Miracle-dz/UniFAR-FTUs huggingface dataset:
ConceptFusion: Open-set Multimodal 3D Mapping

Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approaches that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in recent work, using text prompts. We address both these issues with ConceptFusion, a scene representation that is (1) fundamentally open-set, enabling reasoning beyond a closed set of concepts and (ii) inherently multimodal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping. For more information, visit our project page https://concept-fusion.github. io or watch our 5-minute explainer video https://www.youtube. com/watch? v=rkXgws8fiDs
Analysis of optimal portfolio on finite and small-time horizons for a stochastic volatility model with multiple correlated assets

In this paper, we consider the portfolio optimization problem in a financial market where the underlying stochastic volatility model is driven by n-dimensional Brownian motions. At first, we derive a Hamilton-Jacobi-Bellman equation including the correlations among the standard Brownian motions. We use an approximation method for the optimization of portfolios. With such approximation, the value function is analyzed using the first-order terms of expansion of the utility function in the powers of time to the horizon. The error of this approximation is controlled using the second-order terms of expansion of the utility function. It is also shown that the one-dimensional version of this analysis corresponds to a known result in the literature. We also generate a close-to-optimal portfolio near the time to horizon using the first-order approximation of the utility function. It is shown that the error is controlled by the square of the time to the horizon. Finally, we provide an approximation scheme to the value function for all times and generate a close-to-optimal portfolio.
L–D dependence for supernova remnants and its connection with the Σ–D relation

We discuss the L‐D correlation (possible dependence of radio luminosity on linear diameter) for supernova remnants (SNRs) in order to see whether the � ‐D relation actually exists and whether determination of SNR distances on the basis of the � ‐D relation is possible. We do not find any significant correlation, except for the M82 starburst galaxy for which a good � ‐D relation does exist. Finally, we suggest that a similar relation might exist for Galactic SNRs associated with large molecular clouds, indicating once again that the density of the SNR environment is probably the crucial parameter in SNR evolution.
Spectral (Bin) Microphysics Coupled with a Mesoscale Model (MM5). Part II: Simulation of a CaPE Rain Event with a Squall Line

Abstract Spectral (bin) microphysics (SBM) has been implemented into the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The new model was used to simulate a squall line that developed over Florida on 27 July 1991. It is shown that SBM reproduces precipitation rate, rain amounts, and location, radar reflectivity, and cloud structure much better than bulk parameterizations currently implemented in MM5. Sensitivity tests show the importance of (i) raindrop breakup, (ii) in-cloud turbulence, (iii) different aerosol concentrations, and (iv) inclusion of scavenging of aerosols. Breakup decreases average and maximum rainfall. In-cloud turbulence enhances particle drop collision rates and increases rain rates. A “continental” aerosol concentration produces a much larger maximum rainfall rate versus that obtained with “maritime” aerosol concentration. At the same time accumulated rain is larger with maritime aerosol concentration. The scavenging of aerosols by nucleati.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark

We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer. We perform extensive evaluation with popular language models such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models still lag behind human performance when producing such structured reasoning steps. We believe this work will provide a way for the community to better train and test systems on multi-step reasoning and explanations in natural language.
Spin wave Hamiltonian and anomalous scattering in 
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We report a comprehensive spin wave analysis of the semiconducting honeycomb van der Waal antiferromagnet NiPS$_3$. Using single crystal inelastic neutron scattering, we map out the full Brillouin zone and fit the observed modes to a spin wave model with rigorously defined uncertainty. We find that the third neighbor exchange $J_3$ dominates the Hamiltonian, a feature which we fully account for by ab-initio density functional theory calculations. We also quantify the degree to which the three-fold rotation symmetry is broken and account for the $Q=0$ excitations observed in other measurements, yielding a spin exchange model which is consistent across multiple experimental probes. We also identify a strongly reduced static ordered moment and reduced low-energy intensity relative to the linear spin wave calculations, signaling unexplained features in the magnetism which requires going beyond the linear spin wave approximation.
Cross‐national comparison of internal migration: issues and measures

Summary. Our objectives are to identify the issues that researchers encounter when measuring internal migration in different countries and to propose key indicators that analysts can use to compare internal migration at the ‘national’ level. We establish the benefits to be gained by a rigorous approach to cross‐national comparisons of internal migration and discuss issues that affect such comparisons. We then distinguish four dimensions of internal migration on which countries can be compared and, for each dimension, identify a series of summary measures. We illustrate the issues and measures proposed by comparing migration in Australia and Great Britain.
Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey

Neurosymbolic AI is an increasingly active area of research that combines symbolic reasoning methods with deep learning to leverage their complementary benefits. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy to generate models that facilitate interpretability, maintain competitive performance, and integrate expert knowledge. Therefore, we survey methods that perform neurosymbolic reasoning tasks on knowledge graphs and propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the unique characteristics and limitations of these methods, then propose several prospective directions toward which this field of research could evolve.
The economics of reducing emissions from community managed forests in Nepal Himalaya

The climate change agenda is more important in global politics today than ever before. This research set out to examine whether community forest management (CFM) can play a signifi cant role in reducing global emissions, by taking Nepal’s community forestry sector as a case. The thesis selects three community managed forests in Nepal’s Himalaya region to investigate the extent to which management of such forests by the local communities can successfully contribute towards reducing global atmospheric CO2 concentration (Chapter 1). The results of this analysis are used to make policy recommendations as regards the formulation of the new climate treaty that is expected to replace the Kyoto Protocol (KP) after 2012. The thesis shows that climate change can be viewed essentially as a market failure and explains that, as a result, global efforts to mitigate this change are also based on market mechanisms. It is certainly expected that the new treaty to replace the KP will be also market oriented. Climate is a global public good or common resource that requires international management, so the nations have jointly developed the KP to combat the dangers of climate change by regulating emissions. This has largely been done through a cap-and-trade mechanism. This limits the emission levels a country or an industry can emit and then allows individual countries or fi rms to buy and sell credits.
Lero: A Learning-to-Rank Query Optimizer

A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model updating, stem from the inherent hardness of predicting the cost or latency of execution plans using machine learning models. In this paper, we introduce a learning-to-rank query optimizer, called Lero, which builds on top of a native query optimizer and continuously learns to improve the optimization performance. The key observation is that the relative order or rank of plans, rather than the exact cost or latency, is sufficient for query optimization. Lero employs a pairwise approach to train a classifier to compare any two plans and tell which one is better. Such a binary classification task is much easier than the regression task to predict the cost or latency, in terms of model efficiency and accuracy. Rather than building a learned optimizer from scratch, Lero is designed to leverage decades of wisdom of databases and improve the native query optimizer. With its non-intrusive design, Lero can be implemented on top of any existing DBMS with minimal integration efforts. We implement Lero and demonstrate its outstanding performance using PostgreSQL. In our experiments, Lero achieves near optimal performance on several benchmarks. It reduces the plan execution time of the native optimizer in PostgreSQL by up to 70% and other learned query optimizers by up to 37%. Meanwhile, Lero continuously learns and automatically adapts to query workloads and changes in data.
Comparing zeros of distinct Dirichlet L-functions

Let $\chi_1$ and $\chi_2$ be distinct primitive Dirichlet characters whose moduli are $q_1$ and $q_2$, respectively, and put $q:={\rm lcm}[q_1,q_2]\ge 3$. We show that for some absolute constants $C_1,C_2>0$, the $L$-functions $L(s,\chi_1)$ and $L(s,\chi_2)$ do not have the same zeros (counted with multiplicity) in the region $$ \big\{s=\sigma+it\in{\mathbb C}:0<\sigma<1, T<\gamma<T+C_1q^2\log T\big\} $$ for any real number $T\ge C_2\,q^2\log q$.
A Lanczos approach to the Adiabatic Gauge Potential

The Adiabatic Gauge Potential (AGP) is the generator of adiabatic deformations between quantum eigenstates. There are many ways to construct the AGP operator and evaluate the AGP norm. Recently, it was proposed that a Gram-Schmidt-type algorithm can be used to explicitly evaluate the expression of the AGP. We employ a version of this approach by using the Lanczos algorithm to evaluate the AGP operator in terms of Krylov vectors and the AGP norm in terms of the Lanczos coefficients. It has the advantage of minimizing redundancies in evaluating the nested commutators in the analytic expression for the AGP operator. The algorithm is used to explicitly construct the AGP operator for some simple systems. We derive an integral transform relation between the AGP norm and the autocorrelation function of the deformation operator. We present a modification of the Variational approach to derive the regulated AGP norm. Using this, we approximate the AGP to varying degrees of success. Finally, we compare and contrast the quantum-chaos-probing capacities of the AGP and K-complexity in view of the Operator Growth Hypothesis.
Stress as a governing parameter to control the crystallization of amorphous silicon films by thermal annealing

We have fabricated nanocrystalline dots by thermal annealing (TA) of thermal chemical-vapor-deposited amorphous silicon (a-Si:H) films. In S. Hazra, I. Sakata, M. Yamanaka, and E. Suzuki, Appl. Phys. Lett. 80, 1159 (2002), we observed that ultrathin a-Si:H films (2–10 nm) are stressed because of the presence of deformed crystallites or paracrystallites. With the increase of thickness, volume fraction of paracrystallites decreased in the films and stress in the films gradually reduced. Therefore, by changing the thickness, we can control the stress in the a-Si:H films and thereby the dimensions (in the range of 5 to 10 nm) as well as volume fractions of nanocrystallites formed by TA. On the other hand, it has been found that relaxed a-Si:H networks form polycrystalline films by TA.
Nonsingular complexiton solutions for two higher-dimensional fifth-order nonlinear integrable equations

In this work we construct multiple-periodic soliton solutions for two higher-dimensional fifth-order nonlinear integrable equations. The simplified Hirota method is used to carry out the analysis. By extending the real parameters into complex parameters, nonsingular periodic-soliton solutions, or complexiton solutions, are obtained for two specific coefficients of the higher-dimensional equations. The conditions of the existence of periodic-soliton solutions are derived.
Streaking single-electron ionization in open-shell molecules driven by x-ray pulses

We obtain continuum molecular wavefunctions for open-shell molecules in the Hartree-Fock framework. We do so while accounting for the singlet or triplet total spin symmetry of the molecular ion, that is, of the open-shell orbital and the initial orbital where the electron ionizes from. Using these continuum wavefunctions, we obtain the dipole matrix elements for a core electron that ionizes due to single-photon absorption by a linearly polarized X-ray pulse. After ionization from the X-ray pulse, we control or streak the electron dynamics using a circularly polarized infrared (IR) pulse. For a high intensity IR pulse and photon energies of the X-ray pulse close to the ionization threshold of the $1{\sigma}$ or $2{\sigma}$ orbitals, we achieve control of the angle of escape of the ionizing electron by varying the phase delay between the X-ray and IR pulses. For a low intensity IR pulse, we obtain final electron momenta distributions on the plane of the IR pulse and we find that many features of these distributions correspond to the angular patterns of electron escape solely due to the X-ray pulse.
On the Non-negative Integer Solutions to Diophantine Equations $F_n - F_m = 7^a$ and $F_n - F_m = 13^a$

In this paper, we study the solutions of the equation $F_n-F_m=p^a$ where $p$ is either $7$ or $13$ and $n>m\geqslant 0$, $a\geqslant 2$. We confirm the conjecture of Erduvan and Keskin by proving that there is no solutions for this Diophantine equation. We will use the lower bounds for linear forms in logarithms (Baker's theory) and a version of the Baker-Davenport reduction method in Diophantine approximation.
Joint Probability Trees

We introduce Joint Probability Trees (JPT), a novel approach that makes learning of and reasoning about joint probability distributions tractable for practical applications. JPTs support both symbolic and subsymbolic variables in a single hybrid model, and they do not rely on prior knowledge about variable dependencies or families of distributions. JPT representations build on tree structures that partition the problem space into relevant subregions that are elicited from the training data instead of postulating a rigid dependency model prior to learning. Learning and reasoning scale linearly in JPTs, and the tree structure allows white-box reasoning about any posterior probability $P(Q|E)$, such that interpretable explanations can be provided for any inference result. Our experiments showcase the practical applicability of JPTs in high-dimensional heterogeneous probability spaces with millions of training samples, making it a promising alternative to classic probabilistic graphical models.
Central exclusive diffractive production of a single photon in high-energy proton-proton collisions within the tensor-Pomeron approach

We discuss central-exclusive production (CEP) of photons via different fusion processes in the reaction $pp \to pp \gamma$ at high energies, available at RHIC and LHC, within the tensor-pomeron model. We consider two types of processes, the photoproduction contribution via the photon-pomeron and photon-reggeon fusion reactions, and the purely diffractive contribution via the reggeon-pomeron and odderon-pomeron fusion reactions. We present predictions for the measurements of photons at midrapidity, $|{\rm y}|<2.5$, and at relatively low transverse momentum, $0.1~{\rm GeV}<k_{\perp}<1~{\rm GeV}$. To check the main results of our study the measurement of the outgoing protons is not necessary. This is of relevance, e.g., for the present version of the ALICE detector at the LHC. Several differential distributions, for instance, in ${\rm y}$, $k_{\perp}$ and $\omega$, the rapidity, the absolute value of the transverse momentum, and the energy of the photon, respectively, are presented. We show that the photoproduction is an important process in the kinematic region specified above. There it gives a much larger cross section than diffractive bremsstrahlung. This is remarkable as the CEP cross section is of order $\alpha_{\rm em}^{3}$ whereas the bremsstrahlung one is only of order $\alpha_{\rm em}$. On the other hand, the soft-photon bremsstrahlung where the basic $pp \to pp$ reaction is due to strong interaction diffraction is more important than CEP in the forward rapidity range, $|{\rm y}|>4$, and/or at very low $k_{\perp}$. We leave it as a challenge for the planned ALICE 3 experiment at the LHC to study these two contributions to soft photon production in $pp$ collisions. This could shed new light on the so called ``soft photon puzzle'' in hadron-hadron collisions.
Memorization-Dilation: Modeling Neural Collapse Under Label Noise

The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and the features of different classes tend to separate as much as possible. Neural collapse is often studied through a simplified model, called the unconstrained feature representation, in which the model is assumed to have"infinite expressivity"and can map each data point to any arbitrary representation. In this work, we propose a more realistic variant of the unconstrained feature representation that takes the limited expressivity of the network into account. Empirical evidence suggests that the memorization of noisy data points leads to a degradation (dilation) of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon, we show one mechanism by which different losses lead to different performances of the trained network on noisy data. Our proofs reveal why label smoothing, a modification of cross-entropy empirically observed to produce a regularization effect, leads to improved generalization in classification tasks.
Analysis of dynamical effects in the uniform electron liquids with the self-consistent method of moments complemented by the Shannon information entropy and the path-integral Monte-Carlo simulations

Dynamical properties of uniform electron fluids (jellium model) are studied within a novel non-perturbative approach consisting in the combination of the self-consistent version of the method of moments (SCMM) involving up to nine sum rules and other exact relations, the two-parameter Shannon information entropy maximization procedure, and the ab initio path integral Monte Carlo (PIMC) simulations of the imaginary-time intermediate scattering function. The explicit dependence of the electronic dynamic structure factor (DSF) on temperature and density is studied in a broad realm of variation of the dimensionless parameters ($2\leq r_s\leq 36$ and $1\leq \theta \leq 8$). When the coupling is strong ($r_s\geq 16$) we clearly observe a bi-modal structure of the excitation spectrum with a lower-energy mode possessing a well pronounced roton-like feature ($\theta \leq 2$) and an additional high-energy branch within the roton region which evolves into the strongly overdamped high-frequency shoulder when the coupling decreases ($r_s\leq 10$). We are not aware of any reconstruction of the DSF at these conditions with the effects of dynamical correlations, included here via the intermediate scattering and the dynamical Nevanlinna parameter functions. The standard static-local-field approach fails to reproduce this effect. The reliability of our method is confirmed by a detailed comparison with the recent ab initio dynamic-local-field approach by Dornheim et al. [Phys.Rev.Lett. 121, 255001 (2018)] available for high/moderate densities ($r_s\leq 10$). Moreover, within the SCMM we are able to construct the modes dispersion equation in a closed analytical form and find the decrements (lifetimes) of the quasiparticle excitations explicitly. The physical nature of the revealed modes is discussed. Mathematical details of the method are complemented in the Supplementary Material.