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Children with disabilities remain among the most excluded from education in Global Partnership for Education (GPE) partner countries and other lower-income countries. Despite considerable activity funded both through GPE and by other donors, as well as by partner countries themselves, the level of international support to inclusive education remains too low and patchy for countries to transform their education systems so they are fully inclusive of children with disabilities. Development partners need to coordinate; help to build a stronger evidence base at global, regional and national levels; and go beyond isolated interventions to support reforms that have the potential to make the whole system more inclusive. To do this, development partners need to share knowledge on their activities globally and at the country level. This paper, and the underlying mapping exercise, aims to facilitate knowledge sharing in the sector by analyzing thematic and geographic focus areas of major development partners supporting inclusive education.

We report on the properties of SrTb2O4, a frustrated zigzag ladder antiferromagnet, studied by single crystal neutron diffraction (with polarised neutrons in zero field and unpolarised neutrons in an applied magnetic field), as well as by neutron spectroscopy on a polycrystalline sample. The neutron scattering results are supported by single crystal magnetisation and heat capacity measurements. In zero field, neutron diffraction data show no transition to a magnetically ordered state down to the lowest experimentally available temperature of 35 mK, and the material remains magnetically disordered down to this temperature. Polarised neutron diffraction measurements reveal the presence of a diffuse scattering signal suggesting only very weak spin-spin correlations in the ground state. For H // c (the easy magnetisation direction), we followed the magnetisation process using neutron diffraction measurements and observed the appearance of field-induced magnetic Bragg peaks with integer h and k indices in the (hk0) scattering plane. No magnetic peaks with a non-zero propagation vector were detected. The observed in-field data fit well to a simple two-sublattice model with magnetic moments aligned along the field direction but being significantly different in magnitude for the two inequivalent Tb3+ sites in the unit cell. Overall, the collected data point to a nonmagnetic ground state in SrTb2O4 despite the presence of strong interactions., Comment: 13 pages, 11 figures

Let $G$ be a finite simple graph, and let $I(G)$ denote its edge ideal. In this paper, we investigate the asymptotic behavior of the syzygies of powers of edge ideals through the lens of homological shift ideals $\text{HS}_i(I(G)^k)$. We introduce the notion of the $i$th homological strong persistence property for monomial ideals $I$, providing an algebraic characterization that ensures the chain of inclusions $\text{Ass}\,\text{HS}_i(I)\subseteq\text{Ass}\,\text{HS}_i(I^2)\subseteq\text{Ass}\,\text{HS}_i(I^3) \subseteq\cdots$. We prove that edge ideals possess both the 0th and 1st homological strong persistence properties. To this end, we explicitly describe the first homological shift algebra of $I(G)$ and show that $\text{HS}_1(I(G)^{k+1}) = I(G) \cdot \text{HS}_1(I(G)^k)$ for all $k \ge 1$. Finally, we conjecture that if $I(G)$ has a linear resolution, then $\text{HS}_i(I(G)^k)$ also has a linear resolution for all $k \gg 0$, and we present partial results supporting this conjecture.

With lowering thresholds, transparently defending against Rowhammer within DRAM is challenging due to the lack of time to perform mitigation. Commercially deployed in-DRAM defenses like TRR that steal time from normal refreshes~(REF) to perform mitigation have been proven ineffective against Rowhammer. In response, a new Refresh Management (RFM) interface has been added to the DDR5 specifications. RFM provides dedicated time to an in-DRAM defense to perform mitigation. Several recent works have used RFM for the intended purpose - building better Rowhammer defenses. However, to the best of our knowledge, no prior study has looked at the potential security implications of this new feature if an attacker subjects it to intentional misuse. Our paper shows that RFM introduces new side effects in the system - the activity of one bank causes interference with the operation of the other banks. Thus, the latency of a bank becomes dependent on the activity of other banks. We use these side effects to build two new attacks. First, a novel memory-based covert channel, which has a bandwidth of up to 31.3 KB/s, and is also effective even in a bank-partitioned system. Second, a new Denial-of-Service (DOS) attack pattern that exploits the activity within a single bank to reduce the performance of the other banks. Our experiments on SPEC2017, PARSEC, and LIGRA workloads show a slowdown of up to 67\% when running alongside our DOS pattern. We also discuss potential countermeasures for our attacks.

Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S\&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S\&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems., Comment: 25, 6

This paper explores the generalized ghost dark energy model in the framework of $f(\textsl{Q}, \textsl{L}_{m})$ gravity, where $\textsl{Q}$ represents the non-metricity scalar and $\textsl{L}_{m}$ denotes the matter-Lagrangian density. We take the homogeneous and isotropic universe with an ideal matter distribution and examine a scenario with interacting dark energy and dark matter. We then reconstruct $f(\textsl{Q}, \textsl{L}_{m})$ model to examine the effects of this extended gravitational framework on the cosmic evolution. The behavior of numerous cosmic parameters are explored corresponding to distinct parametric values. The stability is evaluated by the squared sound speed method. The statefinder $(r,s)$ and standard diagnostic pairs $(\omega_D-\omega'_{D})$ are used to study the various cosmic eras. Our results align with recent observational evidence, indicating that the $f(\textsl{Q}, \textsl{L}_{m})$ model effectively characterizes dark energy and cosmic evolution., Comment: 28 pages, 6 figures, Accepted

Let $S=K[x_1,\dots,x_n]$ be the polynomial ring over a field $K$, and let $I\subset S$ be a monomial ideal. In this paper, we introduce the $i$th homological shift algebras $\text{HS}_i(\mathcal{R}(I))=S\oplus\bigoplus_{k\ge1}\text{HS}_i(I^k)$ of $I$. These algebras have the structure of a finitely generated bigraded module over the Rees algebra $\mathcal{R}(I)$ of $I$. Hence, many invariants of $\text{HS}_i(I^k)$, such as depth, associated primes, regularity, and the $\text{v}$-number, exhibit well behaved asymptotic behavior. We particularly investigate $\text{HS}_i(I^k)$ when $I$ has linear powers, and determine several families of monomial ideals $I$ for which $\text{HS}_i(I^k)$ has linear resolution for all $k\gg0$. Finally, we show that $\text{HS}_i(I^k)$ is Golod for all monomial ideals $I\subset S$ and all $k\gg0$., Comment: Dedicated with deep gratitude to the memory of Professor J\"urgen Herzog, inspiring mathematician and master of monomials

Narrowband infrared organic photodetectors are in great demand for sensing, imaging, and spectroscopy applications. However, most existing strategies for narrowband detection depend on spectral filtering either through saturable absorption, which requires active layers exceeding 500 nm, restricting the choice of materials for producing high-quality films, or cavity effects, which inherently introduce strong angular dispersion. Microcavity exciton-polariton (polariton) modes, which emerge from strong exciton-photon coupling, have recently been explored as an angular dispersion suppression strategy for organic optoelectronics. In this work, we present the first narrowband infrared polariton organic photodiode that combines angle-independent response with a record-high responsivity of 0.24 A/W at 965 nm and -2 V. This device, featuring a 100-nm-thin active layer comprising a non-fullerene acceptor, exhibits a detection mode with a full-width at half maximum of less than 30 nm and a marginal angular dispersion of under 15 nm across $\pm$$45^\circ$. This study highlights the potential of polaritons as an innovative platform for developing next-generation optoelectronic devices that achieve simultaneous enhancements in optical and electronic performance., Comment: 23 pages, 4 figures, supporting information

Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these modifications in the jet's hard substructure. In this Letter, we present a machine learning framework to identify quenched jets while accounting for pileup, uncorrelated soft particle background, and detector effects; a more experimentally realistic and challenging scenario than previously addressed. Our approach leverages an interpretable sequential attention-based mechanism that integrates representations of individual jet constituents alongside global jet observables as features. The framework sets a new benchmark for tagging quenched jets with reduced model dependence.

Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) algorithms, including convolutional neural networks (CNN), within a smartphone-based application. The app enables users to take pictures of their test kits, which YOLOv8 then processes to precisely crop and extract the membrane region, even if the test kit is not centered in the frame or is positioned at the very edge of the image. This capability offers greater accessibility, allowing even visually impaired individuals to capture test images without needing perfect alignment, thus promoting user independence and inclusivity. The extracted image is analyzed by an additional CNN classifier that determines if the results are positive, negative, or invalid, providing users with the results and a confidence level. Through validation experiments with commonly used rapid test kits across various diagnostic applications, our results demonstrate that the synergistic integration of AI significantly improves sensitivity and specificity in test result interpretation. This improvement can be attributed to the extraction of the membrane zones from the test kit images using the state-of-the-art YOLO algorithm. Additionally, we performed SHapley Additive exPlanations (SHAP) analysis to investigate the factors influencing the model's decisions, identifying reasons behind both correct and incorrect classifications. By facilitating the differentiation of genuine test lines from background noise and providing valuable insights into test line intensity and uniformity, our approach offers a robust solution to challenges in rapid test interpretation.

We present parameter sets corresponding to new underlying event tunes for the Herwig7.3 Monte Carlo event generator. The existing Herwig tunes are in good agreement with LHC data, however, they are not typically designed for center-of-mass energies below $\sqrt{s}=300$ GeV. The tunes presented in this study can describe mid-rapidity data collected at the nominal RHIC energy of $\sqrt{s }=200$ GeV, as well as higher center-of-mass energies utilized by experiments elsewhere, such as the LHC. The base "New Haven" tune is developed by fitting minimum-bias simulations of proton-proton collisions to mid-rapidity identified hadron and jet data from the STAR experiment. The "Nashville" tune includes a separate set of parameters developed by tuning to Tevatron proton-antiproton data at $\sqrt{s}=300$, $900$ and $1960$ GeV from CDF, and LHC proton-proton measurements from CMS at $\sqrt{s}=7$ TeV, in addition to the STAR measurements. Both new tunes demonstrate significant improvements over the recommended default tune currently included in the latest version of Herwig for minimum bias production. As such, we advocate using these tunes for future simulation studies at mid-rapidity by the experimental collaborations at RHIC (STAR and sPHENIX) and the LHC (ATLAS, ALICE, CMS).

We present a phenomenology study probing pair production of supersymmetric charginos and neutralinos ("electroweakinos") with the vector boson fusion (VBF) topology in proton-proton collisions at CERN's Large Hadron Collider (LHC). In particular, we examine the compressed-mass spectrum phase space that has been traditionally challenging due to experimental constraints. The final states considered have two jets, large missing transverse momentum, and one, two, or three light leptons. Different model scenarios are considered for the production and decays of the electroweakinos. A novel high-performance and interpretable sequential attention-based machine learning algorithm is employed for signal-background discrimination and is observed to significantly improve signal sensitivity over traditional methods. We report expected signal significances for integrated luminosities of $137$, $300$, and $3000$ $\textrm{fb}^{-1}$ corresponding to the current data acquired at the LHC, expectation for the end of Run 3, and the expectation for the high-luminosity LHC. Our methodology results in projected 95\% confidence level bounds that cover chargino masses up to 1.1 TeV in compressed-mass spectrum scenarios within the R-parity conserving minimal supersymmetric standard model. This parameter space, currently beyond the reach of ATLAS and CMS searches at the LHC, is traditionally challenging to explore due to significant Standard Model backgrounds and low signal cross-sections.

Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper presents a new, integrated heuristic-based active sensing and Monte-Carlo Tree Search (MCTS)-based retrieval planning approach. These components provide feedback to one another to actively sense critical, unobserved areas suitable for the retrieval planner to plan a sequence for relocating path-blocking obstacles and a collision-free trajectory for retrieving the target object. We demonstrate the effectiveness of our approach using a robot arm equipped with an in-hand camera in both simulated and real-world confined, cluttered scenarios. Our framework is compared against various state-of-the-art methods. The results indicate that our proposed approach outperforms baseline methods by a significant margin in terms of the success rate, the object rearrangement planning time consumption and the number of planning trials before successfully retrieving the target. Videos can be found at https://youtu.be/tea7I-3RtV0.

We address the issue of the exploding computational requirements of recent State-of-the-art (SOTA) open set multimodel 3D mapping (dense 3D mapping) algorithms and present Voxel-Aggregated Feature Synthesis (VAFS), a novel approach to dense 3D mapping in simulation. Dense 3D mapping involves segmenting and embedding sequential RGBD frames which are then fused into 3D. This leads to redundant computation as the differences between frames are small but all are individually segmented and embedded. This makes dense 3D mapping impractical for research involving embodied agents in which the environment, and thus the mapping, must be modified with regularity. VAFS drastically reduces this computation by using the segmented point cloud computed by a simulator's physics engine and synthesizing views of each region. This reduces the number of features to embed from the number of captured RGBD frames to the number of objects in the scene, effectively allowing a "ground truth" semantic map to be computed an order of magnitude faster than traditional methods. We test the resulting representation by assessing the IoU scores of semantic queries for different objects in the simulated scene, and find that VAFS exceeds the accuracy and speed of prior dense 3D mapping techniques., Comment: 6 pages, 2 figures, CVPR 2025

Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as an additional cue to compensate for the loss of building height estimation using single-view imagery. We proposed a novel method that first localized a building and its shadow in the given satellite image. After localization, the shadow length is estimated using a regression model. To estimate the final height of each building, we utilize the principles of photogrammetry, specifically considering the relationship between the solar elevation angle, the vertical edge length of the building, and the length of the building's shadow. For the localization of buildings in our model, we utilized a modified YOLOv7 detector, and to regress the shadow length for each building we utilized the ResNet18 as backbone architecture. Finally, we estimated the associated building height using solar elevation with shadow length through analytical formulation. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin., Comment: 6 pages, 5 figures, 2 tables

Simulators are indispensable parts of the research and development necessary to advance countless industries, including cellular networks. With simulators, the evaluation, analysis, testing, and experimentation of novel designs and algorithms can be executed in a more cost-effective and convenient manner without the risk of real network service disruption. Additionally, recent trends indicate that the advancement of these Digital System Models (DSM), such as system-level simulators, will hold a pivotal role in advancing cellular networks by facilitating the development of digital twins. Given this growing significance, in this survey and tutorial paper, we present an extensive review of the currently available DSMs for 5G and beyond (5G&B) networks. Specifically, we begin with a tutorial on the fundamental concepts of 5G&B network simulations, followed by an identification of the essential design requirements needed to model the key features of these networks. We also devised a taxonomy of different types of 5G&B network simulators. In contrast to existing simulator surveys, which mostly leverage traditional metrics applicable to legacy networks, we devise and use 5G-specific evaluation metrics that capture three key facets of a network simulator, namely realism, completeness, and computational efficiency. We evaluate each simulator according to the devised metrics to generate an applicability matrix that maps different 5G&B simulators vis-a-vis the different research themes they can potentially enable. We also present the current challenges in developing 5G&B simulators while laying out several potential solutions to address the issues. Finally, we discuss the future challenges related to simulator design provisions that will arise with the emergence of 6G networks.

Templated copolymerization, in which information stored in the sequence of a heteropolymer template is copied into another polymer product, is the mechanism behind all known methods of genetic information transfer. A key aspect of templated copolymerization is the eventual detachment of the product from the template. A second key feature of natural biochemical systems is that the template-binding free energies of both correctly-matched and incorrect monomers are heterogeneous. Previous work has considered the thermodynamic consequences of detachment, and the consequences of heterogeniety for polymerisation speed and accuracy, but the interplay of both separation and heterogeniety remains unexplored. In this work, we investigate a minimal model of templated copying that simultaneously incorporates both detachment from behind the leading edge of the growing copy and heterogeneous interactions. We first extend existing coarse-graining methods for models of polymerisation to allow for heterogeneous interactions. We then show that heterogeneous copying systems with explicit detachment do not exhibit the subdiffusive behaviour observed in the absence of detachment when near equilibrium. Next, we show that heterogeneity in correct monomer interactions tends to result in slower, less accurate copying, while heterogeneity in incorrect monomer interactions tends to result in faster, more accurate copying, due to an increased roughness in the free energy landscape of either correct or incorrect monomer pairs. Finally, we show that heterogeneity can improve on known thermodynamic efficiencies of homogeneous copying, but these increased thermodynamic efficiencies do not always translate to increased efficiencies of information transfer.

Planar microcavity polaritons have recently emerged as a promising technology for improving several performance characteristics of organic light-emitting diodes, photodiodes and photovoltaics. To form polaritons and achieve enhanced performance, traditional microcavities with high reflectivity mirrors are fabricated by energy-intensive physical vapor deposition methods, which restrict their use in applications requiring flexibility and low cost. Here, for the first time, we demonstrate a dielectric all-solution-processed polariton microcavity consisting of Rhodamine 6G films in a poly(vinyl alcohol) matrix, exhibiting more than 400 meV Rabi-splitting and photoluminescence with uniform dispersion along the lower polariton mode. Our fully automated deposition and annealing fabrication protocol played a key role in preventing interlayer mixing and producing high optical-quality polariton microcavities, enabling us to observe enhanced scattering of reservoir excitons to the lower polariton and to explore the effects of strong coupling on bimolecular interactions. Notably, we found that polariton microcavities exhibit a more than 10-fold increase in the critical excitation density for bimolecular annihilation compared to bare Rhodamine 6G films. This enhancement can only be partially attributed to the sub-3-fold measured enhancement in radiative lifetime, highlighting the critical role of strong coupling in the influence of molecular dynamics., Comment: 25 pages, 4 figures, supporting information

A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model contains a light scalar boson $\phi'$ and a heavy vector-like quark $\chi_\mathrm{u}$ that can be probed at CERN's Large Hadron Collider (LHC). We perform a phenomenology study on the production of $\phi'$ and ${\chi}_u$ particles from proton-proton $(\mathrm{pp})$ collisions at the LHC at $\sqrt{s}=13.6$ TeV, primarily through $g{-g}$ and $t{-\chi_\mathrm{u}}$ fusion. We work under an effective field theory approach, in which the $\chi_\mathrm{u}$ and $\phi'$ masses are free parameters. We perform a phenomenological analysis considering $\chi_\mathrm{u}$ final states to b-quarks, muons, and neutrinos, and $\phi'$ decays to $\mu^+\mu^-$. A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of $3000$ $\textrm{fb}^{-1}$. The proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.

Visual servoing, the method of controlling robot motion through feedback from visual sensors, has seen significant advancements with the integration of optical flow-based methods. However, its application remains limited by inherent challenges, such as the necessity for a target image at test time, the requirement of substantial overlap between initial and target images, and the reliance on feedback from a single camera. This paper introduces Imagine2Servo, an innovative approach leveraging diffusion-based image editing techniques to enhance visual servoing algorithms by generating intermediate goal images. This methodology allows for the extension of visual servoing applications beyond traditional constraints, enabling tasks like long-range navigation and manipulation without predefined goal images. We propose a pipeline that synthesizes subgoal images grounded in the task at hand, facilitating servoing in scenarios with minimal initial and target image overlap and integrating multi-camera feedback for comprehensive task execution. Our contributions demonstrate a novel application of image generation to robotic control, significantly broadening the capabilities of visual servoing systems. Real-world experiments validate the effectiveness and versatility of the Imagine2Servo framework in accomplishing a variety of tasks, marking a notable advancement in the field of visual servoing., Comment: Published at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing mapping methods create maps that require computationally expensive motion planning tools to find a path solution. In this paper, we propose a new mapping feature called arrival time fields, which is a solution to the Eikonal equation. The arrival time fields can directly guide the robot in navigating the given environments. Therefore, this paper introduces a new approach called Active Neural Time Fields (Active NTFields), which is a physics-informed neural framework that actively explores the unknown environment and maps its arrival time field on the fly for robot motion planning. Our method does not require any expert data for learning and uses neural networks to directly solve the Eikonal equation for arrival time field mapping and motion planning. We benchmark our approach against state-of-the-art mapping and motion planning methods and demonstrate its superior performance in both simulated and real-world environments with a differential drive robot and a 6 degrees-of-freedom (DOF) robot manipulator. The supplementary videos can be found at https://youtu.be/qTPL5a6pRKk, and the implementation code repository is available at https://github.com/Rtlyc/antfields-demo.

Quantum error correction is necessary for achieving exponential speedups on important applications. The planar surface code has remained the most studied error-correcting code for the last two decades because of its relative simplicity. However, encoding a singular logical qubit with the planar surface code requires physical qubits quadratic in the code distance~($d$), making it space-inefficient for the large-distance codes necessary for promising applications. Thus, {\em Quantum Low-Density Parity-Check (QLDPC)} have emerged as an alternative to the planar surface code but require a higher degree of connectivity. Furthermore, the problems of fault-tolerant syndrome extraction and decoding are understudied for these codes and also remain obstacles to their usage. In this paper, we consider two under-studied families of QLDPC codes: hyperbolic surface codes and hyperbolic color codes. We tackle the three challenges mentioned above as follows. {\em First}, we propose {\em Flag-Proxy Networks (FPNs)}, a generalizable architecture for quantum codes that achieves low connectivity through flag and proxy qubits. {\em Second}, we propose a {\em greedy syndrome extraction scheduling} algorithm for general quantum codes and further use this algorithm for fault-tolerant syndrome extraction on FPNs. {\em Third}, we present two decoders that leverage flag measurements to decode the hyperbolic codes accurately. Our work finds that degree-4 FPNs of the hyperbolic surface and color codes are respectively $2.9\times$ and $5.5\times$ more space-efficient than the $d = 5$ planar surface code, and become even more space-efficient when considering higher distances. The hyperbolic codes also have error rates comparable to their planar counterparts., Comment: Accepted to the International Symposium on Computer Microarchitecture (MICRO) 2024. Will be presented in November 2024

Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and real-world visual data. In this paper, we propose SplatSim, a novel framework that leverages Gaussian Splatting as the primary rendering primitive to reduce the Sim2Real gap for RGB-based manipulation policies. By replacing traditional mesh representations with Gaussian Splats in simulators, SplatSim produces highly photorealistic synthetic data while maintaining the scalability and cost-efficiency of simulation. We demonstrate the effectiveness of our framework by training manipulation policies within SplatSim and deploying them in the real world in a zero-shot manner, achieving an average success rate of 86.25%, compared to 97.5% for policies trained on real-world data. Videos can be found on our project page: https://splatsim.github.io

The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.

Understanding stakeholder needs is essential for project success, as stakeholder importance varies across projects. This study proposes a framework for early stakeholder identification and continuous engagement throughout the project lifecycle. The framework addresses common organizational failures in stakeholder communication that lead to project delays and cancellations. By classifying stakeholders by influence and interest, establishing clear communication channels, and implementing regular feedback loops, the framework ensures effective stakeholder involvement. This approach allows for necessary project adjustments and builds long-term relationships, validated by a survey of IT professionals. Engaging stakeholders strategically at all stages minimizes misunderstandings and project risks, contributing to better project management and lifecycle outcomes., Comment: 15 Pages

With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data. A number of studies have shown that such models propagate gender stereotypes, as well as geographical and racial bias, among other biases. While existing works tackle this issue by preprocessing data and debiasing embeddings, the proposed methods require a lot of computational resources and annotation effort while being limited to certain types of biases. To address these issues, we introduce REFINE-LM, a debiasing method that uses reinforcement learning to handle different types of biases without any fine-tuning. By training a simple model on top of the word probability distribution of a LM, our bias agnostic reinforcement learning method enables model debiasing without human annotations or significant computational resources. Experiments conducted on a wide range of models, including several LMs, show that our method (i) significantly reduces stereotypical biases while preserving LMs performance; (ii) is applicable to different types of biases, generalizing across contexts such as gender, ethnicity, religion, and nationality-based biases; and (iii) it is not expensive to train.

This paper investigates secure low-cost in-DRAM trackers for mitigating Rowhammer (RH). In-DRAM solutions have the advantage that they can solve the RH problem within the DRAM chip, without relying on other parts of the system. However, in-DRAM mitigation suffers from two key challenges: First, the mitigations are synchronized with refresh, which means we cannot mitigate at arbitrary times. Second, the SRAM area available for aggressor tracking is severely limited, to only a few bytes. Existing low-cost in-DRAM trackers (such as TRR) have been broken by well-crafted access patterns, whereas prior counter-based schemes require impractical overheads of hundreds or thousands of entries per bank. The goal of our paper is to develop an ultra low-cost secure in-DRAM tracker. Our solution is based on a simple observation: if only one row can be mitigated at refresh, then we should ideally need to track only one row. We propose a Minimalist In-DRAM Tracker (MINT), which provides secure mitigation with just a single entry. At each refresh, MINT probabilistically decides which activation in the upcoming interval will be selected for mitigation at the next refresh. MINT provides guaranteed protection against classic single and double-sided attacks. We also derive the minimum RH threshold (MinTRH) tolerated by MINT across all patterns. MINT has a MinTRH of 1482 which can be lowered to 356 with RFM. The MinTRH of MINT is lower than a prior counter-based design with 677 entries per bank, and is within 2x of the MinTRH of an idealized design that stores one-counter-per-row. We also analyze the impact of refresh postponement on the MinTRH of low-cost in-DRAM trackers, and propose an efficient solution to make such trackers compatible with refresh postponement., Comment: 13 pages including appendix

DRAM cells are susceptible to Data-Disturbance Errors (DDE), which can be exploited by an attacker to compromise system security. Rowhammer is a well-known DDE vulnerability that occurs when a row is repeatedly activated. Rowhammer can be mitigated by tracking aggressor rows inside DRAM (in-DRAM) or at the Memory Controller (MC). Row-Press (RP) is a new DDE vulnerability that occurs when a row is kept open for a long time. RP significantly reduces the number of activations required to induce an error, thus breaking existing RH solutions. Prior work on Explicit Row-Press mitigation, ExPress, requires the memory controller to limit the maximum row-open-time, and redesign existing Rowhammer solutions with reduced Rowhammer threshold. Unfortunately, ExPress incurs significant performance and storage overheads, and being a memory controller-based solution, it is incompatible with in-DRAM trackers. In this paper, we propose Implicit Row-Press mitigation (ImPress), which does not restrict row-open-time, is compatible with memory controller-based and in-DRAM solutions and does not reduce the tolerated Rowhammer threshold. ImPress treats a row open for a specified time as equivalent to an activation. We design ImPress by developing a Unified Charge-Loss Model, which combines the net effect of both Rowhammer and Row-Press for arbitrary patterns. We analyze both controller-based (Graphene and PARA) and in-DRAM trackers (Mithril and MINT). We show that ImPress makes Rowhammer solutions resilient to Row-Press transparently, without affecting the Rowhammer threshold., Comment: 12 page paper

This research is motivated by a scenario where a group of UAVs is assigned to map an unknown scalar field, with the imperative of maintaining a safe distance from the sources of the field to evade detection or damage. The location of the sources is unknown a priori, so the UAVs rely on measurements of the field intensity to gauge safety. The UAVs estimate the unknown scalar field using Gaussian process (GP) regression and use the estimate to generate a map of high-intensity regions using Hough transform (HT), updated online based on the field measurements. A convergence analysis shows the boundedness of the error between the actual scalar field and the learned scalar field. The effectiveness of the method is evaluated through simulations, showcasing its ability to accurately learn scalar fields with multiple high-intensity regions while reducing the number of measurements taken inside the high-intensity regions.

Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs. Predicting compositions unseen during training is a challenging task. We are exploring Open World Compositional Zero-Shot Learning (OW-CZSL) in this study, where our test space encompasses all potential combinations of attributes and objects. Our approach involves utilizing the self-attention mechanism between attributes and objects to achieve better generalization from seen to unseen compositions. Utilizing a self-attention mechanism facilitates the model's ability to identify relationships between attribute and objects. The similarity between the self-attended textual and visual features is subsequently calculated to generate predictions during the inference phase. The potential test space may encompass implausible object-attribute combinations arising from unrestricted attribute-object pairings. To mitigate this issue, we leverage external knowledge from ConceptNet to restrict the test space to realistic compositions. Our proposed model, Attention-based Simple Primitives (ASP), demonstrates competitive performance, achieving results comparable to the state-of-the-art., Comment: 10 pages, 6 figures

As the novel topological states, the higher-order topological insulators have attracted great attentions in the past years. However, their realizations in realistic materials, in particular in two dimensional systems, remains the big challenge due to the lack of adequate candidates. Here, based on the first-principle calculation and tight-binding model simulations, we identify the currently \emph{existing} bilayer $\alpha_{5}$-phase borophenes as the two-dimensional second-order topological insulators, protected by the $C_{2}$-rotational symmetry. The formation of interlayer B-B covalent bonds, stabilizing the bilayer borophenes and opening the large direct bulk gaps ($\sim 0.55-0.62$ eV) at Fermi level, plays the key roles. The second-order topology is characterized by the bulk quantized quadrupole momentum. Our results enriches the candidates for the second-order topological insulators, and also provide a way to study topological states in borophenes., Comment: 9 Pages, 7 figures

The security vulnerabilities due to Rowhammer have worsened over the last decade, with existing in-DRAM solutions, such as TRR, getting broken with simple patterns. In response, the DDR5 specifications have been extended to support Per-Row Activation Counting (PRAC), with counters inlined with each row, and ALERT-Back-Off (ABO) to stop the memory controller if the DRAM needs more time to mitigate. Although PRAC+ABO represents a strong advance in Rowhammer protection, they are just a framework, and the actual security is dependent on the implementation. In this paper, we first show that a prior work, Panopticon (which formed the basis for PRAC+ABO), is insecure, as our Jailbreak pattern can cause 1150 activations on an attack row for Panopticon configured for a threshold of 128. We then propose MOAT, a provably secure design, which uses two internal thresholds: ETH, an "Eligibility Threshold" for mitigating a row, and ATH, an "ALERT Threshold" for initiating an ABO. As JEDEC specifications permit a few activations between consecutive ALERTs, we also study how an attacker can exploit such activations to inflict more activations than ATH on an attack row and thus increase the tolerated Rowhammer threshold. Our analysis shows that MOAT configured with ATH=64 can safely tolerate a Rowhammer threshold of 99. Finally, we also study performance attacks and denial-of-service due to ALERTs. Our evaluations, with SPEC and GAP workloads, show that MOAT with ATH=64 incurs an average slowdown of 0.28\% and 7 bytes of SRAM per bank., Comment: 10.5 pages normal text, 2.5 pages of appendix

Global time is a gauge or relational choice of time variable in canonical gravity. Local time is the time used in a flat patch of spacetime. We compare the dynamics of a scalar field with respect to choices of global time and Minkowski patch time in an expanding cosmology. Our main results are that evolutions starting from the same initial conditions are similar on the time scales of terrestrial experiments, and that global time leads to a mechanism for evolving coupling constants., Comment: 18 pages, 5 figures. Comments welcome

Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc. Prior work typically makes use of previous frames in a multi view stereo framework, relying on matching textures in a local neighborhood. In contrast, our model leverages historical predictions by giving the latest 3D geometry data as an extra input to our network. This self-generated geometric hint can encode information from areas of the scene not covered by the keyframes and it is more regularized when compared to individual predicted depth maps for previous frames. We introduce a Hint MLP which combines cost volume features with a hint of the prior geometry, rendered as a depth map from the current camera location, together with a measure of the confidence in the prior geometry. We demonstrate that our method, which can run at interactive speeds, achieves state-of-the-art estimates of depth and 3D scene reconstruction in both offline and incremental evaluation scenarios., Comment: ECCV 2024 Version

In this paper, a novel online, output-feedback, critic-only, model-based reinforcement learning framework is developed for safety-critical control systems operating in complex environments. The developed framework ensures system stability and safety, regardless of the lack of full-state measurement, while learning and implementing an optimal controller. The approach leverages linear matrix inequality-based observer design method to efficiently search for observer gains for effective state estimation. Then, approximate dynamic programming is used to develop an approximate controller that uses simulated experiences to guarantee the safety and stability of the closed-loop system. Safety is enforced by adding a recentered robust Lyapunov-like barrier function to the cost function that effectively enforces safety constraints, even in the presence of uncertainty in the state. Lyapunov-based stability analysis is used to guarantee uniform ultimate boundedness of the trajectories of the closed-loop system and ensure safety. Simulation studies are performed to demonstrate the effectiveness of the developed method through two real-world safety-critical scenarios, ensuring that the state trajectories of a given system remain in a given set and obstacle avoidance.

In this article, we study the squarefree powers of facet ideals associated with simplicial trees. Specifically, we examine the linearity of their minimal free resolution and their regularity. Additionally, we investigate when the first syzygy module of squarefree powers of a simplicial tree is generated by linear relations. Finally, we provide a combinatorial formula for the regularity of the squarefree powers of $t$-path ideals of path graphs., Comment: Any comments are welcome

With the continuous advancement of large language models (LLMs), it is essential to create new benchmarks to effectively evaluate their expanding capabilities and identify areas for improvement. This work focuses on multi-image reasoning, an emerging capability in state-of-the-art LLMs. We introduce ReMI, a dataset designed to assess LLMs' ability to Reason with Multiple Images. This dataset encompasses a diverse range of tasks, spanning various reasoning domains such as math, physics, logic, code, table/chart understanding, and spatial and temporal reasoning. It also covers a broad spectrum of characteristics found in multi-image reasoning scenarios. We have benchmarked several cutting-edge LLMs using ReMI and found a substantial gap between their performance and human-level proficiency. This highlights the challenges in multi-image reasoning and the need for further research. Our analysis also reveals the strengths and weaknesses of different models, shedding light on the types of reasoning that are currently attainable and areas where future models require improvement. To foster further research in this area, we are releasing ReMI publicly: https://huggingface.co/datasets/mehrankazemi/ReMI.

Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However, such purely geometric methods are understandably oblivious to plane semantics, which are crucial to discerning distinct planes. To overcome this limitation, we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.

Given the rapid emergence and applications of Large Language This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLO11 (or YOLOv11). Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv11 and progressing through YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications. YOLOV11 to YOLOv1, Comment: 11 Figures, 7 Tables

As urban areas grapple with unprecedented challenges stemming from population growth and climate change, the emergence of urban digital twins offers a promising solution. This paper presents a case study focusing on Sydney's urban digital twin, a virtual replica integrating diverse real-time and historical data, including weather, crime, emissions, and traffic. Through advanced visualization and data analysis techniques, the study explores some applications of this digital twin in urban sustainability, such as spatial ranking of suburbs and automatic identification of correlations between variables. Additionally, the research delves into predictive modeling, employing machine learning to forecast traffic crash risks using environmental data, showcasing the potential for proactive interventions. The contributions of this work lie in the comprehensive exploration of a city-scale digital twin for sustainable urban planning, offering a multifaceted approach to data-driven decision-making.