Miracle-dz/UniFAR-FTUs huggingface dataset:
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

We study the problem of decomposing a complex text-to-sql task into smaller sub-tasks and how such a decomposition can significantly improve the performance of Large Language Models (LLMs) in the reasoning process. There is currently a significant gap between the performance of fine-tuned models and prompting approaches using LLMs on challenging text-to-sql datasets such as Spider. We show that SQL queries, despite their declarative structure, can be broken down into sub-problems and the solutions of those sub-problems can be fed into LLMs to significantly improve their performance. Our experiments with three LLMs show that this approach consistently improves their performance by roughly 10%, pushing the accuracy of LLMs towards state-of-the-art, and even beating large fine-tuned models on the holdout Spider dataset.
The $L^2$-unique continuation property on manifolds with bounded geometry and the deformation operator

A differential operator $T$ satisfies the $L^2$-unique continuation property if every $L^2$-solution of $T$ that vanishes on an open subset vanishes identically. We study the $L^2$-unique continuation property of an operator $T$ acting on a manifold with bounded geometry. In particular, we establish some connections between this property and the regularity properties of $T$. As an application, we prove that the deformation operator on a manifold with bounded geometry satisfies regularity and $L^2$-unique continuation properties. As another application, we prove that suitable elliptic operators are invertible (Hadamard well-posedness). Our results apply to compact manifolds, which have bounded geometry.
The cross-scattering component of polarimetric SAR in urban areas and its application to model-based scattering decomposition

ABSTRACT After the work of Freeman, Durden, Pottier, and Yamaguchi, many decomposition techniques have been proposed for urban areas, mainly to resolve the overestimation problem of volume scattering. Since it has been validated that the cross-polarized (HV) scattering is caused not only by forests but also by rotated dihedrals, in this paper, we propose a cross-scattering coherency matrix to model the HV component from orientated and complex buildings and then demonstrate its performance on model-based scattering decomposition. The building orientation angle is considered in this coherency matrix, making it flexible and adaptive in the decomposition. Therefore, the HV components from forests and orientated urban areas can be modelled. Two decomposition procedures are applied in this paper. The first one is to validate the effectiveness of this scattering model. We regard the HV component from urban areas as cross-scattering, which is an independent scattering component added to the Yamaguchi’s four-component decomposition. Another one is the urban area decomposition application using this scattering model. Decomposition is implemented for urban and natural areas, and the HV component from urban areas is regarded as their volume scattering. This procedure is similar to many other state-of-the-art methods for urban areas and needs to discriminate the urban and natural areas before decomposition. Spaceborne Radarsat-2 C-band, the airborne synthetic aperture radar (AIRSAR) L-band, and uninhabited aerial vehicle synthetic aperture radar (UAVSAR) L-band full polarimetric SAR data are used to validate the performance of this cross-scattering coherency matrix. The HV component of orientated buildings is generated, leading to a better decomposition result for urban areas.
Fundamental Limitations of Alignment in Large Language Models

An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by tuning the model in a way that enhances desired behaviors and inhibits undesired ones, a process referred to as alignment. In this paper, we propose a theoretical approach called Behavior Expectation Bounds (BEB) which allows us to formally investigate several inherent characteristics and limitations of alignment in large language models. Importantly, we prove that within the limits of this framework, for any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt. This implies that any alignment process that attenuates an undesired behavior but does not remove it altogether, is not safe against adversarial prompting attacks. Furthermore, our framework hints at the mechanism by which leading alignment approaches such as reinforcement learning from human feedback make the LLM prone to being prompted into the undesired behaviors. This theoretical result is being experimentally demonstrated in large scale by the so called contemporary"chatGPT jailbreaks", where adversarial users trick the LLM into breaking its alignment guardrails by triggering it into acting as a malicious persona. Our results expose fundamental limitations in alignment of LLMs and bring to the forefront the need to devise reliable mechanisms for ensuring AI safety.
Mathematical modelling of multi conductor cables

This paper proposes a formal justification of simplified 1D models for the propagation of electromagnetic waves in thin non-homogeneous lossy conductor cables. Our approach consists in deriving these models from an asymptotic analysis of 3D Maxwell's equations. In essence, we extend and complete previous results to the multi-wires case.
The effect of surface-charge convection on the settling velocity of spherical drops in a uniform electric field

The mechanism of surface-charge convection, quantified by the electric Reynolds number $Re$ , renders the Melcher–Taylor electrohydrodynamic model inherently nonlinear, with the electrostatic problem coupled to the flow. Because of this nonlinear coupling, the settling speed of a drop under a uniform electric field differs from that in its absence. This difference was calculated by Xu & Homsy (J. Fluid Mech., vol. 564, 2006, pp. 395–414) assuming small $Re$ . We here address the same problem using a different route, considering the case where the applied electric field is weak in the sense that the magnitude of the associated electrohydrodynamic velocity is small compared with the settling velocity. As convection is determined at leading order by the well-known flow associated with pure settling, the electrostatic problem becomes linear for arbitrary value of $Re$ . The electrohydrodynamic correction to the settling speed is then provided as a linear functional of the electric-stress distribution associated with that problem. Calculation of the settling speed eventually amounts to the solution of a difference equation governing the respective coefficients in a spherical harmonics expansion of the electric potential. It is shown that, despite the present weak-field assumption, our model reproduces the small- $Re$ approximation of Xu and Homsy as a particular case. For finite $Re$ , inspection of the difference equation reveals a singularity at the critical $Re$ -value $4S(1+R)(1+M)/(1+S)M$ , wherein $R$ , $S$ and $M$ respectively denote the ratios of resistivity, permittivity and viscosity values in the suspending and drop phases, as defined by Melcher & Taylor (Annu. Rev. Fluid Mech., vol. 1, 1969, pp. 111–146). Straightforward numerical solutions of this equation for electric Reynolds numbers smaller than the critical value reveal a non-monotonic dependence of the settling speed upon the electric field magnitude, including a transition from velocity enhancement to velocity decrement.
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Estimating heterogeneous treatment effects from observational data is a crucial task across many fields, helping policy and decision-makers take better actions. There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data. We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on the level of hidden confounding. We derive the B-Learner by adapting recent results for sharp and valid bounds of the average treatment effect (Dorn et al., 2021) into the framework given by Kallus&Oprescu (2023) for robust and model-agnostic learning of conditional distributional treatment effects. The B-Learner can use any function estimator such as random forests and deep neural networks, and we prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods. Semi-synthetic experimental comparisons validate the theoretical findings, and we use real-world data to demonstrate how the method might be used in practice.
Can GPT-4 Perform Neural Architecture Search?

We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textbf{G}PT-4 \textbf{E}nhanced \textbf{N}eural arch\textbf{I}tect\textbf{U}re \textbf{S}earch (GENIUS), leverages the generative capabilities of GPT-4 as a black-box optimiser to quickly navigate the architecture search space, pinpoint promising candidates, and iteratively refine these candidates to improve performance. We assess GENIUS across several benchmarks, comparing it with existing state-of-the-art NAS techniques to illustrate its effectiveness. Rather than targeting state-of-the-art performance, our objective is to highlight GPT-4's potential to assist research on a challenging technical problem through a simple prompting scheme that requires relatively limited domain expertise\footnote{Code available at \href{https://github.com/mingkai-zheng/GENIUS}{https://github.com/mingkai-zheng/GENIUS}. }. More broadly, we believe our preliminary results point to future research that harnesses general purpose language models for diverse optimisation tasks. We also highlight important limitations to our study, and note implications for AI safety.
A Cross-correlation Study between IceCube Neutrino Events and the FERMI Unresolved Gamma-Ray Sky

With the coincident detections of electromagnetic radiation together with gravitational waves (GW170817) or neutrinos (TXS 0506+056), the new era of multimessenger astrophysics has begun. Of particular interest are the searches for correlation between the high-energy astrophysical neutrinos detected by the IceCube Observatory and gamma-ray photons detected by the Fermi Large Area Telescope (LAT). So far, only sources detected by the LAT have been considered in correlation with IceCube neutrinos, neglecting any emission from sources too faint to be resolved individually. Here we present the first cross-correlation analysis considering the unresolved gamma-ray background (UGRB) and IceCube events. We perform a thorough sensitivity study, and, given the lack of identified correlation, we place upper limits on the fraction of the observed neutrinos that would be produced in proton–proton or proton–γ interactions from the population of sources contributing to the UGRB emission and dominating its spatial anisotropy (aka blazars). Our analysis suggests that, under the assumption that there is no intrinsic cutoff and/or hardening of the spectrum above Fermi-LAT energies, and that all gamma rays from the unresolved blazars dominating the UGRB fluctuation field are produced by neutral pions from p–p (p–γ) interactions, up to 60% (30%) of such a population may contribute to the total neutrino events observed by IceCube. This translates into an O (1%) maximum contribution to the astrophysical high-energy neutrino flux observed by IceCube at 100 TeV.
FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMIM for self-supervised pre-training to better accomplish medical image segmentation tasks. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the high-level semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our FreMIM over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMIM could consistently bring considerable improvements to model performance. The code will be publicly available at https://github.com/Rubics-Xuan/FreMIM.
Velocity and acceleration statistics of heavy spheroidal particles in turbulence

Abstract Non-spherical particles transported by turbulent flow have a rich dynamics that combines their translational and rotational motions. Here, the focus is on small, heavy, inertial particles with a spheroidal shape fully prescribed by their aspect ratio. Such particles undergo an anisotropic, orientation-dependent viscous drag with the carrier fluid flow whose associated torque is given by the Jeffery equations. Direct numerical simulations of homogeneous, isotropic turbulence are performed to study systematically how the translational motion of such spheroidal particles depends on their shape and size. It is found that the Lagrangian statistics of both velocity and acceleration can be described in terms of an effective Stokes number obtained as an isotropic average over angles of the particle's orientation. Corrections to the translational motion of particles due to their non-sphericity and rotation can hence be recast as an effective radius obtained from such a mean.
Revealing the higher-order spin nature of the Hall effect in non-collinear antiferromagnet $\mathrm{Mn_3Ni_{0.35}Cu_{0.65}N}$

Ferromagnets generate an anomalous Hall effect even without the presence of a magnetic field, something that conventional antiferromagnets cannot replicate but noncollinear antiferromagnets can. The anomalous Hall effect governed by the resistivity tensor plays a crucial role in determining the presence of time reversal symmetry and the topology present in the system. In this work we reveal the complex origin of the anomalous Hall effect arising in noncollinear antiferromagnets by performing Hall measurements with fields applied in selected directions in space with respect to the crystalline axes. Our coplanar magnetic field geometry goes beyond the conventional perpendicular field geometry used for ferromagnets and allows us to suppress any magnetic dipole contribution. It allows us to map the in-plane anomalous Hall contribution and we demonstrate a 120$^\circ$ symmetry which we find to be governed by the octupole moment at high fields. At low fields we subsequently discover a surprising topological Hall-like signature and, from a combination of theoretical techniques, we show that the spins can be recast into dipole, emergent octupole and noncoplanar effective magnetic moments. These co-existing orders enable magnetization dynamics unachievable in either ferromagnetic or conventional collinear antiferromagnetic materials.
DeepReShape: Redesigning Neural Networks for Efficient Private Inference

Prior work on Private Inference (PI)--inferences performed directly on encrypted input--has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs. Recent work has shown that FLOPs for PI can no longer be ignored and have high latency penalties. In this paper, we develop DeepReShape, a network redesign technique that tailors architectures to PI constraints, optimizing for both ReLUs and FLOPs for the first time. The {\em key insight} is that a strategic allocation of channels such that the network's ReLUs are aligned in their criticality order simultaneously optimizes ReLU and FLOPs efficiency. DeepReShape automates network development with an efficient process, and we call generated networks HybReNets. We evaluate DeepReShape using standard PI benchmarks and demonstrate a 2.1\% accuracy gain with a 5.2$\times$ runtime improvement at iso-ReLU on CIFAR-100 and an 8.7$\times$ runtime improvement at iso-accuracy on TinyImageNet. Furthermore, we demystify the input network selection in prior ReLU optimizations and shed light on the key network attributes enabling PI efficiency.
Transient jet ejections associated with limit-cycle behaviors in the very high state of black hole binaries

The ejection mechanism of transient relativistic jets from black hole binaries is studied. Based on observations of the limit-cycle behaviors of the superluminal jet source GRS 1915+105, we infer that transient jet ejections could happen just after the slim disk emerging from the standard disk at some distance runs over the standard disk and reaches the vicinity of the central black hole. The standard disk releases about half of the gravitational energy in the course of the accretion, but the released radiative energy could be absorbed by the optically thick slim disk covering the standard disk in this situation. Then, since the inward motion of the slim disk is much faster than that of the standard disk, a quantity of energy released by an amount of gas in the standard disk is received by the much smaller amount of gas in the slim disk. As a result, the energy per mass received by the slim disk is expected to be greatly amplified and is estimated to become highly relativistic. Since the energy is much larger than the gravitational energy, the height of the slim disk could increase significantly. Hence, the innermost part of the slim disk from which almost all the angular momentum has been transferred outward could have a much larger height than the black hole size, so they could collide with one another around the central axis of the disk, turning to an outward flow along the axis normal to the disk plane. The flow in this direction can be approximated to be that through the de Laval nozzle and could become supersonic near the distance where the flow has the smallest cross-section.
Detectability of Late-time Supernova Neutrinos with Fallback Accretion onto Protoneutron Star

We investigate the late-time neutrino emission powered by fallback mass accretion onto a protoneutron star (PNS), using neutrino radiation-hydrodynamic simulations with full Boltzmann neutrino transport. We follow the time evolution of the accretion flow onto the PNS until the system reaches a quasi-steady state. A standing shock wave is commonly formed in the accretion flow, whereas the shock radius varies depending on the mass accretion rate and the PNS mass. A sharp increase in temperature emerges in the vicinity of the PNS (∼10 km), which characterizes neutrino emission. Both the neutrino luminosity and the average energy become higher with increasing mass accretion rate and PNS mass. The mean energy of the emitted neutrinos is in the range of 10 ≲ ϵ ≲ 20 MeV, which is higher than that estimated from PNS cooling models (≲10 MeV). Assuming a distance to core-collapse supernova of 10 kpc, we quantify neutrino event rates for Super-Kamiokande (Super-K) and Deep Underground Neutrino Experiment (DUNE). The estimated detection rates are well above the background, and their energy-dependent features are qualitatively different from those expected from PNS cooling models. Another notable feature is that the neutrino emission is strongly flavor dependent, exhibiting that the neutrino event rate hinges on the neutrino oscillation model. We estimate them in the case with the adiabatic Mikheev–Smirnov–Wolfenstein model, and show that the normal and inverted mass hierarchy offer a large number of neutrino detections in Super-K and DUNE, respectively. Hence the simultaneous observation with Super-K and DUNE of fallback neutrinos will provide a strong constraint on the neutrino mass hierarchy.
Parameterization of Outgoing Infrared Radiation Derived from Detailed Radiative Calculations.

Abstract State-of-the-art radiative transfer models can calculate outgoing infrared (IR) irradiance at the top of the atmosphere (F) to an accuracy suitable for climate modeling given the proper atmospheric profiles of temperature and absorbing gases and aerosols. However, such sophisticated methods are computationally time consuming and ill-suited for simple vertically-averaged models or diagnostic studies. The alternative of empirical expressions for F is plagued by observational uncertainty which forces the functional forms to be very simple. We develop, a parameterization of climatological F by curve-fitting the results of a detailed radiative transfer model. The parameterization comprises clear-sky and cloudy-sky terms. Only two parameters are used to predict clear-sky outgoing IR irradiance: surface air temperature (Ts) and 0–12 km height-mean relative humidity (ˆRH). With this choice of parameters (in particular, the use of ˆRH instead of precipitable water) the outgoing IR irradiance can be estima.
Exact entanglement in the driven quantum symmetric simple exclusion process

Entanglement properties of driven quantum systems can potentially differ from the equilibrium situation due to long range coherences. We confirm this observation by studying a suitable toy model for mesoscopic transport: the open quantum symmetric simple exclusion process (QSSEP). We derive exact formulae for its mutual information between different subsystems in the steady state and show that it satisfies a volume law. Surprisingly, the QSSEP entanglement properties only depend on data related to its transport properties and we suspect that such a relation might hold for more general mesoscopic systems. Exploiting the free probability structure of QSSEP, we obtain these results by developing a new method to determine the eigenvalue spectrum of sub-blocks of random matrices from their so-called local free cumulants – a mathematical result on its own with potential applications in the theory of random matrices. As an illustration of this method, we show how to compute expectation values of observables in systems satisfying the Eigenstate Thermalization Hypothesis (ETH) from the local free cumulants.
Scattering dynamics and boundary states of a non-Hermitian Dirac equation

We study a non-Hermitian variant of the (2+1)-dimensional Dirac wave equation, which hosts a real energy spectrum with pairwise-orthogonal eigenstates. In the spatially uniform case, the Hamiltonian's non-Hermitian symmetries allow its eigenstates to be mapped to a pair of Hermitian Dirac subsystems. When a wave is transmitted across an interface between two spatially uniform domains with different model parameters, an anomalous form of Klein tunneling can occur, whereby reflection is suppressed while the transmitted flux is substantially higher or lower than the incident flux. The interface can even function as a simultaneous laser and coherent perfect absorber. Remarkably, the violation of flux conservation occurs entirely at the interface, as no wave amplification or damping takes place in the bulk. Moreover, at energies within the Dirac mass gaps, the interface can support exponentially localized boundary states with real energies. These features of the continuum model can also be reproduced in non-Hermitian lattice models.