It was shown that this system is small time, locally, exactly controllable for all non-critical lengths and its linearized system is not controllable for {\it all} critical lengths. Even though the controllability of the KdV system has been studied extensively in the last two decades, the local controllability of this system for critical lengths remains an open question. In this paper, we give a definitive answer to this question. First, we characterize all critical lengths and the corresponding unreachable space for the linearized system. In particular, we show that the unreachable space is always of dimension 1. Second, we prove that the KdV system with the right Dirichlet control is not locally null controllable in small time. Third, we give a criterion to determine whether the system is locally exactly controllable in finite time or {\it not} locally null controllable in any positive time for {\it all} critical lengths. Consequently, we show that there exist critical lengths such that the system is not locally null controllable in small time but is locally exactly controllable in finite time. These facts are surprising and distinct in comparison with related known results. First, it is known that the corresponding KdV system with the right zero Dirichlet is locally exactly controllable in small time using internal controls. Second, the unreachable space of the linearized system of the corresponding KdV system with the right Neumann control might be of arbitrary dimension. Third, the KdV system with the right Neumann control is locally exactly controllable in small time if the corresponding unreachable space is of dimension 1.
The dual-path hypothesis for the emergence of anosognosia in Alzheimer’s disease

Although neurocognitive models have been proposed to explain anosognosia in Alzheimer’s disease (AD), the neural cascade responsible for its origin in the human brain remains unknown. Here, we build on a mechanistic dual-path hypothesis that brings error-monitoring and emotional processing systems as key elements for self-awareness, with distinct impacts on the emergence of anosognosia in AD. Proceeding from the notion of anosognosia as a dimensional syndrome, varying between a lack of concern about one’s own deficits (i.e., anosodiaphoria) and a complete lack of awareness of deficits, our hypothesis states that (i) unawareness of deficits would result from primary damage to the error-monitoring system, whereas (ii) anosodiaphoria would more likely result from an imbalance between emotional processing and error-monitoring. In the first case, a synaptic failure in the error-monitoring system, in which the anterior and posterior cingulate cortices play a major role, would have a negative impact on error (or deficits) awareness, preventing patients from becoming aware of their condition. In the second case, an impairment in the emotional processing system, in which the amygdala and the orbitofrontal cortex play a major role, would prevent patients from monitoring the internal milieu for relevant errors (or deficits) and assigning appropriate value to them, thus biasing their impact on the error-monitoring system. Our hypothesis stems on two scientific premises. One comes from preliminary results in AD patients showing a synaptic failure in the error-monitoring system along with a decline of awareness for cognitive difficulties at the time of diagnosis. Another comes from the somatic marker hypothesis, which proposes that emotional signals are critical to adaptive behavior. Further exploration of these premises will be of great interest to illuminate the foundations of self-awareness and improve our knowledge of the underlying paths of anosognosia in AD and other brain disorders.
DASH: Accelerating Distributed Private Machine Learning Inference with Arithmetic Garbled Circuits

The adoption of machine learning solutions is rapidly increasing across all parts of society. Cloud service providers such as Amazon Web Services, Microsoft Azure and the Google Cloud Platform aggressively expand their Machine-Learning-as-a-Service offerings. While the widespread adoption of machine learning has huge potential for both research and industry, the large-scale evaluation of possibly sensitive data on untrusted platforms bears inherent data security and privacy risks. Since computation time is expensive, performance is a critical factor for machine learning. However, prevailing security measures proposed in the past years come with a significant performance overhead. We investigate the current state of protected distributed machine learning systems, focusing on deep convolutional neural networks. The most common and best-performing mixed MPC approaches are based on homomorphic encryption, secret sharing, and garbled circuits. They commonly suffer from communication overheads that grow linearly in the depth of the neural network. We present Dash, a fast and distributed private machine learning inference scheme. Dash is based purely on arithmetic garbled circuits. It requires only a single communication round per inference step, regardless of the depth of the neural network, and a very small constant communication volume. Dash thus significantly reduces performance requirements and scales better than previous approaches. In addition, we introduce the concept of LabelTensors. This allows us to efficiently use GPUs while using garbled circuits, which further reduces the runtime. Dash offers security against a malicious attacker and is up to 140 times faster than previous arithmetic garbling schemes.