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Global allocations in the upper mid-band spectrum (4-24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40-880 m propagation distance, encompassing 6 Line of Sight (LOS) and 14 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in PL model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The NLOS RMS DS and RMS AS mean values are obtained consistently lower compared to 3GPP model predictions. Point data for all measured statistics at each TX-RX location are provided to supplement the models and results. The spatio-temporal statistics evaluated here offer valuable insights for the design of next-generation wireless systems and networks., Comment: 6 pages, 4 figures, 6 tables

With the advent of aerial image datasets, dense stereo matching has gained tremendous progress. This work analyses dense stereo correspondence analysis on aerial images using different techniques. Traditional methods, optimization based methods and learning based methods have been implemented and compared here for aerial images. For traditional methods, we implemented the architecture of Stereo SGBM while using different cost functions to get an understanding of their performance on aerial datasets. Analysis of most of the methods in standard datasets has shown good performance, however in case of aerial dataset, not much benchmarking is available. Visual qualitative and quantitative analysis has been carried out for two stereo aerial datasets in order to compare different cost functions and techniques for the purpose of depth estimation from stereo images. Using existing pre-trained models, recent learning based architectures have also been tested on stereo pairs along with different cost functions in SGBM. The outputs and given ground truth are compared using MSE, SSIM and other error metrics.

Site-specific wireless channel simulations via ray tracers can be used to effectively study wireless, decreasing the need for extensive site-specific radio propagation measurements. To ensure that ray tracer simulations faithfully reproduce wireless channels, calibration of simulation results against real-world measurements is required. In this study we introduce NYURay, a 3D ray tracer specifically tailored for mmWave and sub-THz frequencies. To reliably generate site-specific wireless channel parameters, NYURay is calibrated using radio propagation measurements conducted at 28, 73, and 142 GHz in diverse scenarios such as outdoor areas, indoor offices, and factories. Traditional ray tracing calibration assumes angle-dependent reflection, requiring slow iterative optimization techniques with no closed form solution. We propose a simpler and quicker novel calibration method that assumes angle-independent reflection. The effectiveness of the proposed calibration approach is demonstrated using NYURay. When comparing the directional multipath power predicted by NYURay to the actual measured power, the standard deviation in error was less than 3 dB in indoor office environments and less than 2 dB in outdoor and factory environments. The root mean square (RMS) delay spread and angular spread was underpredicted by NYURay due to incomplete environmental maps available for calibration, however an overall agreement between the measured and simulated values was observed. These results highlight the high level of accuracy NYURay provides in generating the site-specific real-world wireless channel, that could be used to generate synthetic data for machine learning., Comment: Accepted and to appear in IEEE Transactions on Antennas and Propagation

With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-based vulnerability detection systems, particularly those leveraging pre-trained language models, have demonstrated significant potential in promptly identifying vulnerabilities in communication networks and reducing the risk of exploitation. However, the shortage of accurately labeled vulnerability datasets hinders further progress in this field. Failing to represent real-world vulnerability data variety and preserve vulnerability semantics, existing augmentation approaches provide limited or even counterproductive contributions to model training. In this paper, we propose a data augmentation technique aimed at enhancing the performance of pre-trained language models for vulnerability detection. Given the vulnerability dataset, our method performs natural semantic-preserving program transformation to generate a large volume of new samples with enriched data diversity and variety. By incorporating our augmented dataset in fine-tuning a series of representative code pre-trained models (i.e., CodeBERT, GraphCodeBERT, UnixCoder, and PDBERT), up to 10.1% increase in accuracy and 23.6% increase in F1 can be achieved in the vulnerability detection task. Comparison results also show that our proposed method can substantially outperform other prominent vulnerability augmentation approaches., Comment: Accepted by EAI International Conference on Security and Privacy in Communication Networks (SecureComm 2024)

In this work, we explore Self-supervised Learning (SSL) as an auxiliary task to blend the texture-based local descriptors into feature modelling for efficient face analysis. Combining a primary task and a self-supervised auxiliary task is beneficial for robust representation. Therefore, we used the SSL task of mask auto-encoder (MAE) as an auxiliary task to reconstruct texture features such as local patterns along with the primary task for robust and unbiased face analysis. We experimented with our hypothesis on three major paradigms of face analysis: face attribute and face-based emotion analysis, and deepfake detection. Our experiment results exhibit that better feature representation can be gleaned from our proposed model for fair and bias-less face analysis.

We prove that a compact Riemannian manifold $M$ does not admit any non-trivial $m$-modified homothetic vector fields. In the corresponding case of an $m$-modified conformal vector field $V$, we establish an inequality that implies the triviality of $V$. Further, we demonstrate that an affine Killing $m$-modified conformal vector field on a non-compact Riemannian manifold $M$ must be trivial. Finally, we show that an $m$-modified gradient conformal vector field is trivial under the assumptions of polynomial volume growth and convergence to zero at infinity.

While astronomers often assume that exoplanets are perfect spheres when analyzing observations, the subset of these distant worlds that are subject to strong tidal forces and/or rapid rotations are expected to be distinctly ellipsoidal or even triaxial. Since a planet's response to these forces is determined in part by its interior structure, measurements of an exoplanet's deviations from spherical symmetry can lead to powerful insights into its composition and surrounding environment. These shape deformations will imprint themselves on a planet's phase curve and transit lightcurve and cause small (1s-100s of parts per million) deviations from their spherical-planet counterparts. Until recently, these deviations were undetectable in typical real-world datasets due to limitations in photometric precision. Now, however, current and soon-to-come-online facilities such as JWST will routinely deliver observations that warrant the consideration of more complex models. To this end we present squishyplanet, a JAX-based Python package that implements an extension of the polynomial limb-darkened transit model presented in Agol et al. 2020 to non-spherical (triaxial) planets, as well as routines for modeling reflection and emission phase curves., Comment: 4 pages, accepted to JOSS

We have considered an effective field theory framework in which the Standard Model is extended by a non-renormalizable dimension six operator ($\frac{1}{\Lambda^2}(H^{\dagger}H)^3$) respecting the symmetries of the Standard Model. Such an operator can affect the dynamics of Higgs field and the electroweak phase transition. Presence of such term modifies the triple Higgs coupling which is already under the lens from di-Higgs searches in various channels at the LHC. Constraint on the scale parameter $\Lambda$ is obtained from the di-Higgs data at the LHC Run II. Additionally, such dimension six terms will give rise to additional couplings which will affect tri-Higgs and four-Higgs productions at future high energy, high luminosity hadron colliders. The shape of the scalar potential also gets modified due to such higher dimensional term composed of Higgs field. It is worthwhile to study the impact of such higher-dimensional operators at suitable gravitational wave (GW) interferometry experiments since GW astronomy has become a good probe for new physics searches. Bounds obtained on the scale parameter $\Lambda$ from the allowed first-order electroweak phase transition is $450 \lesssim \Lambda \lesssim 590$~GeV. On the other hand, di-Higgs results from the LHC Run II put a lower bound on $\Lambda \gtrsim 340$~GeV., Comment: 25 pages, 8 figures

Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning foundation models to human values and preferences. However, current RLHF techniques cannot account for the naturally occurring differences in individual human preferences across a diverse population. When these differences arise, traditional RLHF frameworks simply average over them, leading to inaccurate rewards and poor performance for individual subgroups. To address the need for pluralistic alignment, we develop a class of multimodal RLHF methods. Our proposed techniques are based on a latent variable formulation - inferring a novel user-specific latent and learning reward models and policies conditioned on this latent without additional user-specific data. While conceptually simple, we show that in practice, this reward modeling requires careful algorithmic considerations around model architecture and reward scaling. To empirically validate our proposed technique, we first show that it can provide a way to combat underspecification in simulated control problems, inferring and optimizing user-specific reward functions. Next, we conduct experiments on pluralistic language datasets representing diverse user preferences and demonstrate improved reward function accuracy. We additionally show the benefits of this probabilistic framework in terms of measuring uncertainty, and actively learning user preferences. This work enables learning from diverse populations of users with divergent preferences, an important challenge that naturally occurs in problems from robot learning to foundation model alignment., Comment: weirdlabuw.github.io/vpl

This paper utilizes video analytics to study pedestrian and vehicle traffic behavior, focusing on analyzing traffic patterns during football gamedays. The University of Florida (UF) hosts six to seven home football games on Saturdays during the college football season, attracting significant pedestrian activity. Through video analytics, this study provides valuable insights into the impact of these events on traffic volumes and safety at intersections. Comparing pedestrian and vehicle activities on gamedays versus non-gamedays reveals differing patterns. For example, pedestrian volume substantially increases during gamedays, which is positively correlated with the probability of the away team winning. This correlation is likely because fans of the home team enjoy watching difficult games. Win probabilities as an early predictor of pedestrian volumes at intersections can be a tool to help traffic professionals anticipate traffic management needs. Pedestrian-to-vehicle (P2V) conflicts notably increase on gamedays, particularly a few hours before games start. Addressing this, a "Barnes Dance" movement phase within the intersection is recommended. Law enforcement presence during high-activity gamedays can help ensure pedestrian compliance and enhance safety. In contrast, we identified that vehicle-to-vehicle (V2V) conflicts generally do not increase on gamedays and may even decrease due to heightened driver caution.

We study the effect of dark matter scalar and vector-mediated interactions on dark matter admixed neutron stars. In particular, we exploit the two-fluid formalism of Tolman-Oppenheimer-Volkoff equations as generic framework for dark matter admixed neutron stars. The fluids couple to each other only by gravity. In particular, the baryonic sector is described by the BSk22 equation of state, whereas we employ a relativistic mean field model for the dark matter, where we include the interaction of dark massive fermions with light dark scalar and vector mediators. We consider both the linear and the quadratic scalar interactions with the dark fermion. In the quadratic scalar scenario, we take into account a quartic self-interaction that significantly affects the stellar properties. Interestingly, in both cases, the effect of the scalar coupling is smaller than that of the vector, though quantitative differences arise. We also compute the sound speed of DM, finding that the scalar and quadratic couplings have an important influence on it. We compare our results with GW1708017, GW190425 and NICER data and constrain DM couplings and mass., Comment: 33 pages, 12 figures, 2 tables, comments are welcome

We analyse the impact of dark matter density spike around the Milky Way's supermassive black hole (SMBH), Sgr A$^*$, in probing the Bino-dominated neutralino dark matter (DM) $\tilde \chi_1^0$ within the MSSM, which typically produces relatively faint signals in the conventional DM halos. In particular, we explore the indirect search prospects of sub-TeV Bino-Higgsino and Bino-Wino-Higgsino DM in the MSSM, consistent with the supersymmetric predictions required to explain the anomalous magnetic moment of the muon. Typical over-abundance of Bino DM is ameliorated with slepton and/or Wino coannihilations. The lightest neutralino, thus, may be associated with a compressed supersymmetric particle spectrum, which, in general, is difficult to probe at conventional LHC searches. Similarly, for a rather tiny Higgsino mixing, $\tilde \chi_1^0$ does not offer much prospect to assess its predictions at dark matter direct detection searches. Accommodating the inclusive effects of density spike, here, we present the requisite boost factor to facilitate $\gamma-$ray searches of Bino-dominated DM in the MSSM, especially focusing on the Fermi-LAT and HESS observations., Comment: 29 pages, 11 figures, comments are welcome

The advent of Urban Air Mobility (UAM) presents the scope for a transformative shift in the domain of urban transportation. However, its widespread adoption and economic viability depends in part on the ability to optimally schedule the fleet of aircraft across vertiports in a UAM network, under uncertainties attributed to airspace congestion, changing weather conditions, and varying demands. This paper presents a comprehensive optimization formulation of the fleet scheduling problem, while also identifying the need for alternate solution approaches, since directly solving the resulting integer nonlinear programming problem is computationally prohibitive for daily fleet scheduling. Previous work has shown the effectiveness of using (graph) reinforcement learning (RL) approaches to train real-time executable policy models for fleet scheduling. However, such policies can often be brittle on out-of-distribution scenarios or edge cases. Moreover, training performance also deteriorates as the complexity (e.g., number of constraints) of the problem increases. To address these issues, this paper presents an imitation learning approach where the RL-based policy exploits expert demonstrations yielded by solving the exact optimization using a Genetic Algorithm. The policy model comprises Graph Neural Network (GNN) based encoders that embed the space of vertiports and aircraft, Transformer networks to encode demand, passenger fare, and transport cost profiles, and a Multi-head attention (MHA) based decoder. Expert demonstrations are used through the Generative Adversarial Imitation Learning (GAIL) algorithm. Interfaced with a UAM simulation environment involving 8 vertiports and 40 aircrafts, in terms of the daily profits earned reward, the new imitative approach achieves better mean performance and remarkable improvement in the case of unseen worst-case scenarios, compared to pure RL results., Comment: Presented at the AIAA Aviation Forum 2024

Analog front-end design heavily relies on specialized human expertise and costly trial-and-error simulations, which motivated many prior works on analog design automation. However, efficient and effective exploration of the vast and complex design space remains constrained by the time-consuming nature of SPICE simulations, making effective design automation a challenging endeavor. In this paper, we introduce INSIGHT, a GPU-powered, technology-agnostic, effective universal neural simulator in the analog front-end design automation loop. INSIGHT accurately predicts the performance metrics of analog circuits across various technologies with just a few microseconds of inference time. Notably, its autoregressive capabilities enable INSIGHT to accurately predict simulation-costly critical transient specifications leveraging less expensive performance metric information. The low cost and high fidelity feature make INSIGHT a good substitute for standard simulators in analog front-end optimization frameworks. INSIGHT is compatible with any optimization framework, facilitating enhanced design space exploration for sample efficiency through sophisticated offline learning and adaptation techniques. Our experiments demonstrate that INSIGHT-M, a model-based batch reinforcement learning sizing framework with INSIGHT as the accurate surrogate, only requires < 20 real-time simulations with 100-1000x lower simulation costs and significant speedup over existing sizing methods.

In analog circuits, process variation can cause unpredictability in circuit performance. Common-centroid (CC) type layouts have been shown to mitigate process-induced variations and are widely used to match circuit elements. Nevertheless, selecting the most suitable CC topology necessitates careful consideration of important layout constraints. Manual handling of these constraints becomes challenging, especially with large size problems. State-of-the-art CC placement methods lack an optimization framework to handle important layout constraints collectively. They also require manual efforts and consequently, the solutions can be suboptimal. To address this, we propose a unified framework based on multi-objective optimization for CC placement of analog transistors. Our method handles various constraints, including degree of dispersion, routing complexity, diffusion sharing, and layout dependent effects. The multi-objective optimization provides better handling of the objectives when compared to single-objective optimization. Moreover, compared to existing methods, our method explores more CC topologies. Post-layout simulation results show better performance compared to state-of-the-art techniques in generating CC layouts.

Quantum systems are open, continually exchanging energy and information with the surrounding environment. This interaction leads to decoherence and decay of quantum states. In complex systems, formed by many particles, decay can become correlated and enhanced. A fundamental question then arises: what is the maximal decay rate of a large quantum system, and how does it scale with its size? In this work, we address these issues by reformulating the problem into finding the ground state energy of a generic spin Hamiltonian. Inspired by recent work in Hamiltonian complexity theory, we establish rigorous and general upper and lower bounds on the maximal decay rate. These bounds are universal, as they hold for a broad class of Markovian many-body quantum systems. For many physically-relevant systems, the bounds are asymptotically tight, resulting in exact scaling laws with system size. Specifically, for large atomic arrays in free space, these scalings depend only on the arrays' dimensionality and are insensitive to details at short length-scales. The scaling laws establish fundamental limits on the decay rates of quantum states and offer valuable insights for research in many-body quantum dynamics, metrology, and fault tolerant quantum computation.

In this paper we expand on the work of the first author on ambient obstruction solitons, which are self-similar solutions to the ambient obstruction flow. Our main result is to show that any closed ambient obstruction soliton is ambient obstruction flat and has constant scalar curvature. We show, in fact, that the first part of this result is true for a more general extended soliton equation where we allow an arbitrary conformal factor to be added to the equation. We discuss how this implies that, on a compact manifold, the ambient obstruction flow has no fixed points up to conformal diffeomorphism other than ambient obstruction flat metrics. These results are the consequence of a general integral inequality that can be applied to the solitons to any geometric flow. Additionally, we use these results to obtain a generalization of the Bourguignon-Ezin identity on a closed Riemannian manifold and study the converse problem on a closed extended $q$-soliton. We also study this extended equation further in the case of homogeneous and product metrics.

Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital circuit design, the complexity and scarcity of data in analog circuitry pose significant challenges. To mitigate these issues, we introduce AnalogCoder, the first training-free LLM agent for designing analog circuits through Python code generation. Firstly, AnalogCoder incorporates a feedback-enhanced flow with tailored domain-specific prompts, enabling the automated and self-correcting design of analog circuits with a high success rate. Secondly, it proposes a circuit tool library to archive successful designs as reusable modular sub-circuits, simplifying composite circuit creation. Thirdly, extensive experiments on a benchmark designed to cover a wide range of analog circuit tasks show that AnalogCoder outperforms other LLM-based methods. It has successfully designed 20 circuits, 5 more than standard GPT-4o. We believe AnalogCoder can significantly improve the labor-intensive chip design process, enabling non-experts to design analog circuits efficiently.

The 4--8 GHz FR1(C) and 7--24 GHz upper mid-band FR3 spectrum are promising new 6G spectrum allocations being considered by the International Telecommunications Union (ITU) and major governments around the world. There is an urgent need to understand the propagation behavior and radio coverage, outage, and material penetration for the global mobile wireless industry in both indoor and outdoor environments in these emerging frequency bands. This work presents measurements and models that describe the penetration loss in co-polarized and cross-polarized antenna configurations, exhibited by common materials found inside buildings and on building perimeters, including concrete, low-emissivity glass, wood, doors, drywall, and whiteboard at 6.75 GHz and 16.95 GHz. Measurement results show consistent lower penetration loss at 6.75 GHz compared to 16.95 GHz for all ten materials measured for co and cross-polarized antennas at incidence. For instance, the low-emissivity glass wall presents 33.7 dB loss at 6.75 GHz, while presenting 42.3 dB loss at 16.95 GHz. Penetration loss at these frequencies is contrasted with measurements at sub-6 GHz, mmWave and sub-THz frequencies along with 3GPP material penetration loss models. The results provide critical knowledge for future 5G and 6G cellular system deployments as well as refinements for the 3GPP material penetration models., Comment: 6 pages, 4 figures, 2 tables, IEEE GLOBECOM 2024

New spectrum allocations in the 4--8 GHz FR1(C) and 7--24 GHz FR3 mid-band frequency spectrum are being considered for 5G/6G cellular deployments. This paper presents results from the world's first comprehensive indoor hotspot (InH) propagation measurement campaign at 6.75 GHz and 16.95 GHz in the NYU WIRELESS Research Center using a 1 GHz wideband channel sounder system over distances from 11 to 97 m in line-of-sight (LOS) and non-LOS (NLOS). Analysis of directional and omnidirectional path loss (PL) using the close-in free space 1 m reference distance model shows a familiar waveguiding effect in LOS with an omnidirectional path loss exponent (PLE) of 1.40 at 6.75 GHz and 1.32 at 16.95 GHz. Compared to mmWave frequencies, the directional NLOS PLEs are lower at FR3 and FR1(C), while omnidirectional NLOS PLEs are similar, suggesting better propagation distances at lower frequencies for links with omnidirectional antennas at both ends of the links, but also, importantly, showing that higher gain antennas will offer better coverage at higher frequencies when antenna apertures are kept same over all frequencies. Comparison of the omnidirectional and directional RMS delay spread (DS) at FR1(C) and FR3 with mmWave frequencies indicates a clear decrease with increasing frequency. The mean spatial lobe and omnidirectional RMS angular spread (AS) is found to be wider at 6.75 GHz compared to 16.95 GHz indicating more multipath components are found in the azimuthal spatial domain at lower frequencies., Comment: 6 pages, 7 figures, 4 tables, IEEE GLOBECOM 2024

Ultralight electrophilic scalar field can mediate a long-range force or radiate from a pulsar or a magnetar if the scalar field has a coupling with the Goldreich-Julian charge density or the net electron charge density of the star. The interaction of the electron with the long-range scalar profile results in a spatial variation of the electron mass. A scalar induced magnetic field is created due to such interaction. The mass of the scalar in such cases is constrained by the radius of the star. The scalar field can also radiate from a binary system or an isolated star if the mass of the scalar is less than the orbital frequency and the spin frequency respectively. The electrophilic scalar radiation can contribute to the orbital period loss of binary systems and pulsar spin-down. Comparing with existing and projected experimental sensitivities, we obtain constraints on scalar coupling with ultralight mass. Some of these bounds are stronger than the existing fifth force constraints. The constraints on the scalar coupling can be significantly screened if the scalar has a coupling with the ubiquitous cosmic neutrino background. Improvements in experimental sensitivity and observations of compact objects with stronger magnetic fields and higher angular velocities could further refine these bounds., Comment: 35 pages, 10 figures, and comments are welcome

We introduce the notion of a strong generalized holomorphic (SGH) fiber bundle and develop connection and curvature theory for an SGH principal $G$-bundle over a regular generalized complex (GC) manifold, where $G$ is a complex Lie group. We develop a de Rham cohomology for regular GC manifolds, and a Dolbeault cohomology for SGH vector bundles. Moreover, we establish a Chern-Weil theory for SGH principal $G$-bundles under certain mild assumptions on the leaf space of the GC structure. We also present a Hodge theory along with associated dualities and vanishing theorems for SGH vector bundles. Several examples of SGH fiber bundles are given., Comment: 73 pages, minor revision, comments are welcome

Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclical generation. We present techniques for augmenting hierarchies, which led to coverage increase by 98% for intents and 99% for colors in our knowledge graph., Comment: European Conference on Information Retrieval 2024

The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines (e.g., Flu, MMR, IPV, HPV vaccines) have also changed due to the pandemic. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. Almost all prior works that utilized social media to understand anti-vaccine opinions considered only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not been any effort to identify the specific reasons/concerns behind the anti-vax sentiments (e.g., side-effects, conspiracy theories, political reasons) on social media at scale. In this work, we propose two novel methods for classifying tweets into 11 different anti-vax concerns -- a discriminative approach (entailment-based) and a generative approach (based on instruction tuning of LLMs) -- which outperform several strong baselines. We then apply this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 - Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine concerns among the masses. We find that the pandemic has made the anti-vaccine discourse far more complex than in the pre-COVID times, and increased the variety of concerns being voiced. Alarmingly, we find that concerns about COVID vaccines are now being projected onto the non-COVID vaccines, thus making more people hesitant in taking vaccines in the post-COVID era., Comment: This work has been accepted to appear at the 18th International AAAI Conference on Web and Social Media (ICWSM), 2024

Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts. This phenomenon, where T2I models generate harmful representations that were not explicit in the input prompt, poses a potentially greater risk than adversarial prompts, leaving users unintentionally exposed to harms. Our paper addresses this issue by formalizing a definition for this phenomenon which we term harm amplification. We further contribute to the field by developing a framework of methodologies to quantify harm amplification in which we consider the harm of the model output in the context of user input. We then empirically examine how to apply these different methodologies to simulate real-world deployment scenarios including a quantification of disparate impacts across genders resulting from harm amplification. Together, our work aims to offer researchers tools to comprehensively address safety challenges in T2I systems and contribute to the responsible deployment of generative AI models.

Let $X$ be a complete variety over an algebraically closed field $k$ of characteristic zero, equipped with an action of an algebraic group $G$. Let $H$ be a reductive group. We study the notion of $G$-connection on a principal $H$-bundle. We give necessary and sufficient criteria for the existence of $G$-connections extending the Atiyah-Weil type criterion for holomorphic connections obtained by Azad and Biswas. We also establish a relationship between the existence of $G$-connection and equivariant structure on a principal $H$-bundle, under the assumption that $G$ is semisimple and simply connected. These results have been obtained by Biswas et al. when the underlying variety is smooth., Comment: 23 pages

Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels, e.g., barges. While methods to track vessels, e.g., tug and tow boats, are publicly available through Automatic Identification Systems (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are non-existent, especially in real-time settings. This paper develops a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models, specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet are employed. The model detects the presence of vessels and/or barges from video and performs a classification (no vessel or barge, vessel without barge, vessel with barge, and barge). A dataset of 331 annotated images was collected from five existing traffic cameras along the Mississippi and Ohio Rivers for model development. YOLOv8 achieves an F1-score of 96%, outperforming YOLOv5, SSD, and EfficientDet models with 86%, 79%, and 77% respectively. Sensitivity analysis was carried out regarding weather conditions (fog and rain) and location (Mississippi and Ohio rivers). A background subtraction technique was used to normalize video images across the various locations for the location sensitivity analysis. This model can be used to detect the presence of barges along river segments, which can be used for anonymous bulk commodity tracking and monitoring. Such data is valuable for long-range transportation planning efforts carried out by public transportation agencies, in addition to operational and maintenance planning conducted by federal agencies such as the US Army Corp of Engineers.

The next generation of wireless communication is expected to harness the potential of the sub-THz bands to achieve exceptional performance and ubiquitous connectivity. However, network simulators such as ns-3 currently lack support for channel models above 100 GHz. This limits the ability of researchers to study, design, and evaluate systems operating above 100 GHz. Here, we use the drop-based NYUSIM channel model to simulate channels above 100 GHz in all 3GPP scenarios including urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), indoor hotspot (InH), and indoor factory (InF). We evaluate the full stack downlink end-to-end performance (throughput, latency, and packet drop) experienced by a single user equipment (UE) connected to a Next Generation Node B (gNB) operating in the sub-THz bands for three gNB--UE antenna configurations: 8x8--4x4, 16x16--4x4, and 64x64--8x8 by using the NYUSIM channel model at 140 GHz in the ns-3 mmWave module. Our simulations demonstrate that sub-THz bands can enable high-fidelity applications that require data rates exceeding 1 Gbps and latency below 15 milliseconds (ms) using the current mmWave protocol stack, and large antenna arrays. In addition, we show the variation in throughput vs number of realizations and find the optimal number of realizations required to obtain statistically significant results. We strongly encourage researchers worldwide to adopt a similar approach, as it enables the readers to assess the accuracy and reliability of the reported results and enhance the findings' overall interpretability.

Flat space cosmologies (FSCs) are time dependent solutions of three-dimensional (3D) gravity with a vanishing cosmological constant. They can be constructed from a discrete quotient of empty 3D flat spacetime and are also called shifted-boost orbifolds. Using this quotient structure, we build a new and generalized Selberg zeta function for FSCs, and show that it is directly related to the scalar 1-loop partition function. We then propose an extension of this formalism applicable to more general quotient manifolds $\mathcal M/\mathbb Z$, based on representation theory of fields propagating on this background. Our prescription constitutes a novel and expedient method for calculating regularized 1-loop determinants, without resorting to the heat kernel. We compute quasinormal modes in the FSC using the zeroes of a Selberg zeta function, and match them to known results., Comment: 25 pages, a few clarifications added