Miracle-dz/UniFAR-FTUs huggingface dataset:
Singular order parameter interaction at the nematic quantum critical point in two-dimensional electron systems

We analyze the infrared behavior of effective N-point interactions between order parameter fluctuations for nematic and other quantum critical electron systems with a scalar order parameter in two dimensions. The interactions exhibit a singular momentum and energy dependence and thus cannot be represented by local vertices. They diverge for all N greater or equal 4 in a collinear static limit, where energy variables scale to zero faster than momenta, and momenta become increasingly collinear. The degree of divergence is not reduced by any cancellations and renders all N-point interactions marginal. A truncation of the order parameter action at quartic or any other finite order is therefore not justified. The same conclusion can be drawn for the effective action describing fermions coupled to a U(1) gauge field in two dimensions.
Fundamental Investigation of Reactive-Convective Transport: Implications for Long-Term Carbon Dioxide (Co2) Sequestration

The density-driven convection coupled with chemical reaction is the preferred mechanism for permanently storing CO2 in saline aquifers. This study uses a 2D visual Hele-Shaw cell to evaluate and visualize the density-driven convection formed due to gravitational instabilities, also known as Rayleigh-Taylor instability. The primary goal of the experiments is to understand the various mechanisms for the mass transfer of gaseous CO2 into brine with different initial ionic concentrations and flow permeability. Moreover, the impact of CO2 flow rates, injection locations, reservoir dipping angle, and permeability heterogeneity is also investigated. We observed that the presence of salts resulted in earlier onset of convection and a larger convective finger wavelength than the case with no dissolved salts. In addition, experimental data showed a higher lateral mixing between CO2 fingers when dipping is involved. The visual investigation also revealed that the CO2 dissolution rate, measured by the rate of the convective fingers advance, depends on the type and concentration of the ions present in the brine. The CO2 dissolution for solutions with varying salt dissolved, indicated by the area of the pH-depressed region, is observed to be 0.38-0.77 times compared to when no salt is present. Although convective flow is slowed down in the presence of salts, the diffusive flux is enhanced, as observed from both qualitative and quantitative results. Moreover, the reduced formation permeability, introduced by using a flow barrier, resulted in numerous regions not being swept by the dissolved CO2, indicating an inefficient dissolution. We also investigated the effect of discrete high conductivity fractures within the flow barriers, which showed an uneven vertical sweep and enhanced flow channeling. Lastly, the parameters regarding CO2 leakage risk during storage are identified and discussed.
The strong Brunn--Minkowski inequality and its equivalence with the CD condition

In the setting of essentially non-branching metric measure spaces, we prove the equivalence between the curvature dimension condition CD ( K, N ), in the sense of Lott–Sturm–Villani [Stu06a, Stu06b, LV09], and a newly introduced notion that we call strong Brunn–Minkowski inequality SBM ( K, N ). This condition is a reinforcement of the generalized Brunn–Minkowski inequality BM ( K, N ), which is known to hold in CD ( K, N ) spaces. Our result is a ﬁrst step towards providing a full equivalence between the CD ( K, N ) condition and the validity of BM ( K, N ), which has been recently proved in [MPR22] in the framework of weighted Riemannian manifolds.
Recitation-Augmented Language Models

We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs' own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of \method~on four pre-trained models (PaLM, UL2, OPT, and Codex) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Our code is available at"https://github.com/Edward-Sun/RECITE".
Public Transit Arrival Prediction: a Seq2Seq RNN Approach

Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation etc. The developing world in particular is plagued by additional factors like lack of lane discipline, excess vehicles, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. A novel data-driven model based on recurrent neural networks (RNNs) is proposed for BATP (in real-time) in the current work. The model intelligently incorporates both spatial and temporal correlations in a unique (non-linear) fashion distinct from existing approaches. In particular, we propose a Gated Recurrent Unit (GRU) based Encoder-Decoder(ED) OR Seq2Seq RNN model (originally introduced for language translation) for BATP. The geometry of the dynamic real time BATP problem enables a nice fit with the Encoder-Decoder based RNN structure. We feed relevant additional synchronized inputs (from previous trips) at each step of the decoder (a feature classically unexplored in machine translation applications). Further motivated from accurately modelling congestion influences on travel time prediction, we additionally propose to use a bidirectional layer at the decoder (something unexplored in other time-series based ED application contexts). The effectiveness of the proposed algorithms is demonstrated on real field data collected from challenging traffic conditions. Our experiments indicate that the proposed method outperforms diverse existing state-of-art data-driven approaches proposed for the same problem.
Quantum communication complexity of linear regression

Quantum computers may achieve speedups over their classical counterparts for solving linear algebra problems. However, in some cases – such as for low-rank matrices – dequantised algorithms demonstrate that there cannot be an exponential quantum speedup. In this work, we show that quantum computers have provable polynomial and exponential speedups in terms of communication complexity for some fundamental linear algebra problems if there is no restriction on the rank. We mainly focus on solving linear regression and Hamiltonian simulation. In the quantum case, the task is to prepare the quantum state of the result. To allow for a fair comparison, in the classical case, the task is to sample from the result. We investigate these two problems in two-party and multiparty models, propose near-optimal quantum protocols and prove quantum/classical lower bounds. In this process, we propose an efficient quantum protocol for quantum singular value transformation, which is a powerful technique for designing quantum algorithms. We feel this will be helpful in developing efficient quantum protocols for many other problems.
Two-fluid Hydrodynamics of a quasi-1D unitary Fermi gas

This thesis is devoted to the study of the hydrodynamic behavior of the unitary Fermi gas trapped by a highly elongated harmonic potential. Propagation of sound is one of the most exciting features exhibited by interacting many-body systems. It provides crucial information on the dynamic behavior of the system as well as on key thermodynamic quantities. The propagation of sound is particularly interesting in superfluids where two-fluid hydrodynamic theory predicts the occurrence of two different sounds: first sound, where the normal and superfluid component oscillate in phase, and second sound, where the two components oscillate with opposite phase. In the thesis, we investigate the propagation of sound waves of the unitary Fermi gas in a cylindrical geometry by solving the equations of two-fluid hydrodynamics in the `1D' scenario at finite temperature. The relevant thermodynamic functions entering the hydrodynamic equations are discussed in the superfluid and normal regimes in terms of universal scaling functions. Both the first sound and second sound solutions are calculated as a function of temperature and the role of the superfluid density is explicitly pointed out. The density fluctuations in the second sound wave are found to be large enough to be measured as a consequence of the finite thermal expansion coefficient of the gas, which is the strategy used in a recent experiment carried out at Innsbruck where second sound was detected in the unitary Fermi gas. We also provide an investigation of the temperature dependence of the collective oscillations of first sound nature exhibited by a highly elongated harmonically trapped Fermi gas at unitarity, including the region below the critical temperature for superfluidity. Differently from the lowest axial breathing mode, the hydrodynamic frequencies of the higher-nodal excitations show a temperature dependence, which is calculated starting from Landau two-fluid theory and using the available experimental knowledge of the equation of state.
Bulk strong matter: the trinity

ABSTRACT Our world is wonderful because of the normal but negligibly small baryonic part (i.e. atoms) although unknown dark matter and dark energy dominate the Universe. A stable atomic nucleus could be simply termed as ‘strong matter’ since its nature is dominated by the fundamental strong interaction. Is there any other form of strong matter? Although nuclei are composed of 2-flavoured (i.e. up and down flavours of valence quarks) nucleons, it is conjectured that bulk strong matter could be 3-flavoured (with additional strange quarks) if the baryon number exceeds the critical value, Ac in which case quarks could be either free (so-called strange quark matter) or localized (in strangeons, coined by combining ‘strange nucleon’). Bulk strong matter could be manifested in the form of compact stars, cosmic rays, and even dark matter. This trinity will be explained in this brief review that may impact dramatically on today’s physics, particularly in the era of multi-messenger astronomy after the discovery of gravitational wave. GRAPHICAL abstract
Robot Task Planning and Situation Handling in Open Worlds

Automated task planning algorithms have been developed to help robots complete complex tasks that require multiple actions. Most of those algorithms have been developed for"closed worlds"assuming complete world knowledge is provided. However, the real world is generally open, and the robots frequently encounter unforeseen situations that can potentially break the planner's completeness. This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense. In particular, common sense is extracted from Large Language Models based on the current task at hand and robot skills. For systematic evaluations, we collected a dataset that includes 561 execution-time situations in a dining domain, where each situation corresponds to a state instance of a robot being potentially unable to complete a task using a solution that normally works. Experimental results show that our approach significantly outperforms competitive baselines from the literature in the success rate of service tasks. Additionally, we have demonstrated COWP using a mobile manipulator. Supplementary materials are available at: https://cowplanning.github. io/
Transformation hydrodynamic metamaterials: Rigorous arguments on form invariance and structured design with spatial variance

The method of transformation optics has been a powerful tool to manipulate physical fields if governing equations are formally invariant under coordinate transformations. However, regulation of hydrodynamics is still far from satisfactory due to the lack of rigorous arguments on the validation of transformation theory for various categories of fluids. In this paper, we systematically investigate the applicability of transformation optics to fluid mechanics. We find that the Stokes equation and the Navier-Stokes equations, respectively describing the Stokes flow and general flow, will alter their forms under curvilinear transformations. On the contrary, the Hele-Shaw flow characterized with shallow geometries rigidly retain the form of its governing equation under arbitrary transformations. Based on the derived transformation rules, we propose the design of multilayered structures with spatially varying cell depth, instead of engineering the rank-2 shear viscosity tensor, to realize the required anisotropy of transformation Hele-Shaw hydrodynamic metamaterials. The theoretical certify and fabrication method revealed in this work may pave an avenue for precisely controlling flow distribution with the concept of artificial structure design.
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima

We consider Sharpness-Aware Minimization (SAM), a gradient-based optimization method for deep networks that has exhibited performance improvements on image and language prediction problems. We show that when SAM is applied with a convex quadratic objective, for most random initializations it converges to a cycle that oscillates between either side of the minimum in the direction with the largest curvature, and we provide bounds on the rate of convergence. In the non-quadratic case, we show that such oscillations effectively perform gradient descent, with a smaller step-size, on the spectral norm of the Hessian. In such cases, SAM's update may be regarded as a third derivative -- the derivative of the Hessian in the leading eigenvector direction -- that encourages drift toward wider minima.
Informed Systematic Method to Identify Variable Mid- and Late-T Dwarfs

The majority of brown dwarfs show some level of photometric or spectrophotometric variability in different wavelength ranges. This variability allow us to trace the 3D atmospheric structures of variable brown dwarfs and directly-imaged exoplanets with radiative-transfer models and mapping codes. Nevertheless, to date, we do not have an informed method to preselect the brown dwarfs that might show a higher variability amplitude for a thorough variability study. In this work, we designed and tested near-infrared spectral indices to preselect the most likely variable mid- and late-T dwarfs, which overlap in effective temperatures with directly-imaged exoplanets. We used time-resolved near-infrared Hubble Space Telescope Wide Field Camera 3 spectra of a T6.5 dwarf, 2MASS J22282889–431026, to design our novel spectral indices. We tested these spectral indices on 26 T5.5–T7.5 near-infrared SpeX/IRTF spectra, and we provided eight new mid- and late-T variable candidates. We estimated the variability fraction of our sample as 38−30+4 %, which agrees with the variability fractions provided by Metchev for mid- to late-T dwarfs. In addition, two of the three previously known variables in our sample of SpeX spectra are flagged as variable candidates by our indices. Similarly, all seven known nonvariables in our sample are flagged as nonvariable objects by our indices. These results suggest that our spectral indices might be used to find variable mid- and late-T brown dwarf variables. These indices may be crucial in the future to select cool directly-imaged exoplanets for variability studies.
CK Vul: evolving nebula and three curious background stars

We analyse the remnants of CK Vul (Nova Vul 1670) using optical imaging and spectroscopy. The imaging, obtained between 1991 and 2010, spans 5.6% of the life-time of the nebula. The flux of the nebula decreased during the last 2 decades. The central source still maintains the ionization of the innermost part of the nebula, but recombination proceeds in more distant parts of the nebula. Surprisingly, we discovered two stars located within 10 arcsec of the expansion centre of the radio emission that are characterized by pronounced long term variations and one star with high proper motion. The high proper motion star is a foreground object, and the two variable stars are background objects. The photometric variations of two variables are induced by a dusty cloud ejected by CK Vul and passing through the line of sight to those stars. The cloud leaves strong lithium absorption in the spectra of the stars. We discuss the nature of the object in terms of recent observations.
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive learning requires rich and diverse examples as keys and anchors, we leverage a prototype memory bank capturing class-wise global dataset information efficiently into a small number of prototypes acting as keys. An entropy-driven sampling technique then allows us to select good pixels from predictions as anchors. Experiments on three projection-based backbones show we outperform baselines on three challenging real-world outdoor datasets, working with as low as 0.001% annotations.
Effect of metallicity on the gravitational-wave signal from the cosmological population of compact binary coalescences

Recent studies on stellar evolution have shown that the properties of compact objects strongly depend on metallicity of the environment in which they were formed. In this work, we study how the metallicity of the stellar population can affect unresolved gravitational waves background from extragalactic compact binaries. We obtain a suit of models using population synthesis code, estimate the gravitational wave background they produce and discuss its detectability with second (Advanced LIGO, Advanced Virgo) and third (Einstein Telescope) generation detectors. Our results show that the background is dominated by binary black holes for all considered models in the frequency range of terrestrial detectors, and that it could be detected in most cases by Advanced LIGO/Virgo, and with Einstein Telescope with a very high signal to noise ratio.
Rethinking Lipschitz Neural Networks for Certiﬁed L-inﬁnity Robustness

Designing neural networks with bounded Lipschitz constant is a promising way to obtain certiﬁably robust classiﬁers against adversarial examples. However, the relevant progress for the important (cid:96) ∞ perturbation setting is rather limited, and a principled understanding of how to design expressive (cid:96) ∞ Lipschitz networks is still lacking. In this paper, we bridge the gap by studying certiﬁed (cid:96) ∞ robustness from a novel perspective of representing Boolean functions. We derive two fundamental impossibility results that hold for any standard Lipschitz network: one for robust classiﬁcation on ﬁnite datasets, and the other for Lipschitz function approximation. These results identify that networks built upon norm-bounded afﬁne layers and Lip-schitz activations intrinsically lose expressive power even in the two-dimensional case, and shed light on how recently proposed Lipschitz networks (e.g., GroupSort and (cid:96) ∞ -distance nets) bypass these impossibilities by leveraging order statistic functions . Finally, based on these insights, we develop a uniﬁed Lipschitz network that generalizes prior works, and design a practical version that can be efﬁciently trained (making certiﬁed robust training free). Extensive experiments show that our approach is scalable, efﬁcient, and consistently yields better certiﬁed robustness across multiple datasets and perturbation radii than prior Lipschitz networks.
ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives

Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g. , Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g. , Probabilistic Movement Primitives (ProMPs). To date, however, there is no MP method that unifies both, i. e. that can generate smooth trajectories from an arbitrary initial state while capturing higher-order statistics. In this letter, we introduce a unified perspective of both approaches by solving the ODE underlying the DMPs. We convert expensive online numerical integration of DMPs into position and velocity basis functions that can be used to represent trajectories or trajectory distributions similar to ProMPs while maintaining all the properties of dynamical systems. Since we inherit the properties of both methodologies, we call our proposed model Probabilistic Dynamic Movement Primitives (ProDMPs). Additionally, we embed ProDMPs in deep neural network architecture and propose a new cost function for efficient end-to-end learning of higher-order trajectory statistics. To this end, we leverage Bayesian Aggregation for non-linear iterative conditioning on sensory inputs. Our proposed model achieves smooth trajectory generation, goal-attractor convergence, correlation analysis, nonlinear conditioning, and online re-planing in one framework.
Quantum fluctuation relations for ensembles of wave functions

New quantum fluctuation relations are presented. In contrast with the standard approach, where the initial state of the driven system is described by the (micro) canonical density matrix, here we assume that it is described by a (micro) canonical distribution of wave functions, as originally proposed by Schrödinger. While the standard fluctuation relations are based on von Neumann measurement postulate, these new fluctuation relations do not involve any quantum collapse, but involve instead a notion of work as the change in expectation of the Hamiltonian.
NuSTAR OBSERVATIONS OF MAGNETAR 1E 1048.1–5937

We report on simultaneous Nuclear Spectroscopic Telescope Array (NuSTAR) and XMM-Newton observations of the magnetar 1E 1048.1−5937, along with Rossi X-ray Timing Explorer (RXTE) data for the same source. The NuSTAR data provide a clear detection of this magnetar’s persistent emission up to 20 keV. We detect a previously unreported small secondary peak in the average pulse profile in the 7–10 keV band, which grows to an amplitude comparable to that of the main peak in the 10–20 keV band. We show using RXTE data that this secondary peak is likely transient. We find that the pulsed fraction increases with energy from a value of ∼0.55 at ∼2 keV to a value of ∼0.75 near 8 keV but shows evidence of decreasing at higher energies. After filtering out multiple bright X-ray bursts during the observation, we find that the phase-averaged spectrum from combined NuSTAR and XMM data is well described by an absorbed double blackbody plus power-law model, with no evidence for the spectral turn-up near ∼10 keV as has been seen in some other magnetars. Our data allow us to rule out a spectral turn-up similar to those seen in magnetars 4U 0142+61 and 1E 2259+586 of ΔΓ ≳ 2, where ΔΓ is the difference between the soft-band and hard-band photon indexes. The lack of spectral turn-up is consistent with what has been observed from an active subset of magnetars given previously reported trends suggesting that the degree of spectral turn-up is correlated with spin-down rate and/or spin-inferred magnetic field.