Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaching Human-Level Forecasting with Language Models

Forecasting future events is important for policy and decision making. In this work, we study whether language models (LMs) can forecast at the level of competitive human forecasters. Towards this goal, we develop a retrieval-augmented LM system designed to automatically search for relevant information, generate forecasts, and aggregate predictions. To facilitate our study, we collect a large dataset of questions from competitive forecasting platforms. Under a test set published after the knowledge cut-offs of our LMs, we evaluate the end-to-end performance of our system against the aggregates of human forecasts. On average, the system nears the crowd aggregate of competitive forecasters, and in some settings surpasses it. Our work suggests that using LMs to forecast the future could provide accurate predictions at scale and help to inform institutional decision making.
Extracting quantum-critical properties from directly evaluated enhanced perturbative continuous unitary transformations

Directly evaluated enhanced perturbative continuous unitary transformations (deepCUTs) are used to calculate non-perturbatively extrapolated numerical data for the ground-state energy and the energy gap. The data coincides with the perturbative series up to the order with respect to which the deepCUT is truncated. We develop a general scheme to extract quantum-critical properties from the deepCUT data based on critical scaling and a strict correspondence between the truncation used for deepCUT and the length scale of correlations at the critical point. We apply our approach to transverse-field Ising models (TFIMs) as paradigmatic systems for quantum phase transitions of various universality classes depending on the lattice geometry and the choice of antiferromagnetic or ferromagnetic coupling. In particular, we focus on the quantum phase diagram of the bilayer antiferromagnetic TFIM on the triangular lattice with an Ising-type interlayer coupling. Without a field, the model is known to host a classically disordered ground state, and in the limit of decoupled layers it exhibits the 3d-XY 'order by disorder' transition of the corresponding single-layer model. Our starting point for the unknown parts of the phase diagram is a high-order perturbative calculation about the limit of isolated dimers where the model is in a gapped phase.
VideoMAC: Video Masked Autoencoders Meet ConvNets

Recently, the advancement of self-supervised learning techniques, like masked autoencoders (MAE), has greatly influenced visual representation learning for images and videos. Nevertheless, it is worth noting that the predominant approaches in existing masked image / video modeling rely excessively on resource-intensive vision transformers (ViTs) as the feature encoder. In this paper, we propose a new approach termed as \textbf{VideoMAC}, which combines video masked autoencoders with resource-friendly ConvNets. Specifically, VideoMAC employs symmetric masking on randomly sampled pairs of video frames. To prevent the issue of mask pattern dissipation, we utilize ConvNets which are implemented with sparse convolutional operators as encoders. Simultaneously, we present a simple yet effective masked video modeling (MVM) approach, a dual encoder architecture comprising an online encoder and an exponential moving average target encoder, aimed to facilitate inter-frame reconstruction consistency in videos. Additionally, we demonstrate that VideoMAC, empowering classical (ResNet) / modern (ConvNeXt) convolutional encoders to harness the benefits of MVM, outperforms ViT-based approaches on downstream tasks, including video object segmentation (+\textbf{5.2\%} / \textbf{6.4\%} $\mathcal{J}\&\mathcal{F}$), body part propagation (+\textbf{6.3\%} / \textbf{3.1\%} mIoU), and human pose tracking (+\textbf{10.2\%} / \textbf{11.1\%} PCK@0.1).
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review

The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. This marks a fundamental paradigm shift in the realm of recommendations. Amidst the dynamic research landscape, researchers actively harness the language comprehension and generation capabilities of LLMs to redefine the foundations of recommendation tasks. The investigation thoroughly explores the inherent strengths of LLMs within recommendation frameworks, encompassing nuanced contextual comprehension, seamless transitions across diverse domains, adoption of unified approaches, holistic learning strategies leveraging shared data reservoirs, transparent decision-making, and iterative improvements. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations, necessitating continuous refinement and evolution in LLM-driven recommender systems.
Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series

Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (PGDSs) are newly-developed methods, which can well capture the expressive latent transition structure and bursty dynamics behind count sequences. In particular, PGDSs demonstrate superior performance in terms of data imputation and prediction, compared with canonical linear dynamical system (LDS) based methods. Despite these advantages, PGDS cannot capture the heterogeneous overdispersed behaviours of the underlying dynamic processes. To mitigate this defect, we propose a negative-binomial-randomized gamma Markov process, which not only significantly improves the predictive performance of the proposed dynamical system, but also facilitates the fast convergence of the inference algorithm. Moreover, we develop methods to estimate both factor-structured and graph-structured transition dynamics, which enable us to infer more explainable latent structure, compared with PGDSs. Finally, we demonstrate the explainable latent structure learned by the proposed method, and show its superior performance in imputing missing data and forecasting future observations, compared with the related models.
Transition of the semiclassical resonance widths across a tangential crossing energy-level

We consider a 1D $2\times 2$ matrix-valued operator \eqref{System0} with two semiclassical Schr\"odinger operators on the diagonal entries and small interactions on the off-diagonal ones. When the two potentials cross at a turning point with contact order $n$, the corresponding two classical trajectories at the crossing level intersect at one point in the phase space with contact order $2n$. We compute the transfer matrix at this point between the incoming and outgoing microlocal solutions and apply it to the semiclassical distribution of resonances at the energy crossing level. It is described in terms of a generalized Airy function. This result generalizes \cite{FMW1} to the tangential crossing and \cite{AFH1} to the crossing at a turning point.
Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

As the role of artificial intelligence becomes increasingly pivotal in modern society, the efficient training and deployment of deep neural networks have emerged as critical areas of focus. Recent advancements in attention-based large neural architectures have spurred the development of AI accelerators, facilitating the training of extensive, multi-billion parameter models. Despite their effectiveness, these powerful networks often incur high execution costs in production environments. Neuromorphic computing, inspired by biological neural processes, offers a promising alternative. By utilizing temporally-sparse computations, Spiking Neural Networks (SNNs) offer to enhance energy efficiency through a reduced and low-power hardware footprint. However, the training of SNNs can be challenging due to their recurrent nature which cannot as easily leverage the massive parallelism of modern AI accelerators. To facilitate the investigation of SNN architectures and dynamics researchers have sought to bridge Python-based deep learning frameworks such as PyTorch or TensorFlow with custom-implemented compute kernels. This paper introduces Spyx, a new and lightweight SNN simulation and optimization library designed in JAX. By pre-staging data in the expansive vRAM of contemporary accelerators and employing extensive JIT compilation, Spyx allows for SNN optimization to be executed as a unified, low-level program on NVIDIA GPUs or Google TPUs. This approach achieves optimal hardware utilization, surpassing the performance of many existing SNN training frameworks while maintaining considerable flexibility.
MOSAIC: A Modular System for Assistive and Interactive Cooking

We present MOSAIC, a modular architecture for home robots to perform complex collaborative tasks, such as cooking with everyday users. MOSAIC tightly collaborates with humans, interacts with users using natural language, coordinates multiple robots, and manages an open vocabulary of everyday objects. At its core, MOSAIC employs modularity: it leverages multiple large-scale pre-trained models for general tasks like language and image recognition, while using streamlined modules designed for task-specific control. We extensively evaluate MOSAIC on 60 end-to-end trials where two robots collaborate with a human user to cook a combination of 6 recipes. We also extensively test individual modules with 180 episodes of visuomotor picking, 60 episodes of human motion forecasting, and 46 online user evaluations of the task planner. We show that MOSAIC is able to efficiently collaborate with humans by running the overall system end-to-end with a real human user, completing 68.3% (41/60) collaborative cooking trials of 6 different recipes with a subtask completion rate of 91.6%. Finally, we discuss the limitations of the current system and exciting open challenges in this domain. The project's website is at https://portal-cornell.github. io/MOSAIC/
Gravity Data Analysis and 3D Modeling of the Caribe-South America Boundary (76°– 64° W)

A new gravity data compilation for Venezuela reprocessed and homogenized. Analysis of the gravity maps using different techniques. In this work, results of independent detailed geophysical surveys were used in conjunction with the reprocessed gravity data in the construction of 3-D density model to study of the tectonic of the Caribbean-South America plate boundary.
Status of corals and reef fishes community near mining operation site in Tubay, Agusan del Norte, Philippines.

This study aims to determine the (a) percent cover of benthic community; (b) species richness, diversity, abundance and biomass of reef fishes; (c) correlation between live hard coral cover and reef species richness, abundance and biomass; and (d) sedimentation rate of the four sampling stations near mining operation site in Tubay, Agusan del Norte. Regarding the live hard coral cover, Punta Binuangan and Binuangan Dako have Fair Coral Cover (25-49.9%). Punta Binuangan noted of having fair and moderate fish diversity and fish density indices. Concerning fish biomass Pier 5 fall under very low category (1-3.0 mt km y) while the remaining stations fall under low biomass category. Among the four sampling stations, Pier 5, near mining operation obtained the highest sedimentation rate (1.896 mg cm d); highest abiotic component (48.48%), but Poor Coral Cover (< 24.9%), very poor reef fish richness and very low fish biomass.
Primordial Magnetic Fields in Light of Dark Ages Global 21-cm Signal

We study the constraints on the primordial magnetic fields in light of Dark Ages global 21-cm signal. An early absorption signal in the redshift of $200 \leq z \leq 30$ is predicted in the $\Lambda\rm CDM$ model of cosmology. During the Dark Ages, there were no stars, therefore, measuring the global 21-cm signal can provide pristine cosmological information. However, measuring the Dark Ages global 21-cm signal from ground-based telescopes is challenging. To overcome this difficulty, recently lunar and space-based experiments have been proposed, such as FARSIDE, DAPPER, FarView, etc. Primordial magnetic fields can heat the intergalactic medium gas via magnetohydrodynamic effects. We study the effects of magnetic fields on the Dark Ages global 21-cm signal and constrain the present-day strength of primordial magnetic fields and the spectral index. We find that measuring the Dark Ages signal can provide stronger bounds compared to the existing constraints from Planck 2016. Additionally, we also explore the dark-ages consistency ratio which can identify the magnetic heating of IGM by measuring the 21-cm signal at only three different redshifts in future experiments.
Limits of noisy quantum metrology with restricted quantum controls

The Heisenberg limit (HL) and the standard quantum limit (SQL) are two quantum metrological limits, which describe the scalings of estimation precision $\Delta \hat\theta$ of an unknown parameter $\theta$ with respect to $n$, the number of one-parameter quantum channels applied. It was known that the HL ($\Delta \hat\theta \propto 1/n$) is achievable using quantum error correction (QEC) strategies when the ``Hamiltonian-not-in-Kraus-span'' (HNKS) condition is satisfied; and when HNKS is violated, the SQL ($\Delta \hat\theta \propto 1/n^{1/2}$) is optimal and can be achieved with $n$ repeated measurements. However, it is unknown whether such limits are still achievable using restricted quantum devices where the required QEC operations are not available -- e.g., finite-size devices where only unitary controls are available or where noiseless ancilla is not available. In this work, we identify various new noisy metrological limits for estimating one-parameter qubit channels in different settings with restricted controls. The HL is proven to be unattainable in these cases, indicating the necessity of QEC in achieving the HL. Furthermore, we find a necessary and sufficient condition for qubit channels to attain the SQL, called the ``rotation-generators-not-in-Kraus-span'' (RGNKS) condition. When RGNKS is satisfied, the SQL is achievable using only unitary controls and a single measurement. When RGNKS is violated, the estimation precision (in most cases) has a constant floor when repeated measurements are not allowed. Demonstration of this separation in metrological powers is within reach of current quantum technologies.
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective

Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing privacy protection by avoiding sharing private data directly. However, the potential risks associated with such an approach have not been comprehensively examined. In this paper, we take an adversarial perspective to investigate the potential privacy and fairness risks associated with the sharing of diffusion models. Specifically, we investigate the circumstances in which one party (the sharer) trains a diffusion model using private data and provides another party (the receiver) black-box access to the pre-trained model for downstream tasks. We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver's downstream models by manipulating the training data distribution of the diffusion model. Meanwhile, the receiver can perform property inference attacks to reveal the distribution of sensitive features in the sharer's dataset. Our experiments conducted on real-world datasets demonstrate remarkable attack performance on different types of diffusion models, which highlights the critical importance of robust data auditing and privacy protection protocols in pertinent applications.
PeLLE: Encoder-based language models for Brazilian Portuguese based on open data

In this paper we present PeLLE, a family of large language models based on the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open data from the Carolina corpus. Aiming at reproducible results, we describe details of the pretraining of the models. We also evaluate PeLLE models against a set of existing multilingual and PT-BR refined pretrained Transformer-based LLM encoders, contrasting performance of large versus smaller-but-curated pretrained models in several downstream tasks. We conclude that several tasks perform better with larger models, but some tasks benefit from smaller-but-curated data in its pretraining.
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation

Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on multiple local datasets across different users and server-based secure aggregation of the individual estimators' updates. PrivatEyes guarantees that individual gaze data remains private even if a majority of the aggregating servers is malicious. We also introduce a new data leakage attack DualView that shows that PrivatEyes limits the leakage of private training data more effectively than previous approaches. Evaluations on the MPIIGaze, MPIIFaceGaze, GazeCapture, and NVGaze datasets further show that the improved privacy does not lead to a lower gaze estimation accuracy or substantially higher computational costs - both of which are on par with its non-secure counterparts.
Ecotourism, water quality and wet tropics streams

[Extract] This project aimed to investigate the relationships between stream-based ecotourism and environmental quality in the Wet Tropics of Queensland by: 1. examining the impact of tourism on streams and in terms of water quality and selected ecological indicators; 2. examining the effects of environmental quality on the quality of the tourism experience; 3. developing a conceptual model interrelating two measurable parameters of stream tourist foci, namely the quality of the tourism experience and the quality of the environment; 4. providing preliminary guidelines for management of stream-based tourism sites.
Efficient learning of quantum states prepared with few fermionic non-Gaussian gates

The experimental realization of increasingly complex quantum states underscores the pressing need for new methods of state learning and verification. In one such framework, quantum state tomography, the aim is to learn the full quantum state from data obtained by measurements. Without prior assumptions on the state, this task is prohibitively hard. Here, we present an efficient algorithm for learning states on $n$ fermion modes prepared by any number of Gaussian and at most $t$ non-Gaussian gates. By Jordan-Wigner mapping, this also includes $n$-qubit states prepared by nearest-neighbour matchgate circuits with at most $t$ SWAP-gates. Our algorithm is based exclusively on single-copy measurements and produces a classical representation of a state, guaranteed to be close in trace distance to the target state. The sample and time complexity of our algorithm is $\mathrm{poly}(n,2^t)$; thus if $t=O(\log(n))$, it is efficient. We also show that, if $t$ scales slightly more than logarithmically, any learning algorithm to solve the same task must be inefficient, under common cryptographic assumptions. We also provide an efficient property testing algorithm that, given access to copies of a state, determines whether such state is far or close to the set of states for which our learning algorithm works. Beyond tomography, our work sheds light on the structure of states prepared with few non-Gaussian gates and offers an improved upper bound on their circuit complexity.
Helios Observations of Quasiperiodic Density Structures in the Slow Solar Wind at 0.3, 0.4, and 0.6 AU

Following previous investigations of quasiperiodic plasma density structures in the solar wind at 1 AU, we show using the Helios1 and Helios2 data their first identification in situ in the inner heliosphere at 0.3, 0.4, and 0.6 AU. We present five events of quasiperiodic density structures with time scales ranging from a few minutes to a couple of hours in slow solar wind streams. Where possible, we locate the solar source region of these events using photospheric field maps from the Mount Wilson Observatory as input for the Wang‐Sheeley‐Arge model. The detailed study of the plasma properties of these structures is fundamental to understanding the physical processes occurring at the origin of the release of solar wind plasma. Temperature changes associated with the density structures are consistent with these periodic structures developing in the solar atmosphere as the solar wind is formed. One event contains a flux rope, suggesting that the solar wind was formed as magnetic reconnection opened up a previously closed flux tube at the Sun. This study highlights the types of structures that Parker Solar Probe and the upcoming Solar Orbiter mission will observe, and the types of data analyses these missions will enable. The data from these spacecrafts will provide additional in situ measurements of the solar wind properties in the inner heliosphere allowing, together with the information of the other interplanetary probes, a more comprehensive study of solar wind formation.
PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction

Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user"likes", we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models.
A Bayesian approach with Gaussian priors to the inverse problem of source identification in elliptic PDEs

We consider the statistical linear inverse problem of making inference on an unknown source function in an elliptic partial differential equation from noisy observations of its solution. We employ nonparametric Bayesian procedures based on Gaussian priors, leading to convenient conjugate formulae for posterior inference. We review recent results providing theoretical guarantees on the quality of the resulting posterior-based estimation and uncertainty quantification, and we discuss the application of the theory to the important classes of Gaussian series priors defined on the Dirichlet-Laplacian eigenbasis and Mat\'ern process priors. We provide an implementation of posterior inference for both classes of priors, and investigate its performance in a numerical simulation study.