Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Building Point Cloud Cleaning, Plane Detection and Semantic Segmentation

This paper presents a framework to address the challenges involved in building point cloud cleaning, plane detection, and semantic segmentation, with the ultimate goal of enhancing building modeling. We focus in the cleaning stage on removing outliers from the acquired point cloud data by employing an adaptive threshold technique based on z-score measure. Following the cleaning process, we perform plane detection using the robust RANSAC paradigm. The goal is to carry out multiple plane segmentations, and to classify segments into distinct categories, such as floors, ceilings, and walls. The resulting segments can generate accurate and detailed point clouds representing the building's architectural elements. Moreover, we address the problem of semantic segmentation, which plays a vital role in the identification and classification of different components within the building, such as walls, windows, doors, roofs, and objects. Inspired by the PointNet architecture, we propose a deep learning architecture for efficient semantic segmentation in buildings. The results demonstrate the effectiveness of the proposed framework in handling building modeling tasks, paving the way for improved accuracy and efficiency in the field of building modelization.
Witnessing non-Markovianity with Gaussian quantum steering in collision model

The nonincreasing feature of temporal quantum steering under a completely positive trace-preserving (CPTP) map, as proposed by Chen, et al. in Phys. Rev. Lett. 116, 020503 (2016), has been considered as a practical measure of non-Markovianity. In this paper, we utilize an all-optical scheme to simulate a non-Markovian collision model and to examine how Gaussian steering can be used as a tool for quantifying the non-Markovianity of a structured continuous variable (CV) Gaussian channel. By modifying the reflectivity of the beam splitters (BSs), we are able to tune the degree of non-Markovianity of the channel. After analyzing the non-Markovian degree of the dissipative channel within two steering scenarios, we discovered that the Gaussian steering-based non-Markovian measure depends the specific scenario because of the asymmetry of Gaussian steering. We also compared the Gaussian steering based non-Markovianity to the one based on the violation of the divisibility of CPTP map.
Preconditioning for Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications. In this paper, we propose to use condition number as a metric to diagnose and mitigate the pathologies in PINNs. Inspired by classical numerical analysis, where the condition number measures sensitivity and stability, we highlight its pivotal role in the training dynamics of PINNs. We prove theorems to reveal how condition number is related to both the error control and convergence of PINNs. Subsequently, we present an algorithm that leverages preconditioning to improve the condition number. Evaluations of 18 PDE problems showcase the superior performance of our method. Significantly, in 7 of these problems, our method reduces errors by an order of magnitude. These empirical findings verify the critical role of the condition number in PINNs' training.
Randomly Monitored Quantum Codes

Quantum measurement has conventionally been regarded as the final step in quantum information processing, which is essential for reading out the processed information but collapses the quantum state into a classical state. However, recent studies have shown that quantum measurement itself can induce novel quantum phenomena. One seminal example is a monitored random circuit, which can generate long-range entanglement faster than a random unitary circuit. Inspired by these results, in this paper, we address the following question: When quantum information is encoded in a quantum error-correcting code, how many physical qubits should be randomly measured to destroy the encoded information? We investigate this question for various quantum error-correcting codes and derive the necessary and sufficient conditions for destroying the information through measurements. In particular, we demonstrate that for a large class of quantum error-correcitng codes, it is impossible to destroy the encoded information through random single-qubit Pauli measurements when a tiny portion of physical qubits is still unmeasured. Our results not only reveal the extraordinary robustness of quantum codes under measurement decoherence, but also suggest potential applications in quantum information processing tasks.
Reanalysis of the MACHO constraints on PBH in the light of Gaia DR3 data

The recent astrometric data of hundreds of millions of stars from Gaia DR3 has allowed a precise determination of the Milky Way rotation curve up to $28$~kpc. The data suggests a rapid decline in the density of dark matter beyond $19$~kpc. We fit the whole rotation curve with four components (gas, disk, bulge and halo) and compute the microlensing optical depth to the Large Magellanic Cloud. With this model of the galaxy we reanalyse the microlensing events of the MACHO and EROS-2 Collaborations. Using the published MACHO efficiency function for the duration of their survey, together with the rate of expected events according to the new density profile, we find that the Dark Matter halo could be composed up to $100\%$ of massive compact halo objects for any mass between $10^{-4}$ to $100~M_\odot$, except a broad range around $0.01~M_\odot$, where it cannot be larger than $\sim20~\%$. For the EROS-2 survey, using a modified efficiency curve for consistency with the MACHO analysis, we also find compatibility with a 100\% MACHO halo, but with a tighter constraint around $0.001~M_\odot$ where the halo fraction cannot be larger than $\sim12~\%$. This result assumes that MACHOs all have the same mass. If these were distributed in an extended mass function like that of the Thermal History Model, the constraints are weakened, allowing $100\%$ of all DM in the form of Primordial Black Holes.
Explaining the $\gamma\gamma+X$ Excesses at $\approx$151.5 GeV via the Drell-Yan Production of a Higgs Triplet

The multi-lepton anomalies and searches for the associated production of a narrow resonance indicate the existence of a $\approx$151 GeV Higgs with a significance of $>5\sigma$ and $>3.9\sigma$, respectively. On the one hand, these anomalies require a sizable branching fraction of the new scalar to $WW$, while on the other hand, no $ZZ$ signal at this mass has been observed. This suggests that the new boson is the neutral component of an $SU(2)_L$ triplet with zero hypercharge. This field leads to a positive definite shift in the $W$ mass, as preferred by the current global fit, and is produced via the Drell-Yan process $pp\to W^*\to \Delta^0\Delta^\pm$. We use the side-bands of the ATLAS analysis \cite{ATLAS:2023omk} of the associated production of the Standard Model Higgs in the di-photon channel to search for this production mode of the triplet. Since the dominant decays of $\Delta^\pm$ depend only on its mass, the effect in the 22 signal categories considered by ATLAS is completely correlated. We find that the ones most sensitive to the Drell-Yan production of the triplet Higgs show consistent excesses at a mass of $\approx$151.5 GeV. Combining these channels in a likelihood ratio test, a non-zero Br$[\Delta^0\to\gamma\gamma] = 0.66\%$ is preferred by $\approx$3$\sigma$, supporting our conjecture.
Strain induced phase transition from antiferromagnet to altermagnet

The newly discovered altermagnets are unconventional collinear compensated magnetic systems, exhibiting even (d, g, or i-wave) spin-polarization order in the band structure, setting them apart from conventional collinear ferromagnets and antiferromagnets. Altermagnets offer advantages of spin polarized current akin to ferromagnets, and THz functionalities similar to antifferomagnets, while introducing new novel effects like spin-splitter currents. A key challenge for future applications and functionalization of altermagnets, is to demonstrate controlled transitioning to the altermagnetic phase from other conventional phases in a single material. Here we prove a viable path towards overcoming this challenge through a strain-induced transition from an antiferromagnetic to an altermagnetic phase in ReO$_2$. Combining spin group symmetry analysis and \textit{ab-initio} calculations, we demonstrate that under compressive strain ReO$_2$ undergoes such transition, lifting the Kramer's degeneracy of the band structure of the antiferromagnetic phase in the non-relativistic regime. In addition, we show that this magnetic transition is accompanied by a metal insulator transition, and calculate the distinct spin polarized spectral functions of the two phases, which can be detected in angle resolved photo-emission spectroscopy experiments.
TrackGPT - A generative pre-trained transformer for cross-domain entity trajectory forecasting

The forecasting of entity trajectories at future points in time is a critical capability gap in applications across both Commercial and Defense sectors. Transformers, and specifically Generative Pre-trained Transformer (GPT) networks have recently revolutionized several fields of Artificial Intelligence, most notably Natural Language Processing (NLP) with the advent of Large Language Models (LLM) like OpenAI's ChatGPT. In this research paper, we introduce TrackGPT, a GPT-based model for entity trajectory forecasting that has shown utility across both maritime and air domains, and we expect to perform well in others. TrackGPT stands as a pioneering GPT model capable of producing accurate predictions across diverse entity time series datasets, demonstrating proficiency in generating both long-term forecasts with sustained accuracy and short-term forecasts with high precision. We present benchmarks against state-of-the-art deep learning techniques, showing that TrackGPT's forecasting capability excels in terms of accuracy, reliability, and modularity. Importantly, TrackGPT achieves these results while remaining domain-agnostic and requiring minimal data features (only location and time) compared to models achieving similar performance. In conclusion, our findings underscore the immense potential of applying GPT architectures to the task of entity trajectory forecasting, exemplified by the innovative TrackGPT model.
Subgradient evolution of value functions in discrete-time optimal control

In this paper we investigate how the subgradients of the value function of a discrete-time convex Bolza problem evolve over time. In particular, we develop a discrete-time version of the characteristic method introduced by Rockafellar and Wolenski in the 2000s, by showing that the time-evolution of the subgradients of the value functions can be associated with trajectories of a discrete-time Hamiltonian system. To do so, we first prove that the value function has a dual counterpart, which corresponds to the conjugate of the value function of a suitable dual problem. We finally make a discussion about the qualification conditions we require for our results, showing in particular that classical problems, such as the Liner-Quadratic regulator, satisfy these hypotheses.
Can Large Language Models Understand Context?

Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited attention has been paid to probing their linguistic capability of understanding contextual features. This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. This benchmark comprises of four distinct tasks and nine datasets, all featuring prompts designed to assess the models’ ability to understand context. First, we evaluate the performance of LLMs under the in-context learning pretraining scenario. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. Second, as LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings. We find that 3-bit post-training quantization leads to varying degrees of performance reduction on our benchmark. We conduct an extensive analysis of these scenarios to substantiate our experimental results.
On the $O(\frac{\sqrt{d}}{T^{1/4}})$ Convergence Rate of RMSProp and Its Momentum Extension Measured by $\ell_1$ Norm: Better Dependence on the Dimension

Although adaptive gradient methods have been extensively used in deep learning, their convergence rates have not been thoroughly studied, particularly with respect to their dependence on the dimension. This paper considers the classical RMSProp and its momentum extension and establishes the convergence rate of $\frac{1}{T}\sum_{k=1}^TE\left[\|\nabla f(x^k)\|_1\right]\leq O(\frac{\sqrt{d}}{T^{1/4}})$ measured by $\ell_1$ norm without the bounded gradient assumption, where $d$ is the dimension of the optimization variable and $T$ is the iteration number. Since $\|x\|_2\ll\|x\|_1\leq\sqrt{d}\|x\|_2$ for problems with extremely large $d$, our convergence rate can be considered to be analogous to the $\frac{1}{T}\sum_{k=1}^TE\left[\|\nabla f(x^k)\|_2\right]\leq O(\frac{1}{T^{1/4}})$ one of SGD measured by $\ell_1$ norm.
High-Quality Medical Image Generation from Free-hand Sketch

Generating medical images from human-drawn free-hand sketches holds promise for various important medical imaging applications. Due to the extreme difficulty in collecting free-hand sketch data in the medical domain, most deep learning-based methods have been proposed to generate medical images from the synthesized sketches (e.g., edge maps or contours of segmentation masks from real images). However, these models often fail to generalize on the free-hand sketches, leading to unsatisfactory results. In this paper, we propose a practical free-hand sketch-to-image generation model called Sketch2MedI that learns to represent sketches in StyleGAN's latent space and generate medical images from it. Thanks to the ability to encode sketches into this meaningful representation space, Sketch2MedI only requires synthesized sketches for training, enabling a cost-effective learning process. Our Sketch2MedI demonstrates a robust generalization to free-hand sketches, resulting in high-quality and realistic medical image generations. Comparative evaluations of Sketch2MedI against the pix2pix, CycleGAN, UNIT, and U-GAT-IT models show superior performance in generating pharyngeal images, both quantitative and qualitative across various metrics.
Designing for Sustained Motivation: A Review of Self-Determination Theory in Behaviour Change Technologies

Recent years have seen a surge in applications and technologies aimed at motivating users to achieve personal goals and improve their wellbeing. However, these often fail to promote long-term behaviour change, and sometimes even backfire. We consider how self-determination theory (SDT), a metatheory of human motivation and wellbeing, can help explain why such technologies fail, and how they may better help users internalise the motivation behind their goals and make enduring changes in their behaviour. In this work, we systematically reviewed 15 papers in the ACM Digital Library that apply SDT to the design of behaviour change technologies (BCTs). We identified 50 suggestions for design features in BCTs, grounded in SDT, that researchers have applied to enhance user motivation. However, we find that SDT is often leveraged to optimise engagement with the technology itself rather than with the targeted behaviour change per se. When interpreted through the lens of SDT, the implication is that BCTs may fail to cultivate sustained changes in behaviour, as users' motivation depends on their enjoyment of the intervention, which may wane over time. An underexplored opportunity remains for designers to leverage SDT to support users to internalise the ultimate goals and value of certain behaviour changes, enhancing their motivation to sustain these changes in the long term.
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning

Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.
JWST-MIRI Spectroscopy of Warm Molecular Emission and Variability in the AS 209 Disk

We present MIRI Medium-resolution Spectrograph observations of the large, multi-gapped protoplanetary disk around the T Tauri star AS 209. The observations reveal hundreds of water vapor lines from 4.9–25.5 μm toward the inner ∼1 au in the disk, including the first detection of rovibrational water emission in this disk. The spectrum is dominated by hot (∼800 K) water vapor and OH gas, with only marginal detections of CO2, HCN, and a possible colder water vapor component. Using slab models with a detailed treatment of opacities and line overlap, we retrieve the column density, emitting area, and excitation temperature of water vapor and OH, and provide upper limits for the observable mass of other molecules. Compared to MIRI spectra of other T Tauri disks, the inner disk of AS 209 does not appear to be atypically depleted in CO2 nor HCN. Based on Spitzer Infrared Spectrograph observations, we further find evidence for molecular emission variability over a 10 yr baseline. Water, OH, and CO2 line luminosities have decreased by factors of 2–4 in the new MIRI epoch, yet there are minimal continuum emission variations. The origin of this variability is yet to be understood.
Online Distribution Learning with Local Private Constraints

We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy. Let $\mathcal{F}$ be a distribution-valued function class with unbounded label set. We aim at estimating an \emph{unknown} function $f\in \mathcal{F}$ in an online fashion so that at time $t$ when the context $\boldsymbol{x}_t$ is provided we can generate an estimate of $f(\boldsymbol{x}_t)$ under KL-divergence knowing only a privatized version of the true labels sampling from $f(\boldsymbol{x}_t)$. The ultimate objective is to minimize the cumulative KL-risk of a finite horizon $T$. We show that under $(\epsilon,0)$-local differential privacy of the privatized labels, the KL-risk grows as $\tilde{\Theta}(\frac{1}{\epsilon}\sqrt{KT})$ upto poly-logarithmic factors where $K=|\mathcal{F}|$. This is in stark contrast to the $\tilde{\Theta}(\sqrt{T\log K})$ bound demonstrated by Wu et al. (2023a) for bounded label sets. As a byproduct, our results recover a nearly tight upper bound for the hypothesis selection problem of gopi et al. (2020) established only for the batch setting.
Robust sufficient dimension reduction via
 α
 -distance covariance

We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using $\alpha$-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on the predictors, the central subspace is effectively estimated and model-free advantage without estimating link function based on the projection on the Stiefel manifold. We establish the convergence property of the proposed estimation under some regularity conditions. We compare the performance of our method with existing SDR methods by simulation and real data analysis and show that our algorithm improves the computational efficiency and effectiveness.
A link between anomalous viscous loss and boson peak in soft jammed solids

Soft jammed solids exhibit intriguing mechanical properties, while their linear response is elusive. In particular, foams and emulsions generally reveal anomalous viscous loss with the loss and storage modulus following $G^{\prime \prime} \propto \sqrt{\omega}$ and $G^{\prime} \propto \omega^0$. In this study, we offer a comprehensive microscopic understanding of this behavior. Using microrheology experiment, we measured $G^* = G^{\prime} + i G^{\prime \prime}$ of concentrated emulsions in a wide range of frequencies. In theory, we applied a linear response formalism for microrheology to a soft sphere model that undergoes the jamming transition. We find that the theory quantitatively explains the experiments without the need for parameter adjustments. Our analysis reveals that the anomalous viscous loss results from the boson peak, which is a universal vibrational property of amorphous solids and reflects the marginal stability in soft jammed solids. We discuss that the anomalous viscous loss is universal in systems with various interparticle interactions as it stems from the universal boson peak, and it even survives below the jamming density where thermal fluctuation is pronounced and the dynamics becomes inherently nonlinear.
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues

While state-of-the-art facial expression recognition (FER) classifiers achieve a high level of accuracy, they lack interpretability, an important aspect for end-users. To recognize basic facial expressions, experts resort to a codebook associating a set of spatial action units to a facial expression. In this paper, we follow the same expert footsteps, and propose a learning strategy that allows us to explicitly incorporate spatial action units (aus) cues into the classifier's training to build a deep interpretable model. In particular, using this aus codebook, input image expression label, and facial landmarks, a single action units heatmap is built to indicate the most discriminative regions of interest in the image w.r. t the facial expression. We leverage this valuable spatial cue to train a deep interpretable classifier for FER. This is achieved by constraining the spatial layer features of a classifier to be correlated with \aus map. Using a composite loss, the classifier is trained to correctly classify an image while yielding interpretable visual layer-wise attention correlated with aus maps, simulating the experts' decision process. This is achieved using only the image class expression as supervision and without any extra manual annotations. Moreover, our method is generic. It can be applied to any CNN- or transformer-based deep classifier without the need for architectural change or adding significant training time. Our extensive evaluation on two public benchmarks RAFDB, and AFFECTNET datasets shows that our proposed strategy can improve layer-wise interpretability without degrading classification performance. In addition, we explore a common type of interpretable classifiers that rely on Class-Activation Mapping methods (CAMs), and we show that our training technique improves the CAM interpretability.