Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Electromagnetic Environments: A General Framework

The recent introduction of the smart radio environments (SREs) paradigm, enabled by reconfigurable intelligent surfaces (RISs), has put in evidence the need for physically-consistent models and design tools for communication systems integrating electromagnetic (EM) and signal processing theories. In this perspective, starting from rigorous EM arguments, in this paper we propose a general framework for the characterization and design of programmable EM environments. We first show that any linear EM environment in the presence of boundary conditions can be interpreted as a space-variant linear feedback filter. Then we provide a methodology to characterize programmable EM systems as a linear graph described by matrix operators thus leading to the determination of the transfer function of the EM system. Finally, some examples are given related to the characterization and design of RISs, also showing that some previous results in the literature are just particular cases of our general framework.
Observation of the $\Upsilon$(3S) meson and suppression of $\Upsilon$ states in PbPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV

The production of $\Upsilon$(2S) and $\Upsilon$(3S) mesons in lead-lead (PbPb) and proton-proton (pp) collisions is studied in their dimuon decay channel using the CMS detector at the LHC. The $\Upsilon$(3S) meson is observed for the first time in PbPb collisions, with a significance above five standard deviations. The ratios of yields measured in PbPb and pp collisions are reported for both the $\Upsilon$(2S) and $\Upsilon$(3S) mesons, as functions of transverse momentum and PbPb collision centrality. These ratios, when appropriately scaled, are significantly less than unity, indicating a suppression of $\Upsilon$ yields in PbPb collisions. This suppression increases from peripheral to central PbPb collisions. Furthermore, the suppression is stronger for $\Upsilon$(3S) mesons compared to $\Upsilon$(2S) mesons, extending the pattern of sequential suppression of quarkonium states in nuclear collisions previously seen for the $\psi$/J, $\psi$(2S), $\Upsilon$(1S), and $\Upsilon$(2S) mesons.
DPP-Based Client Selection for Federated Learning with NON-IID DATA

This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL’s data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. Based on this, we lever-age data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP3S). This algorithm effectively diversifies the participants’ datasets in each round of training while preserving their data privacy. We conduct extensive experiments to examine the efficacy of our proposed method. The results show that our scheme attains a faster convergence rate, as well as a smaller communication overhead than several baselines.
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models

This paper deals with the problem of efficient sampling from a stochastic differential equation, given the drift function and the diffusion matrix. The proposed approach leverages a recent model for probabilities \cite{rudi2021psd} (the positive semi-definite -- PSD model) from which it is possible to obtain independent and identically distributed (i.i.d.) samples at precision $\varepsilon$ with a cost that is $m^2 d \log(1/\varepsilon)$ where $m$ is the dimension of the model, $d$ the dimension of the space. The proposed approach consists in: first, computing the PSD model that satisfies the Fokker-Planck equation (or its fractional variant) associated with the SDE, up to error $\varepsilon$, and then sampling from the resulting PSD model. Assuming some regularity of the Fokker-Planck solution (i.e. $\beta$-times differentiability plus some geometric condition on its zeros) We obtain an algorithm that: (a) in the preparatory phase obtains a PSD model with L2 distance $\varepsilon$ from the solution of the equation, with a model of dimension $m = \varepsilon^{-(d+1)/(\beta-2s)} (\log(1/\varepsilon))^{d+1}$ where $1/2\leq s\leq1$ is the fractional power to the Laplacian, and total computational complexity of $O(m^{3.5} \log(1/\varepsilon))$ and then (b) for Fokker-Planck equation, it is able to produce i.i. d. \ samples with error $\varepsilon$ in Wasserstein-1 distance, with a cost that is $O(d \varepsilon^{-2(d+1)/\beta-2} \log(1/\varepsilon)^{2d+3})$ per sample. This means that, if the probability associated with the SDE is somewhat regular, i.e. $\beta \geq 4d+2$, then the algorithm requires $O(\varepsilon^{-0.88} \log(1/\varepsilon)^{4.5d})$ in the preparatory phase, and $O(\varepsilon^{-1/2}\log(1/\varepsilon)^{2d+2})$ for each sample. Our results suggest that as the true solution gets smoother, we can circumvent the curse of dimensionality without requiring any sort of convexity.
On pitfalls (and advantages) of sophisticated large language models

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at \url{https://github.com/dptech-corp/Uni-Mol}.
JWST/NIRCam Discovery of the First Y+Y Brown Dwarf Binary: WISE J033605.05–014350.4

We report the discovery of the first brown dwarf binary system with a Y dwarf primary, WISE J033605.05−014350.4, observed with NIRCam on JWST with the F150W and F480M filters. We employed an empirical point-spread function binary model to identify the companion, located at a projected separation of 0. ″084, position angle of 295°, and with contrasts of 2.8 and 1.8 mag in F150W and F480M, respectively. At a distance of 10 pc based on its Spitzer parallax, and assuming a random inclination distribution, the physical separation is approximately 1 au. Evolutionary models predict for that an age of 1–5 Gyr, the companion mass is about 4–12.5 Jupiter masses around the 7.5–20 Jupiter mass primary, corresponding to a companion-to-host mass fraction of q = 0.61 ± 0.05. Under the assumption of a Keplerian orbit the period for this extreme binary is in the range of 5–9 yr. The system joins a small but growing sample of ultracool dwarf binaries with effective temperatures of a few hundreds of Kelvin. Brown dwarf binaries lie at the nexus of importance for understanding the formation mechanisms of these elusive objects, as they allow us to investigate whether the companions formed as stars or as planets in a disk around the primary.
Methods and advancement of content-based fashion image retrieval: A Review

Content-based fashion image retrieval (CBFIR) has been widely used in our daily life for searching fashion images or items from online platforms. In e-commerce purchasing, the CBFIR system can retrieve fashion items or products with the same or comparable features when a consumer uploads a reference image, image with text, sketch or visual stream from their daily life. This lowers the CBFIR system reliance on text and allows for a more accurate and direct searching of the desired fashion product. Considering recent developments, CBFIR still has limits when it comes to visual searching in the real world due to the simultaneous availability of multiple fashion items, occlusion of fashion products, and shape deformation. This paper focuses on CBFIR methods with the guidance of images, images with text, sketches, and videos. Accordingly, we categorized CBFIR methods into four main categories, i.e., image-guided CBFIR (with the addition of attributes and styles), image and text-guided, sketch-guided, and video-guided CBFIR methods. The baseline methodologies have been thoroughly analyzed, and the most recent developments in CBFIR over the past six years (2017 to 2022) have been thoroughly examined. Finally, key issues are highlighted for CBFIR with promising directions for future research.
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions

Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offline models can further improve performance. But how should we ideally finetune agents obtained from offline RL training? While offline RL algorithms can in principle be used for finetuning, in practice, their online performance improves slowly. In contrast, we show that it is possible to use standard online off-policy algorithms for faster improvement. However, we find this approach may suffer from policy collapse, where the policy undergoes severe performance deterioration during initial online learning. We investigate the issue of policy collapse and how it relates to data diversity, algorithm choices and online replay distribution. Based on these insights, we propose a conservative policy optimization procedure that can achieve stable and sample-efficient online learning from offline pretraining.
3D Line Mapping Revisited

In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. Despite their apparent advantages, current line-based reconstruction methods are far behind their point-based counterparts. In this paper we aim to close the gap by introducing LIMAP, a library for 3D line mapping that robustly and efficiently creates 3D line maps from multi-view imagery. This is achieved through revisiting the degeneracy problem of line triangulation, carefully crafted scoring and track building, and exploiting structural priors such as line coincidence, parallelism, and orthogonality. Our code integrates seamlessly with existing point-based Structure-from-Motion methods and can leverage their 3D points to further improve the line reconstruction. Furthermore, as a byproduct, the method is able to recover 3D association graphs between lines and points / vanishing points (VPs). In thorough experiments, we show that LIMAP significantly outperforms existing approaches for 3D line mapping. Our robust 3D line maps also open up new research directions. We show two example applications: visual localization and bundle adjustment, where integrating lines alongside points yields the best results. Code is available at https://github.com/cvg/limap.
Interface model for HfO2 gate stack from first principles calculations and its application to nanoscale device simulations

Rigorous first-principles calculations are performed to address the interface properties of HfO2 gate stacks with interlayer (IL). The band-gap and dielectric constant change nonabruptly at the HfO2/IL and IL/Si interfaces with transition regions of a few angstrom wide. Device-level simulations are then performed with the interface properties reflected, and electrostatics, gate-leakage and drain currents are found to be substantially influenced by the presence of the transition regions. Based on the findings, the equivalent single-layer model as a practical guide to the modeling and simulation of HfO2 gate stacks is proposed.
Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks - Extended version

This paper develops a control scheme, based on the use of Long Short-Term Memory neural network models and Nonlinear Model Predictive Control, which guarantees recursive feasibility with slow time variant set-points and disturbances, input and output constraints and unmeasurable state. Moreover, if the set-point and the disturbance are asymptotically constant, offset-free tracking is guaranteed. Offset-free tracking is obtained by augmenting the model with a disturbance, to be estimated together with the states of the Long Short-Term Memory network model by a properly designed observer. Satisfaction of the output constraints in presence of observer estimation error, time variant set-points and disturbances is obtained using a constraint tightening approach.
Masked Autoencoders as Image Processors

Transformers have shown significant effectiveness for various vision tasks including both high-level vision and low-level vision. Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks. However, the significance of MAE pre-training on low-level vision tasks has not been sufficiently explored. In this paper, we show that masked autoencoders are also scalable self-supervised learners for image processing tasks. We first present an efficient Transformer model considering both channel attention and shifted-window-based self-attention termed CSformer. Then we develop an effective MAE architecture for image processing (MAEIP) tasks. Extensive experimental results show that with the help of MAEIP pre-training, our proposed CSformer achieves state-of-the-art performance on various image processing tasks, including Gaussian denoising, real image denoising, single-image motion deblurring, defocus deblurring, and image deraining.
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision

There has been a recent explosion of computer vision models which perform many tasks and are composed of an image encoder (usually a ViT) and an autoregressive decoder (usually a Transformer). However, most of this work simply presents one system and its results, leaving many questions regarding design decisions and trade-offs of such systems unanswered. In this work, we aim to provide such answers. We take a close look at autoregressive decoders for multi-task learning in multimodal computer vision, including classification, captioning, visual question answering, and optical character recognition. Through extensive systematic experiments, we study the effects of task and data mixture, training and regularization hyperparameters, conditioning type and specificity, modality combination, and more. Importantly, we compare these to well-tuned single-task baselines to highlight the cost incurred by multi-tasking. A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well. We call this setup locked-image tuning with decoder (LiT-decoder). It can be seen as teaching a decoder to interact with a pretrained vision model via natural language.
Experimental determination of the electrical band-gap energy of porous silicon and the band offsets at the porous silicon/crystalline silicon heterojunction

Photovoltaic and photocurrent measurements have been carried out on a series of samples consisting of porous silicon on top of crystalline silicon. From the experimental data set, the electrical band-gap energy of porous silicon is deduced to be (1.80\ifmmode\pm\else\textpm\fi{}0.02) eV, and the conduction-band and valence-band offsets at the porous silicon/crystalline silicon heterojunction are determined to be (0.12\ifmmode\pm\else\textpm\fi{}0.01) eV and (0.57\ifmmode\pm\else\textpm\fi{}0.03) eV, respectively. The results are compared to previous determinations and discussed.
The Faraday effect in semimagnetic semiconductors

A review of experimental and theoretical research on the Faraday effect in a new class of materials–semimagnetic semiconductors (SMS). The authors discuss the mechanism responsible for the giant Faraday effect in SMS, which is based on s, p–d exchange interactions of excitons, electrons, and holes with magnetic ions. The authors also examine the dependence of Faraday rotation (FR) on wavelength, magnetic component concentration, temperature, and magnetic field intensity in A2B6Mn and A21–xMnxB6 crystals, as well as other SMS (GaAs Mn, CdP2Mn, Pb1–xMnxI2). They examine the use of FR in the study of the paramagnetic-spin glass transition, the role played by relaxation processes involving magnetic Mn2+ ions, excitons, and polarons in the direct and inverse Faraday effects, and the properties of FR in thin SMS films and spin superlattices. Finally, the authors analyze possible applications of the Faraday effect in SMS to practical magnetooptic devices (optical isolators, fiber optic magnetic field sensors).
Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs

Magnetic Resonance Imaging allows high resolution data acquisition with the downside of motion sensitivity due to relatively long acquisition times. Even during the acquisition of a single 2D slice, motion can severely corrupt the image. Retrospective motion correction strategies do not interfere during acquisition time but operate on the motion affected data. Known methods suited to this scenario are compressed sensing (CS), generative adversarial networks (GANs), and motion estimation. In this paper we propose a strategy to correct for motion artifacts using Deep Convolutional Neuronal Networks (Deep CNNs) in a reliable and verifiable manner by explicit motion estimation. The sensitivity encoding (SENSE) redundancy that multiple receiver coils provide, has in the past been used for acceleration, noise reduction and rigid motion compensation. We show that using Deep CNNs the concepts of rigid motion compensation can be generalized to more complex motion fields. Using a simulated synthetic data set, our proposed supervised network is evaluated on motion corrupted MRIs of abdomen and head. We compare our results with rigid motion compensation and GANs.
Large-scale structure in a low-bias universe

We show that a highly evolved, or low bias, Ω=1 cold dark matter model is compatible with current observational data. Nonlinear evolution is sufficient to account for recent observations of structure on scales ∼20-40 h −1 Mpc. Within dissipationless simulations we adopt a physically plausible algorithm to select galaxies which follows their merging. Galaxy tracers are selected at redshifts ∼3 and then assembled into galaxies at the current epoch. The resulting distribution is an acceptable fit to the data at a linear bias, b l , as low as 0.8 in a CDM model: the current microwave background limit
Sub-millimetre galaxies with Webb. Near-infrared counterparts and multi-wavelength morphology

We utilise the unprecedented depth and resolution of recent early-release science (ERS) JWST observations to define the near-infrared counterparts of sub-millimetre selected galaxies (SMGs). We identify 45 SCUBA-2 SMG positions within The Cosmic Evolution Early Release Science Survey (CEERS) JWST/NIRCam fields. Through an analysis of multi-wavelength $p$-values, NIRCam colours and predicted SCUBA-2 fluxes, we define 43 JWST/NIRCam counterparts to the SCUBA-2 SMGs, finding a 63 per cent agreement with those identified in prior $HST$ studies. Using EaZy-py we fit the available HST and JWST observations to quantify the photometric redshifts of the NIRCam-SMGs, establishing a broad range of redshift from $z$$\approx$0.2$-$5.4 with a median of $z$$\approx$2.29, in agreement with other studies of SMGs. We analyse their rest-frame optical and near-infrared morphological properties (e.g. effective radius (R$_{\rm e}$), S\'ersic index ($n$), CAS, Gini and M$_{20}$), finding, on average, late-type disc-like morphologies with large scatter into the intermediate and merger regions of the non-parametric parameter space. For the non-merging galaxies, we find a median rest-frame optical size and S\'ersic index (and $1\sigma$ scatter) of R$_{\rm e}$=3.10$\pm$1.67kpc and $n$=0.96$\pm$0.66. Whilst in the rest-frame near-infrared we establish more compact, higher S\'ersic index morphologies (R$_{\rm e}$=1.64$\pm$0.97, $n$=1.85$\pm$0.63). We further establish that both the rest-frame optical and near-infrared effective radii correlate negatively (at a 2$\sigma$ level) with redshift whilst the S\'ersic index remains constant with cosmic time. Our results are consistent with the picture of inside-out galaxy evolution, with more centrally concentrated older stellar populations, and more extended, younger star-forming regions whose stellar emission is heavily attenuated in the central regions.
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

We study a new paradigm for sequential decision making, called offline policy learning from observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard qualities: 1) only a subset of trajectories is labeled with rewards, 2) labeled trajectories may not contain actions, 3) labeled trajectories may not be of high quality, and 4) the data may not have full coverage. Such imperfection is common in real-world learning scenarios, and offline PLfO encompasses many existing offline learning setups, including offline imitation learning (IL), offline IL from observations (ILfO), and offline reinforcement learning (RL). In this work, we present a generic approach to offline PLfO, called $\textbf{M}$odality-agnostic $\textbf{A}$dversarial $\textbf{H}$ypothesis $\textbf{A}$daptation for $\textbf{L}$earning from $\textbf{O}$bservations (MAHALO). Built upon the pessimism concept in offline RL, MAHALO optimizes the policy using a performance lower bound that accounts for uncertainty due to the dataset's insufficient coverage. We implement this idea by adversarially training data-consistent critic and reward functions, which forces the learned policy to be robust to data deficiency. We show that MAHALO consistently outperforms or matches specialized algorithms across a variety of offline PLfO tasks in theory and experiments. Our code is available at https://github.com/AnqiLi/mahalo.