Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Human-generated Demonstrations Necessary for In-context Learning?

Despite the promising few-shot ability of large language models (LLMs), the standard paradigm of In-context Learning (ICL) suffers the disadvantages of susceptibility to selected demonstrations and the intricacy to generate these demonstrations. In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL. To answer this question, we propose self-contemplation prompting strategy (SEC), a paradigm free from human-crafted demonstrations. The key point of SEC is that, instead of using hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create demonstrations on their own, based on which the final output is generated. SEC is a flexible framework and can be adapted to both the vanilla ICL and the chain-of-thought (CoT), but with greater ease: as the manual-generation process of both examples and rationale can be saved. Extensive experiments in arithmetic reasoning, commonsense reasoning, multi-task language understanding, and code generation benchmarks, show that SEC, which does not require hand-crafted demonstrations, significantly outperforms the zero-shot learning strategy, and achieves comparable results to ICL with hand-crafted demonstrations. This demonstrates that, for many tasks, contemporary LLMs possess a sufficient level of competence to exclusively depend on their own capacity for decision making, removing the need for external training data. Code is available at https://github.com/ruili33/SEC.
Segmentation-Free Streaming Machine Translation

Streaming Machine Translation (MT) is the task of translating an unbounded input text stream in real-time. The traditional cascade approach, which combines an Automatic Speech Recognition (ASR) and an MT system, relies on an intermediate segmentation step which splits the transcription stream into sentence-like units. However, the incorporation of a hard segmentation constrains the MT system and is a source of errors. This paper proposes a Segmentation-Free framework that enables the model to translate an unsegmented source stream by delaying the segmentation decision until the translation has been generated. Extensive experiments show how the proposed Segmentation-Free framework has better quality-latency trade-off than competing approaches that use an independent segmentation model. Software, data and models will be released upon paper acceptance.
FurNav: Development and Preliminary Study of a Robot Direction Giver

When giving directions to a lost-looking tourist, would you first reference the street-names, cardinal directions, landmarks, or simply tell them to walk five hundred metres in one direction then turn left? Depending on the circumstances, one could reasonably make use of any of these direction giving styles. However, research on direction giving with a robot does not often look at how these different direction styles impact perceptions of the robots intelligence, nor does it take into account how users prior dispositions may impact ratings. In this work, we look at generating natural language for two navigation styles using a created system for a Furhat robot, before measuring perceived intelligence and animacy alongside users prior dispositions to robots in a small preliminary study (N=7). Our results confirm findings by previous work that prior negative attitudes towards robots correlates negatively with propensity to trust robots, and also suggests avenues for future research. For example, more data is needed to explore the link between perceived intelligence and direction style. We end by discussing our plan to run a larger scale experiment, and how to improve our existing study design.
Simultaneous stratospheric measurements of H2O, HDO, and CH4 from balloon‐borne and aircraft infrared solar absorption spectra and tunable diode laser laboratory spectra of HDO

High-resolution infrared solar absorption spectra recorded from a balloon-borne platform at 33-km altitude and an aircraft at 12-km altitude have been analyzed to derive simultaneous stratospheric concentrations of H2O, HDO, and CH4. The data were obtained near 33°N with the University of Denver interferometer during a balloon flight in October 1979 and with the NCAR interferometer aboard a Sabreliner aircraft in September 1982. The analysis of the balloon spectra indicates the H2O mixing ratio increased from 3.5 to 4.8 ppmv and the CH4 mixing ratio decreased from 1.15 to 0.70 ppmv between 18 to 38 km. Tunable diode laser laboratory spectra were recorded and analyzed to derive absolute intensities and air-broadened half widths of selected lines of the v2 band of HDO. With these parameters, which are reported here, an HDO profile that increases from 0.61 ppbv near 18 km to 0.88 ppbv near 29 km is inferred from the balloon spectra, corresponding to a D/H ratio, normalized to the reference value of 158 atomic parts per million of Standard Mean Ocean Water (SMOW), of 0.55 to 0.67. The average D/H value of 0.40 above 12 km, derived from the aircraft spectra, provides additional spectroscopic evidence for a large depletion in the D content of water vapor in the lower stratosphere. The simultaneous vertical profiles for the three gases retrieved from the balloon spectra are consistent, within the estimated precision of the data (±10%), with methane oxidation as the sole source of stratospheric H2O in addition to H2O transported upward through the tropopause. However, there is an indication of a small increase in the total hydrogen in H2O and CH4 with altitude, and we believe more precise simultaneous measurements are required.
Online and Offline Dynamic Influence Maximization Games Over Social Networks

In this work, we consider dynamic influence maximization games over social networks with multiple players (influencers). The goal of each influencer is to maximize their own reward subject to their limited total budget rate constraints. Thus, influencers need to carefully design their investment policies considering individuals' opinion dynamics and other influencers' investment strategies, leading to a dynamic game problem. We first consider the case of a single influencer who wants to maximize its utility subject to a total budget rate constraint. We study both offline and online versions of the problem where the opinion dynamics are either known or not known a priori. In the singe-influencer case, we propose an online no-regret algorithm, meaning that as the number of campaign opportunities grows, the average utilities obtained by the offline and online solutions converge. Then, we consider the game formulation with multiple influencers in offline and online settings. For the offline setting, we show that the dynamic game admits a unique Nash equilibrium policy and provide a method to compute it. For the online setting and with two influencers, we show that if each influencer applies the same no-regret online algorithm proposed for the single-influencer maximization problem, they will converge to the set of $\epsilon$-Nash equilibrium policies where $\epsilon=O(\frac{1}{\sqrt{K}})$ scales in average inversely with the number of campaign times $K$ considering the average utilities of the influencers. Moreover, we extend this result to any finite number of influencers under more strict requirements on the information structure. Finally, we provide numerical analysis to validate our results under various settings.
Symmetry breaking and ascending in the magnetic kagome metal FeGe

Spontaneous symmetry breaking-the phenomenon where an infinitesimal perturbation can cause the system to break the underlying symmetry-is a cornerstone concept in the understanding of interacting solid-state systems. In a typical series of temperature-driven phase transitions, higher temperature phases are more symmetric due to the stabilizing effect of entropy that becomes dominant as the temperature is increased. However, the opposite is rare but possible when there are multiple degrees of freedom in the system. Here, we present such an example of a symmetry-ascending phenomenon in a magnetic kagome metal FeGe by utilizing neutron Larmor diffraction and Raman spectroscopy. In the paramagnetic state at 460K, we confirm that the crystal structure is indeed hexagonal kagome lattice. On cooling to TN, the crystal structure changes from hexagonal to monoclinic with in-plane lattice distortions on the order of 10^(-4) and the associated splitting of the double degenerate phonon mode of the pristine kagome lattice. Upon further cooling to TCDW, the kagome lattice shows a small negative thermal expansion, and the crystal structure becomes more symmetric gradually upon further cooling. Increasing the crystalline symmetry upon cooling is unusual, it originates from an extremely weak structural instability that coexists and competes with the CDW and magnetic orders. These observations are against the expectations for a simple model with a single order parameter, hence can only be explained by a Landau free energy expansion that takes into account multiple lattice, charge, and spin degrees of freedom. Thus, the determination of the crystalline lattice symmetry as well as the unusual spin-lattice coupling is a first step towards understanding the rich electronic and magnetic properties of the system and sheds new light on intertwined orders where the lattice degree of freedom is no longer dominant.
Noise-Tolerant Unsupervised Adapter for Vision-Language Models

Recent advances in large-scale vision-language models have achieved very impressive performance in various zero-shot image classification tasks. While prior studies have demonstrated significant improvements by introducing few-shot labelled target samples, they still require labelling of target samples, which greatly degrades their scalability while handling various visual recognition tasks. We design NtUA, a Noise-tolerant Unsupervised Adapter that allows learning superior target models with few-shot unlabelled target samples. NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few-shot unlabelled target samples as key-value pairs. It consists of two complementary designs. The first is adaptive cache formation that combats pseudo-label noises by weighting the key-value pairs according to their prediction confidence. The second is pseudo-label rectification, which corrects both pair values (i.e., pseudo-labels) and cache weights by leveraging knowledge distillation from large-scale vision language models. Extensive experiments show that NtUA achieves superior performance consistently across multiple widely adopted benchmarks.
Equatorial density irregularity structures at intermediate scales and their temporal evolution

We examine high resolution measurements of ion density in the equatorial ionosphere from the AE-E satellite during the years 1977–1981. Structure over spatial scales from 18 km to 200 m is characterized by the spectrum of irregularities at larger and smaller scales and at altitudes above 350 km and below 300 km. In the low-altitude region, only small amplitude large-scale (λ>5 km) density modulations are often observed, and thus the power spectrum of these density structures exhibits a steep spectral slope at kilometer scales. In the high-altitude region, sinusoidal density fluctuations, characterized by enhanced power near 1-km scale, are frequently observed during 2000–0200 LT. However, such fluctuations are confined to regions at the edges of larger bubble structures where the average background density is high. Small amplitude irregularity structures, observed at early local time hours, grow rapidly to high-intensity structures in about 90 min. Fully developed structures, which are observed at late local time hours, decay very slowly producing only small differences in spectral characteristics even 4 hours later. The local time evolution of irregularity structure is investigated by using average statistics for low-(1% 10%) structures. At lower altitudes, little change in the spectral slope is seen as a function of local time, while at higher altitudes the growth and maintenance of structures near 1 km scales dramatically affects the spectral slope.
Depolarized Holography with Polarization-Multiplexing Metasurface

The evolution of computer-generated holography (CGH) algorithms has prompted significant improvements in the performances of holographic displays. Nonetheless, they start to encounter a limited degree of freedom in CGH optimization and physical constraints stemming from the coherent nature of holograms. To surpass the physical limitations, we consider polarization as a new degree of freedom by utilizing a novel optical platform called metasurface. Polarization-multiplexing metasurfaces enable incoherent-like behavior in holographic displays due to the mutual incoherence of orthogonal polarization states. We leverage this unique characteristic of a metasurface by integrating it into a holographic display and exploiting polarization diversity to bring an additional degree of freedom for CGH algorithms. To minimize the speckle noise while maximizing the image quality, we devise a fully differentiable optimization pipeline by taking into account the metasurface proxy model, thereby jointly optimizing spatial light modulator phase patterns and geometric parameters of metasurface nanostructures. We evaluate the metasurface-enabled depolarized holography through simulations and experiments, demonstrating its ability to reduce speckle noise and enhance image quality.
RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation

Recently, the RGB images and point clouds fusion methods have been proposed to jointly estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras and LiDAR sensors adopt a frame-based data acquisition mechanism, their performance is limited by the fixed low sampling rates, especially in highly-dynamic scenes. By contrast, the event camera can asynchronously capture the intensity changes with a very high temporal resolution, providing complementary dynamic information of the observed scenes. In this paper, we incorporate RGB images, Point clouds and Events for joint optical flow and scene flow estimation with our proposed multi-stage multimodal fusion model, RPEFlow. First, we present an attention fusion module with a cross-attention mechanism to implicitly explore the internal cross-modal correlation for 2D and 3D branches, respectively. Second, we introduce a mutual information regularization term to explicitly model the complementary information of three modalities for effective multimodal feature learning. We also contribute a new synthetic dataset to advocate further research. Experiments on both synthetic and real datasets show that our model outperforms the existing state-of-theart by a wide margin. Code and dataset is available at https://npucvr.github. io/RPEFlow.
A Single Ejection Model of the DART/Dimorphos Debris Trail

The collision of the NASA DART spacecraft with asteroid Dimorphos resulted in the formation of a distinctive and long-lived debris trail, formed by the action of solar radiation pressure on ejected particles. This trail briefly displayed a double appearance, which has been interpreted as the result of a double ejection. We present a model that can produce a transient double trail without the need to assume a double ejection. Our model explains the appearance of the double trail as a projection of the cone walls when viewed from a large angle to the cone axis and avoids the problem of producing dust in two epochs from a single, instantaneous impact. The particles follow a broken power-law size distribution, with differential indices q = 2.7 ± 0.2 (1 μm ≤ a ≤ 2 mm), 3.9 ± 0.1 (2 mm < a ≤ 1 cm), and 4.2 ± 0.2 (1 cm < a ≤ 20 cm). We find that the total trail mass in particles from 1 μm to 20 cm in size (for an assumed density 3500 kg m−3) is ∼1.7 × 107 kg, rising to 2.2 × 107 kg, when extended to boulders up to 3.5 m in radius. This corresponds to 0.4%–0.6% of the mass of Dimorphos.
Markov Chain Mirror Descent On Data Federation

Stochastic optimization methods such as mirror descent have wide applications due to low computational cost. Those methods have been well studied under assumption of the independent and identical distribution, and usually achieve sublinear rate of convergence. However, this assumption may be too strong and unpractical in real application scenarios. Recent researches investigate stochastic gradient descent when instances are sampled from a Markov chain. Unfortunately, few results are known for stochastic mirror descent. In the paper, we propose a new version of stochastic mirror descent termed by MarchOn in the scenario of the federated learning. Given a distributed network, the model iteratively travels from a node to one of its neighbours randomly. Furthermore, we propose a new framework to analyze MarchOn, which yields best rates of convergence for convex, strongly convex, and non-convex loss. Finally, we conduct empirical studies to evaluate the convergence of MarchOn, and validate theoretical results.
A High-Resolution Global Sea Surface Temperature Climatology

Abstract In response to the development of a new higher-resolution sea surface temperature (SST) analysis at the National Meteorological Center (NMC), a new monthly 1° global sea surface temperature climatology was constructed from two intermediate climatologies: the 2° SST climatology presently used at NMC and a 1° SST climatology derived from the new analysis. The 2° SST climatology used a 30-yr 1950–1979 base period between roughly 40°S and 60°N based on in situ (ship and buoy) SST data supplemented by four years (1982–1985) of satellite SST retrievals. The 1° SST climatology was based on monthly analyses using in situ SST data, satellite SST retrievals, and sea-ice coverage data over a 12-yr period (1982–1993). The final climatology was combined from these two products so that a 1° resolution was maintained and the base period was adjusted to the 1950–1979 period wherever possible (approximately between 40°S and 60°N). Compared to the 2° climatology, the 1° climatology resolves equatorial upwelling an.
Using Remote Sensing and GIS for Assessing and Mapping Land Use and Land Qualities in the Hawkesbury‐Nepean River Catchment, Australia

Abstract A methodological framework of land resource assessment, based on integrated biophysical information of the land in GIS and remote sensing is presented. The model developed uses two primary sub‐models (modules): remote sensing based land use classification, and land suitability assessment. The first module uses Landsat 7 ETM+ images to produce a land use/land cover map of the study area. A site‐specific framework was developed for use in thematic information extraction involving the use of visual analysis, spectral‐based (automated) procedures, and ancillary information. The second sub‐model implements a fuzzy set methodology, and uses soil landscape data sets and a Digital Elevation Model as evaluation criteria. The databases used were all georeferenced and standardised in a GIS format, enabling spatial analyses for possible pairings of data sets to be performed in order to derive new information sub‐sets. A test case is presented to map the spatial distribution of runoff Curve Number (CN) using soil landscape data, which were also used for land suitability evaluation, along with existing land use/land cover information. The strategies and analytical procedures developed in this research may be useful in other areas, particularly in projects related to mapping biophysical resources for sustainable land use planning and management.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramatic and rapid changes in the world environment, the assumption that data obey Independent Identically Distribution is undermined by the subsequent changes in data distribution, known as concept drift, leading to weak replicability and transferability of the model over unseen data. To address the issue, previous approaches typically retrain the model, forcing it to fit the most recent observed data. However, retraining is problematic in that it leads to model lag, consumption of resources, and model re-invalidation, causing the drift problem to be not well solved in realistic scenarios. In this study, we propose a new urban time series prediction model for the concept drift problem, which encodes the drift by considering the periodicity in the data and makes on-the-fly adjustments to the model based on the drift using a meta-dynamic network. Experiments on real-world datasets show that our design significantly outperforms state-of-the-art methods and can be well generalized to existing prediction backbones by reducing their sensitivity to distribution changes.
Learning Generative Models for Climbing Aircraft from Radar Data

Accurate trajectory prediction for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from the data. The method offers three features: predictions of the arrival time with 66.3% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost.
Virtual Hyperspectral Images and Spectral Feature Extraction Using Symmetric Autoencoders

Spectral data acquired through remote sensing are invaluable for environmental and resource studies. However, these datasets are often marred by nuisance phenomena such as atmospheric interference, sensor noise and other complexities, posing significant challenges for accurate analysis. We show that an autoencoder architecture, called symmetric autoencoder (SymAE), which leverages symmetry under reordering of pixels, can learn to disentangle the influence of nuisance features from class-invariant spectral features on a pixel-by-pixel basis. This disentanglement is achieved through a purely data-driven process, obviating the need for transfer models, noise distribution priors, or reference `clean signals. ' Additionally, SymAE can generate virtual hyperspectral images by manipulating the nuisance effects of each pixel. Using AVIRIS instrument data, we demonstrate that these virtual images are valuable for subsequent image analysis tasks. The extraction of invariant spectral features within classes, enabled by SymAE, also positions it as highly effective in clustering and classification tasks. We demonstrate this by delivering state-of-the-art classification results for a purely spectral method across four popular benchmark HSI datasets.