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Cryogenic calorimeters, also known as bolometers, are among the leading technologies for searching for rare events. The CUPID experiment is exploiting this technology to deploy a tonne-scale detector to search for neutrinoless double-beta decay of $^{100}$Mo. The CUPID collaboration proposed an innovative approach to assembling bolometers in a stacked configuration, held in position solely by gravity. This gravity-based assembly method is unprecedented in the field of bolometers and offers several advantages, including relaxed mechanical tolerances and simplified construction. To assess and optimize its performance, we constructed a medium-scale prototype hosting 28 Li$_2$MoO$_4$ crystals and 30 Ge light detectors, both operated as cryogenic calorimeters at the Laboratori Nazionali del Gran Sasso (Italy). Despite an unexpected excess of noise in the light detectors, the results of this test proved (i) a thermal stability better than $\pm$0.5 mK at 10 mK, (ii) a good energy resolution of Li$_2$MoO$_4$ bolometers, (6.6 $\pm$ 2.2) keV FWHM at 2615 keV, and (iii) a Li$_2$MoO$_4$ light yield measured by the closest light detector of 0.36 keV/MeV, sufficient to guarantee the particle identification requested by CUPID.

Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth System Models (ESMs) face similar challenges, operating at high spatial and temporal resolutions, producing petabytes of data per simulated day. Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates due to their abundance of unlabeled data. In this review, we outline recent developments in NC applied to geospatial data. We introduce the fundamental concepts of NC including seminal works in its traditional applications to image and video compression domains with focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, we review current applications of NC across various EO modalities and explore the limited efforts in ESM compression to date. The advent of self-supervised learning (SSL) and foundation models (FM) has advanced methods to efficiently distill representations from vast unlabeled data. We connect these developments to NC for EO, highlighting the similarities between the two fields and elaborate on the potential of transferring compressed feature representations for machine--to--machine communication. Based on insights drawn from this review, we devise future directions relevant to applications in EO and ESM., Comment: self-consistent review paper

CUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay ($0\nu\beta\beta$) and other rare events using enriched Li$_2$$^{100}$MoO$_4$ scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for $0\nu\beta\beta$\ of $^{100}$Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10$^{-4}$ cnts/(keV$\cdot$kg$\cdot$yr), 240 kg isotope mass, 5 keV FWHM energy resolution and 10 live-years of data taking, CUPID will have a 90\% C.L. half-life exclusion sensitivity of 1.8 $\cdot$ 10$^{27}$ yr, corresponding to an effective Majorana neutrino mass ($m_{\beta\beta}$) sensitivity of 9--15 meV, and a $3\sigma$ discovery sensitivity of 1 $\cdot$ 10$^{27}$ yr, corresponding to an $m_{\beta\beta}$ range of 12--21 meV., Comment: Prepared for submission to EPJC; 24 pages, 6 figures, and 2 tables

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/., Comment: 13 pages, 5 figures, 3 tables, Accepted in MICCAI 2024

Tomato anomalies/damages pose a significant challenge in greenhouse farming. While this method of cultivation benefits from efficient resource utilization, anomalies can significantly degrade the quality of farm produce. A common anomaly associated with tomatoes is splitting, characterized by the development of cracks on the tomato skin, which degrades its quality. Detecting this type of anomaly is challenging due to dynamic variations in appearance and sizes, compounded by dataset scarcity. We address this problem in an unsupervised manner by utilizing a tailored variational autoencoder (VAE) with hyperspectral input. Preliminary analysis of the dataset enabled us to select the optimal range of wavelengths for detecting this anomaly. Our findings indicate that the 530nm - 550nm range is suitable for identifying tomato dry splits. The analysis on reconstruction loss allow us to not only detect the anomalies but also to some degree estimate the anomalous regions., Comment: CVPPA Workshop

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

This paper studies the use of a multi-attribute auction in a communication system to bring about efficient relaying in a non-cooperative setting. We consider a system where a source seeks to offload data to an access point (AP) while balancing both the timeliness and energy-efficiency of the transmission. A deep fade in the communication channel (due to, e.g., a line-of-sight blockage) makes direct communication costly, and the source may alternatively rely on non-cooperative UEs to act as relays. We propose a multi-attribute auction to select a UE and to determine the duration and power of the transmission, with payments to the UE taking the form of energy sent via wireless power transfer (WPT). The quality of the channel from a UE to the AP constitutes private information, and bids consist of a transmission time and transmission power. We show that under a second-preferred-offer auction, truthful bidding by all candidate UEs forms a Nash Equilibrium. However, this auction is not incentive compatible, and we present a modified auction in which truthful bidding is in fact a dominant strategy. Extensive numerical experimentation illustrates the efficacy of our approach, which we compare to a cooperative baseline. We demonstrate that with as few as two candidates, our improved mechanism leads to as much as a 76% reduction in energy consumption, and that with as few as three candidates, the transmission time decreases by as much as 55%. Further, we see that as the number of candidates increases, the performance of our mechanism approaches that of the cooperative baseline. Overall, our findings highlight the potential of multi-attribute auctions to enhance the efficiency of data transfer in non-cooperative settings.

Sensing with RF signals such as mmWave radar has gained considerable interest in recent years. This is particularly relevant to 6G networks, which aim to integrate sensing and communication (ISAC) capabilities for enhanced functionality. The contextual information provided by such sensing, whether collected by standalone non-ISAC units or integrated within ISAC, can not only optimize cellular network assets but can also serve as a valuable tool for a wide range of applications beyond network optimization. In this context, we present a novel methodology for crowd size estimation using monostatic mmWave radar, which is capable of accurately counting large crowds that are unevenly distributed across space. Our estimation approach relies on the rigorous derivation of occlusion probabilities, which are then used to mathematically characterize the probability distributions that describe the number of agents visible to the radar as a function of the crowd size. We then estimate the true crowd size by comparing these derived mathematical models to the empirical distribution of the number of visible agents detected by the radar. This method requires minimal sensing capabilities (e.g., angle-of-arrival information is not needed), thus being well suited for either a dedicated mmWave radar or an ISAC system. Extensive numerical simulations validate our methodology, demonstrating strong performance across diverse spatial distributions and for crowd sizes of up to (and including) 30 agents. We achieve a mean absolute error (MAE) of 0.48 agents, significantly outperforming a baseline which assumes that the agents are uniformly distributed in the area. Overall, our approach holds significant promise for a variety of applications including network resource allocation, crowd management, and urban planning.

This paper studies the use of a multi-attribute auction in a communication system to bring about efficient relaying in a non-cooperative setting. We consider a system where a source seeks to offload data to an access point (AP) while balancing both the timeliness and energy-efficiency of the transmission. A deep fade in the communication channel (due to, e.g., a line-of-sight blockage) makes direct communication costly, and the source may alternatively rely on non-cooperative UEs to act as relays. We propose a multi-attribute auction to select a UE and to determine the duration and power of the transmission, with payments to the UE taking the form of energy sent via wireless power transfer (WPT). The quality of the channel from a UE to the AP constitutes private information, and bids consist of a transmission time and transmission power. We show that under a second-preferred-offer auction, truthful bidding by all candidate UEs forms a Nash Equilibrium. However, this auction is not incentive compatible, and we present a modified auction in which truthful bidding is in fact a dominant strategy. Extensive numerical experimentation illustrates the efficacy of our approach, which we compare to a cooperative baseline. We demonstrate that with as few as two candidates, our improved mechanism leads to as much as a 76% reduction in energy consumption, and that with as few as three candidates, the transmission time decreases by as much as 55\%. Further, we see that as the number of candidates increases, the performance of our mechanism approaches that of the cooperative baseline. Overall, our findings highlight the potential of multi-attribute auctions to enhance the efficiency of data transfer in non-cooperative settings.

The spatial arrangement of components within an mRNA encapsulating nanoparticle has consequences for its thermal stability, which is a key parameter for therapeutic utility. The mesostructure of mRNA nanoparticles formed with cationic polymers have several distinct putative structures: here, we develop a field theoretic simulation model to compute the phase diagram for amphiphilic block copolymers that balance coacervation and hydrophobicity as driving forces for assembly. We predict several distinct morphologies for the mesostructure of these nanoparticles, depending on salt conditions and hydrophobicity. We compare our predictions with cryogenic-electron microscopy images of mRNA encapsulated by charge altering releasable transporters. In addition, we provide a GPU-accelerated, open-source codebase for general purpose field theoretic simulations, which we anticipate will be a useful tool for the community.

Tin oxide aerogels were synthesized using an epoxide-assisted technique and characterized with Fourier transform infrared, X-ray diffraction, and UV-Vis to study the effects of post-synthesis annealing and peroxide treatment. While bulk tin oxide exhibits an optical bandgap of $3.6$ eV, its aerogel form often displays a larger apparent bandgap around $4.6$ eV due to defects. Our study reveals that annealing induces a partial phase change from SnO$_2$ to SnO, but is ineffective in removing defects. Conversely, peroxide passivation effectively lowers the bandgap and disorder levels, suggesting that dangling bonds are the primary cause of the increased bandgap in tin oxide aerogels. These findings offer insights for optimizing the optical properties of tin oxide aerogels for applications like solar cells.

Weak measurement enables the extraction of targeted information from a quantum system while minimizing decoherence due to measurement backaction. However, in many-body quantum systems backaction can have unexpected effects on wavefunction collapse. We theoretically study a minimal many-particle model consisting of weakly measured non-interacting fermions in a one dimensional lattice. Repeated measurement of on-site occupation number with single-site resolution stochastically drives the system toward a Fock state, regardless of the initial state. This need not be the case for measurements that do not, even in principle, have single-site spatial resolution. We numerically show for systems with up to 16 sites that decreasing the spatial resolution strongly affects both the rate of stochastic evolution for each quantum trajectory and the allowed final states. The full Hilbert space can be partitioned into backaction-free subspaces (BFSs) the elements of which are indistinguishable to these measurements. Repeated measurements will drive any initial state into a single BFS, leading to a steady state that is a fixed point of the measurement process. We exactly calculate the properties of these BFSs for systems up to 32 sites and find that even for moderate reductions in measurement resolution they yield non-trivial steady state entanglement and coherence., Comment: Published version

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage., Comment: 24 pages, 8 figures

In this paper, a synergistic computational/experimental approach is presented for the rapid discovery and characterization of novel alloys within the compositionally complex (i.e., "medium/high entropy") refractory alloy space of Ti-V-Nb-Mo-Hf-Ta-W. This was demonstrated via a material design cycle aimed at simultaneously maximizing the objective properties of high specific hardness (hardness normalized by density) and high specific elastic modulus (elastic modulus normalized by density). This framework utilizes high-throughput computational thermodynamics and intelligent filtering to first reduce the untenably large alloy space to a feasible size, followed by an iterative design cycle comprised of high-throughput synthesis, processing, and characterization in batch sizes of 24 alloys. After the first iteration, Bayesian optimization was utilized to inform selection of the next batch of 24 alloys. This paper demonstrates the benefit of using batch Bayesian optimization (BBO) in material design, as significant gains in the objective properties were observed after only two iterations or "shots" of the design cycle without using any prior knowledge or physical models of how the objective properties relate to the design inputs (i.e., composition). Specifically, the hypervolume of the Pareto front increased by 54% between the first and second iterations. Furthermore, 10 of the 24 alloys in the second iteration dominated all alloys from the first iteration.

Uncrewed Vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the 6th Generation of wireless networks. Their seamless integration, cost-effectiveness, and the additional controllability through motion planning make them an attractive deployment option for a wide range of applications, both as assets in the network (e.g., mobile base stations) and as consumers of network services (e.g., autonomous delivery systems). However, despite their potential, the convergence of UVs and wireless systems brings forth numerous challenges that require attention from both academia and industry. This paper then aims to offer a comprehensive overview encompassing the transformative possibilities as well as the significant challenges associated with UV-assisted next-generation wireless communications. Considering the diverse landscape of possible application scenarios, problem formulations, and mathematical tools related to UV-assisted wireless systems, the underlying core theme of this paper is the unification of the problem space, providing a structured framework to understand the use cases, problem formulations, and necessary mathematical tools. Overall, the paper sets forth a clear understanding of how uncrewed vehicles can be integrated in the 6G ecosystem, paving the way towards harnessing the full potential at this intersection.

This paper addresses the challenge of incentivizing energy-constrained, non-cooperative user equipment (UE) to serve as cooperative relays. We consider a source UE with a non-line-of-sight channel to an access point (AP), where direct communication may be infeasible or may necessitate a substantial transmit power. Other UEs in the vicinity are viewed as relay candidates, and our aim is to enable energy-efficient connectivity for the source, while accounting for the self-interested behavior and private channel state information of these candidates, by allowing the source to ``pay" the candidates via wireless power transfer (WPT). We propose a cooperation-inducing protocol, inspired by Myerson auction theory, which ensures that candidates truthfully report power requirements while minimizing the expected power used by the source. Through rigorous analysis, we establish the regularity of valuations for lognormal fading channels, which allows for the efficient determination of the optimal source transmit power. Extensive simulation experiments, employing real-world communication and WPT parameters, validate our theoretical framework. Our results demonstrate over 71% reduction in outage probability with as few as 4 relay candidates, compared to the non-cooperative scenario, and as much as 70% source power savings compared to a baseline approach, highlighting the efficacy of our proposed methodology.

Topology is a powerful tool for categorizing magnetization textures, highlighting specific features in both 2D systems, such as thin films or curved surfaces, and in 3D bulk systems. In the emerging field of 3D nanomagnetism within confined geometries, the contributions from both volume and surface must be considered, requiring appropriate topological analysis to obtain a complete view of the system. Here, we consider domain walls in cylindrical magnetic nanowires to illustrate the use of topological invariants. We begin with micromagnetic simulations of domain wall transformation under the stimulus of an \OErsted field, tracking bulk and surface topological signatures, and analyzing the interplay between multiple micromagnetic objects. For instance, the extensive analysis allowed us to highlight mechanisms of domain wall type conversion from topologically non-trivial to trivial states, a phenomenon disregarded in previous studies. Additionally, we provide experimental evidence of the transient states predicted to occur during the dynamical process.