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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.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. 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 General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications., 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.

Many important phenomena in chemistry and biology are realized via dynamical features such as multi-stability, oscillations, and chaos. Construction of novel chemical systems with such finely-tuned dynamics is a challenging problem central to the growing field of synthetic biology. In this paper, we address this problem by putting forward a molecular version of a recurrent artificial neural network, which we call a recurrent neural chemical reaction network (RNCRN). We prove that the RNCRN, with sufficiently many auxiliary chemical species and suitable fast reactions, can be systematically trained to achieve any dynamics. This approximation ability is shown to hold independent of the initial conditions for the auxiliary species, making the RNCRN more experimentally feasible. To demonstrate the results, we present a number of relatively simple RNCRNs trained to display a variety of biologically-important dynamical features.

Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents subject to quantization. Each node possesses a private local cost function, collectively contributing to a global cost function, which the proposed methodology aims to minimize. In contrast to many existing literature, the information exchange among nodes is quantized. We adopt a first-order computationally-efficient distributed optimization algorithm (with no extra inner consensus loop) that leverages node-level gradient correction based on local data and network-level gradient aggregation only over nearby nodes. This method only requires balanced networks with no need for stochastic weight design. It can handle log-scale quantized data exchange over possibly time-varying and switching network setups. We analyze convergence over both structured networks (for example, training over data-centers) and ad-hoc multi-agent networks (for example, training over dynamic robotic networks). Through analysis and experimental validation, we show that (i) structured networks generally result in a smaller optimality gap, and (ii) logarithmic quantization leads to smaller optimality gap compared to uniform quantization.

We prove the componentwise linearity of ideals that satisfy a certain exchange property similar to polymatroidal ideals. We also discuss the componentwise linearity and exchange properties of ideals of $k$-covers of totally balanced weighted hypergraphs.

Patient hand-off and triage are two fundamental problems in health care. Often doctors must painstakingly summarize complex findings to efficiently communicate with specialists and quickly make decisions on which patients have the most urgent cases. In pursuit of these challenges, we present (1) a model with state-of-art radiology report summarization performance using (2) a novel method for augmenting medical data, and (3) an analysis of the model limitations and radiology knowledge gain. We also provide a data processing pipeline for future models developed on the the MIMIC CXR dataset. Our best performing model was a fine-tuned BERT-to-BERT encoder-decoder with 58.75/100 ROUGE-L F1, which outperformed specialized checkpoints with more sophisticated attention mechanisms. We investigate these aspects in this work., Comment: This version reverts to the original preprint, following the advice from the Artificial Intelligence in Health editorial office. The published version is peer-reviewed and available in the journal (see external DOI). The preprint remains unchanged to maintain version transparency, as noted in the further disclosure section of the published article

This paper explores the concept of multiset dimensions (Mdim) of compressed zero-divisor graphs (CZDG) associated with rings. The authors investigate the interplay between the ring-theoretic properties of a ring $R$ and the associated compressed zero-divisor graph. An undirected graph consisting of a vertex set $ Z(R_E)\backslash\{[0]\} = R_E\backslash\{[0],[1]\}$, where $R_E=\{[x] : x\in R\} $ and $[x]=\{y\in R : \text{ann}(x)=\text{ann}(y)\}$ is called a compressed zero-divisor graph, denoted by $\Gamma_E (R)$. An edge is formed between two vertices $[x]$ and $[y]$ of $Z(R_E)$ if and only if $[x][y]=[xy]=[0]$, that is, iff $xy=0$. For a ring $R$, graph $G$ is said to be realizable as $\Gamma_E (R) $ if $G$ is isomorphic to $\Gamma_E (R)$. We classify the rings based on Mdim of their associated CZDG and obtain the bounds for the Mdim of the compressed zero-divisor graphs. We also study the Mdim of realizable graphs of rings. Moreover, some examples are provided to support our results. Lately, we have discussed the interconnection between Mdim, girth, and diameter of CZDG., Comment: arXiv admin note: substantial text overlap with arXiv:2405.04934

This article investigates multiset dimensions in zero divisor graphs (ZD-graphs) associated with rings. Through rigorous analysis, we establish general bounds for the multiset dimension (Mdim) in ZD-graphs, exploring various commutative rings including the ring Z_n of integers modulo n, Gaussian integers and quotient polynomial rings. Additionally, we examine the behavior of Mdim under algebraic operations and discuss bounds in terms of diameter and maximum degree. This study enhances our understanding of algebraic structures and their graphical representations.

The paper systematically classifies rings based on the dominant metric dimensions (Ddim) of their associated CZDG, establishing consequential bounds for the Ddim of these compressed zero-divisor graphs. The authors investigate the interplay between the ring-theoretic properties of a ring ( R ) and associated CZDG. An undirected graph consisting of vertex set ( Z(R_E)\{[0]}\ =\ R_E\{[0],[1]}), where ( R_E=\{[x]:\ x\in R\} ) and ([x]=\{y\in R:\ \text{ann}(x)=\text{ann}(y)\} ) is called a compressed zero-divisor graph, denoted by ( \Gamma_E(R) ). An edge is formed between two vertices ([x]) and ([y]) of ( Z(R_E) ) if and only if ([x][y]=[xy]=[0]), that is, iff ( xy=0 ). For a ring ( R ), graph ( G ) is said to be realizable as ( \Gamma_E(R) ) if ( G ) is isomorphic to ( \Gamma_E(R) ). Moreover, an exploration into the Ddim of realizable graphs for rings is conducted, complemented by illustrative examples reinforcing the presented results. A recent discussion within the paper elucidates the nuanced relationship between Ddim, diameter, and girth within the domain of compressed zero-divisor graphs. This research offers a comprehensive and insightful analysis at the intersection of algebraic structures and graph theory, providing valuable contributions to the current mathematical discourse., Comment: 13

We document that recent generations of elementary school teachers are significantly more effective in raising student test scores than those from earlier generations. Measuring teachers' value-added for Black and white students separately, the improvements in teaching for Black students are significantly larger than those seen for white students. The race-specific improvements in teacher quality are driven by white teachers. Analyses of mechanisms suggest that changing teachers' biases may be one potential channel. Our results suggest reason for optimism since these teacher quality differences should lead to improved student learning and a narrowing of the Black-white test score gap over time.

Two-photon interference is an interesting quantum phenomenon that is usually captured in two distinct types of experiments, namely the Hanbury-Brown- Twiss (HBT) experiment and the Hong-Ou-Mandel (HOM) experiment. While the HBT experiment was carried out much earlier in 1956, with classical light, the demonstration of the HOM effect came much later in 1987. Unlike the former, the latter has been argued to be a purely quantum effect. A generalized formulation of two-particle interference is presented here. The HOM and the HBT effects emerge as special cases in the general analysis. A realizable two-particle interference experiment, which is intermediate between the two effects, is proposed and analyzed. Thus two-particle interference is shown to be a single phenomenon with various possible implementations, including the HBT and HOM setups., Comment: 6 pages, minor changes

Fault-tolerant quantum computing relies on Quantum Error Correction, which encodes logical qubits into data and parity qubits. Error decoding is the process of translating the measured parity bits into types and locations of errors. To prevent a backlog of errors, error decoding must be performed in real-time. Minimum Weight Perfect Matching (MWPM) is an accurate decoding algorithm for surface code, and recent research has demonstrated real-time implementations of MWPM (RT-MWPM) for a distance of up to 9. Unfortunately, beyond d=9, the number of flipped parity bits in the syndrome, referred to as the Hamming weight of the syndrome, exceeds the capabilities of existing RT-MWPM decoders. In this work, our goal is to enable larger distance RT-MWPM decoders by using adaptive predecoding that converts high Hamming weight syndromes into low Hamming weight syndromes, which are accurately decoded by the RT-MWPM decoder. An effective predecoder must balance both accuracy and coverage. In this paper, we propose Promatch, a real-time adaptive predecoder that predecodes both simple and complex patterns using a locality-aware, greedy approach. Our approach ensures two crucial factors: 1) high accuracy in prematching flipped bits, ensuring that the decoding accuracy is not hampered by the predecoder, and 2) enough coverage adjusted based on the main decoder's capability given the time constraints. Promatch represents the first real-time decoding framework capable of decoding surface codes of distances 11 and 13, achieving an LER of $2.6\times 10^{-14}$ for distance 13. Moreover, we demonstrate that running Promatch concurrently with the recently proposed Astrea-G achieves LER equivalent to MWPM LER, $3.4\times10^{-15}$, for distance 13, representing the first real-time accurate decoder for up-to a distance of 13.

Far-from equilibrium molecular templating networks, like those that maintain the populations of RNA and protein molecules in the cell, are key biological motifs. These networks share the general property that assembled products are produced and degraded via complex pathways controlled by catalysts, including molecular templates. Although it has been suggested that the information propagated from templates to products sets a lower bound on the thermodynamic cost of these networks, this bound has not been explored rigorously to date. We show that, for an arbitrarily catalytic reaction network in steady state, the specificity with which a single product can dominate the ensemble is upper bounded, and the entropy of the product ensemble lower bounded, by a function of $\Delta G$, the difference between the maximal and minimal free-energy changes along pathways to assembly. These simple bounds are particularly restrictive for systems with a smaller number of possible products $M$. Remarkably, however, although $\Delta G$ constrains the information propagated to the product distribution, the systems that saturate the bound operate in a pseudo-equilibrium fashion, and there is no minimal entropy production rate for maintaining this non-equilibrium distribution. Moreover, for large systems, a vanishingly small subset of the possible products can dominate the product ensemble even for small values of $\Delta G/\ln M$., Comment: 23 pages, 12 figures

Biomedical image analysis is of paramount importance for the advancement of healthcare and medical research. Although conventional convolutional neural networks (CNNs) are frequently employed in this domain, facing limitations in capturing intricate spatial and temporal relationships at the pixel level due to their reliance on fixed-sized windows and immutable filter weights post-training. These constraints impede their ability to adapt to input fluctuations and comprehend extensive long-range contextual information. To overcome these challenges, a novel architecture based on self-attention mechanisms as an alternative to conventional CNNs.The proposed model utilizes attention-based mechanisms to surpass the limitations of CNNs. The key component of our strategy is the combination of non-overlapping (vanilla patching) and novel overlapped Shifted Patching Techniques (S.P.T.s), which enhances the model's capacity to capture local context and improves generalization. Additionally, we introduce the Lancoz5 interpolation technique, which adapts variable image sizes to higher resolutions, facilitating better analysis of high-resolution biomedical images. Our methods address critical challenges faced by attention-based vision models, including inductive bias, weight sharing, receptive field limitations, and efficient data handling. Experimental evidence shows the effectiveness of proposed model in generalizing to various biomedical imaging tasks. The attention-based model, combined with advanced data augmentation methodologies, exhibits robust modeling capabilities and superior performance compared to existing approaches. The integration of S.P.T.s significantly enhances the model's ability to capture local context, while the Lancoz5 interpolation technique ensures efficient handling of high-resolution images.

Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increases significantly as batch size shrinks because batch statistics estimates are inaccurate. As a result, computer vision tasks like detection, segmentation, and video, which require tiny batches based on memory consumption, aren't suitable for using Batch Normalization for larger model training and feature transfer. Here, we explore Group Normalization as an easy alternative to using Batch Normalization A Group Normalization is a channel normalization method in which each group is divided into different channels, and the corresponding mean and variance are calculated for each group. Group Normalization computations are accurate across a wide range of batch sizes and are independent of batch size. When trained using a large ImageNet database on ResNet-50, GN achieves a very low error rate of 10.6% compared to Batch Normalization. when a smaller batch size of only 2 is used. For usual batch sizes, the performance of G.N. is comparable to that of Batch Normalization, but at the same time, it outperforms other normalization techniques. Implementing Group Normalization as a direct alternative to B.N to combat the serious challenges faced by the Batch Normalization in deep learning models with comparable or improved classification accuracy. Additionally, Group Normalization can be naturally transferred from the pre-training to the fine-tuning phase. ., Comment: 19 pages, 3 figures

Pairs trading, a strategy that capitalizes on price movements of asset pairs driven by similar factors, has gained significant popularity among traders. Common practice involves selecting highly cointegrated pairs to form a portfolio, which often leads to the inclusion of multiple pairs sharing common assets. This approach, while intuitive, inadvertently elevates portfolio variance and diminishes risk-adjusted returns by concentrating on a small number of highly cointegrated assets. Our study introduces an innovative pair selection method employing graphical matchings designed to tackle this challenge. We model all assets and their cointegration levels with a weighted graph, where edges signify pairs and their weights indicate the extent of cointegration. A portfolio of pairs is a subgraph of this graph. We construct a portfolio which is a maximum weighted matching of this graph to select pairs which have strong cointegration while simultaneously ensuring that there are no shared assets within any pair of pairs. This approach ensures each asset is included in just one pair, leading to a significantly lower variance in the matching-based portfolio compared to a baseline approach that selects pairs purely based on cointegration. Theoretical analysis and empirical testing using data from the S\&P 500 between 2017 and 2023, affirm the efficacy of our method. Notably, our matching-based strategy showcases a marked improvement in risk-adjusted performance, evidenced by a gross Sharpe ratio of 1.23, a significant enhancement over the baseline value of 0.48 and market value of 0.59. Additionally, our approach demonstrates reduced trading costs attributable to lower turnover, alongside minimized single asset risk due to a more diversified asset base.

Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in various scenarios ranging from object manipulation to legged-robot locomotion. However, the zero-volume nature of manifolds makes the CMP problem challenging, and the state-of-the-art methods still take several seconds to find a path and require a computationally expansive path dataset for imitation learning. Recently, physics-informed motion planning methods have emerged that directly solve the Eikonal equation through neural networks for motion planning and do not require expert demonstrations for learning. Inspired by these approaches, we propose the first physics-informed CMP framework that solves the Eikonal equation on the constraint manifolds and trains neural function for CMP without expert data. Our results show that the proposed approach efficiently solves various CMP problems in both simulation and real-world, including object manipulation under orientation constraints and door opening with a high-dimensional 6-DOF robot manipulator. In these complex settings, our method exhibits high success rates and finds paths in sub-seconds, which is many times faster than the state-of-the-art CMP methods., Comment: Accepted at the IEEE International Conference on Robotics and Automation (ICRA), 2024

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ polarized muons stored in a $7.1$-m-radius storage ring with a $1.45$ T uniform magnetic field. The value of $ a_{\mu}$ is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using Nuclear Magnetic Resonance (NMR). The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_\mu = 116 592 057 (25) \times 10^{-11}$ (0.21 ppm). This is the world's most precise measurement of this quantity and represents a factor of $2.2$ improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_\mu(\text{FNAL}) = 116 592 055 (24) \times 10^{-11}$ (0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_\mu$(exp) $ = 116 592 059 (22) \times 10^{-11}$ (0.19 ppm)., Comment: 48 pages, 29 figures; 4 pages of Supplement Material; version accepted for publication in Physical Review D

In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances. This study is dedicated to a comprehensive exploration of the Whisper and MMS systems, with a focus on assessing biases in automatic speech recognition (ASR) inherent to casual conversation speech specific to the Portuguese language. Our investigation encompasses various categories, including gender, age, skin tone color, and geo-location. Alongside traditional ASR evaluation metrics such as Word Error Rate (WER), we have incorporated p-value statistical significance for gender bias analysis. Furthermore, we extensively examine the impact of data distribution and empirically show that oversampling techniques alleviate such stereotypical biases. This research represents a pioneering effort in quantifying biases in the Portuguese language context through the application of MMS and Whisper, contributing to a better understanding of ASR systems' performance in multilingual settings., Comment: EACL-2024 LT-EDI Workshop

Amidst growing food production demands, early plant disease detection is essential to safeguard crops; this study proposes a visual machine learning approach for plant disease detection, harnessing RGB and NIR data collected in real-world conditions through a JAI FS-1600D-10GE camera to build an RGBN dataset. A two-stage early plant disease detection model with YOLOv8 and a sequential CNN was used to train on a dataset with partial labels, which showed a 3.6% increase in mAP compared to a single-stage end-to-end segmentation model. The sequential CNN model achieved 90.62% validation accuracy utilising RGBN data. An average of 6.25% validation accuracy increase is found using RGBN in classification compared to RGB using ResNet15 and the sequential CNN models. Further research and dataset improvements are needed to meet food production demands., Comment: Australasian Conference on Robotics and Automation (ACRA 2023)

Rearrangement planning for object retrieval tasks from confined spaces is a challenging problem, primarily due to the lack of open space for robot motion and limited perception. Several traditional methods exist to solve object retrieval tasks, but they require overhead cameras for perception and a time-consuming exhaustive search to find a solution and often make unrealistic assumptions, such as having identical, simple geometry objects in the environment. This paper presents a neural object retrieval framework that efficiently performs rearrangement planning of unknown, arbitrary objects in confined spaces to retrieve the desired object using a given robot grasp. Our method actively senses the environment with the robot's in-hand camera. It then selects and relocates the non-target objects such that they do not block the robot path homotopy to the target object, thus also aiding an underlying path planner in quickly finding robot motion sequences. Furthermore, we demonstrate our framework in challenging scenarios, including real-world cabinet-like environments with arbitrary household objects. The results show that our framework achieves the best performance among all presented methods and is, on average, two orders of magnitude computationally faster than the best-performing baselines., Comment: Accepted in IEEE/RAS ICRA'24