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The Equity Playbook Initiative Implementation in Kentucky underwent an efficacy study conducted by LXD Research during the 2022-2023 school year. The study aimed to assess the initiative's impact on student academic achievement and school climate. A total of 55 public schools participating in the Equity Playbook Initiative were compared with 240 statistically similar non-participating schools. The demographic breakdown of the sample included 65% economically disadvantaged students, with a significant representation of 30% African American students and 35% non-white students. The findings indicated that Equity Playbook schools significantly improved key academic outcomes, particularly among economically disadvantaged students. Notably, students attending Equity Playbook schools, especially those with a high percentage of economically disadvantaged students, showed significant improvement in testing on grade level in Science and Math compared to their counterparts in non-participating schools. Additionally, the study revealed a reduction in the Kentucky Summative Assessment (KSA) achievement gap for economically disadvantaged students among participating schools. Furthermore, the completion rates of advanced courses for African American and non-white students in Equity Playbook schools showed marked improvement compared to the schools that did not participate in the initiative. Qualitative interviews with state, district, and school-level educators and staff also provided positive feedback regarding the effects of the Equity Playbook Initiative. The findings suggest that the Equity Playbook Initiative holds promise in promoting student academic achievement and enhancing school climate. This study encourages further evaluation and broader adoption of the Equity Playbook Initiative in various districts and schools across Kentucky to leverage its potential benefits. [This report was prepared for engage2learn by LXD Research.]

LXD Research conducted a study to examine the relationship between investment in Zaner-Bloser Handwriting program and Grade 3 student proficiency in handwriting in Alabama. Data from 4,759 students across 54 schools were analyzed, with schools categorized into Low Zaner-Bloser Handwriting (>$1 and [less than or equal to]$5 per student per year), Moderate Zaner-Bloser Handwriting (>$5 and [less than or equal to]$10 per student per year), and High Zaner-Bloser Handwriting (>$10 per student per year) investment groups. The demographic profile of the sample included a diverse range of students, notably with a significant representation of low-income and minority groups. The study revealed a substantial positive correlation between the level of ZB-HW investment and handwriting proficiency. Notably, 91% of students in the High ZB-HW investment group achieved proficiency, compared to 82% in the Moderate group and 69% in the Low investment group. Additionally, each $1 increase in per-student investment was associated with an 18% increase in the likelihood of achieving proficiency. These findings highlight the effectiveness of Zaner-Bloser Handwriting in enhancing handwriting skills and underscore the importance of targeted investment in educational resources to support student outcomes. The findings and rigorous study design support Zaner-Bloser Handwriting as a comprehensive program that meets the criteria for ESSA Level 3. [This report was produced by Zaner-Bloser.]

Social media has a misinformation problem, and counterspeech -- fighting bad speech with more speech -- has been an ineffective solution. Here, we argue that bridging-based ranking -- an algorithmic approach to promoting content favored by users of diverse viewpoints -- is a promising approach to helping counterspeech combat misinformation. By identifying counterspeech that is favored both by users who are inclined to agree and by users who are inclined to disagree with a piece of misinformation, bridging promotes counterspeech that persuades the users most likely to believe the misinformation. Furthermore, this algorithmic approach leverages crowd-sourced votes, shifting discretion from platforms back to users and enabling counterspeech at the speed and scale required to combat misinformation online. Bridging is respectful of users' autonomy and encourages broad participation in healthy exchanges; it offers a way for the free speech tradition to persist in modern speech environments.

The perennial demand for highly efficient short-haul communications is evidenced by a sustained explosion of growth in data center infrastructure that is predicted to continue for the foreseeable future. In these relatively compact networks, cost-sensitivity is of particular importance, which limits options to direct detection schemes that are more cost efficient than their coherent counterparts. Since their initial demonstration, multi-soliton states in optical microresonators have been observed to manifest in self-organised ensembles where soliton pulses are equally spaced around the resonators. In the spectral domain, these states, dubbed soliton crystals (SCs), result in significant enhancements to individual comb lines depending on the crystal state, making them well suited towards intensity-modulated direct detection (IMDD) schemes. In this work, we experimentally demonstrate adiabatic, deterministic access to lower-order soliton crystal states using an auxiliary-assisted cavity pumping method, attaining up to 19.6 dB enhancement of the comb lines in the 7-SC configuration compared to the single-soliton state. Seven comb lines of each 46 Gbaud/s pulse amplitude modulation 4 (PAM4) is transmitted over 4km of fiber in comb lines across the C-band with bit-error-rates (BER) as low as 5E-5. Our demonstration shows the promising way of using soliton crystal states as future integrated sources for highly stable Terabaud/s datacenter communications.

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Recent work has initiated the study of dense graph processing using graph sketching methods, which drastically reduce space costs by lossily compressing information about the input graph. In this paper, we explore the strange and surprising performance landscape of sketching algorithms. We highlight both their surprising advantages for processing dense graphs that were previously prohibitively expensive to study, as well as the current limitations of the technique. Most notably, we show how sketching can avoid bottlenecks that limit conventional graph processing methods. Single-machine streaming graph processing systems are typically bottlenecked by CPU performance, and distributed graph processing systems are typically bottlenecked by network latency. We present Landscape, a distributed graph-stream processing system that uses linear sketching to distribute the CPU work of computing graph properties to distributed workers with no need for worker-to-worker communication. As a result, it overcomes the CPU and network bottlenecks that limit other systems. In fact, for the connected components problem, Landscape achieves a stream ingestion rate one-fourth that of maximum sustained RAM bandwidth, and is four times faster than random access RAM bandwidth. Additionally, we prove that for any sequence of graph updates and queries Landscape consumes at most a constant factor more network bandwidth than is required to receive the input stream. We show that this system can ingest up to 332 million stream updates per second on a graph with $2^{17}$ vertices. We show that it scales well with more distributed compute power: given a cluster of 40 distributed worker machines, it can ingest updates 35 times as fast as with 1 distributed worker machine. Landscape uses heuristics to reduce its query latency by up to four orders of magnitude over the prior state of the art.

In the realm of mental health support chatbots, it is vital to show empathy and encourage self-exploration to provide tailored solutions. However, current approaches tend to provide general insights or solutions without fully understanding the help-seeker's situation. Therefore, we propose PsyMix, a chatbot that integrates the analyses of the seeker's state from the perspective of a psychotherapy approach (Chain-of-Psychotherapies, CoP) before generating the response, and learns to incorporate the strength of various psychotherapies by fine-tuning on a mixture of CoPs. Through comprehensive evaluation, we found that PsyMix can outperform the ChatGPT baseline, and demonstrate a comparable level of empathy in its responses to that of human counselors., Comment: 13pages, 5 figures

We investigate the theory of induction in the setting of doubles of coideal $*$-subalgebras of compact quantum group Hopf $*$-algebras. We then exemplify parts of this theory in the particular case of quantum $SL(2,\mathbb{R})$, and compute the decomposition of the regular representation for quantum $SL(2,\mathbb{R})$ into irreducibles., Comment: 27 pages

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the true underlying treatment effect varies by exposure time can produce severely misleading estimates. Alternatively, the true underlying treatment effect might vary by calendar time. Comparatively less work has examined treatment effect structure misspecification in this setting. Here, we evaluate the behavior of the mixed effects model-based immediate treatment effect, exposure time-averaged treatment effect, and calendar time-averaged treatment effect estimators in different scenarios where they are misspecified for the true underlying treatment effect structure. We prove that the immediate treatment effect estimator can be relatively robust to bias when estimating a true underlying calendar time-averaged treatment effect estimand. However, when there is a true underlying calendar (exposure) time-varying treatment effect, misspecifying an analysis with an exposure (calendar) time-averaged treatment effect estimator can yield severely misleading estimates and even converge to a value of the opposite sign of the true calendar (exposure) time-averaged treatment effect estimand. Researchers should carefully consider how the treatment effect may vary as a function of exposure time and/or calendar time in the analysis of SW-CRTs., Comment: 49 pages (29 main, 20 appendix), 18 figures (10 main, 8 appendix)

Bulk AlN possesses high thermal conductivity due to long phonon mean-free-paths, high group velocity, and long lifetimes. However, the thermal transport scenario becomes very different in a thin AlN film due to phonon-defect and phonon-boundary scattering. Herein, we report experimental observation of ballistic to diffusive transition in a series of AlN thin films (1.6 - 2440 nm) grown on sapphire substrates. The ballistic transport is characterized by constant thermal resistance as a function of film thickness due to phonon scattering by defects and boundaries. In this transport regime, phonons possess very small group velocities and lifetimes. The lifetime of the optical phonons increases by more than an order of magnitude in the diffusive regime, however, remains nearly constant afterwards. Our study is important for understanding the details of nano and microscale thermal transport in a highly conductive material.

Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.

Autism Spectrum Disorder (ASD) significantly affects the social and communication abilities of children, and eye-tracking is commonly used as a diagnostic tool by identifying associated atypical gaze patterns. Traditional methods demand manual identification of Areas of Interest in gaze patterns, lowering the performance of gaze behavior analysis in ASD subjects. To tackle this limitation, we propose a novel method to automatically analyze gaze behaviors in ASD children with superior accuracy. To be specific, we first apply and optimize seven clustering algorithms to automatically group gaze points to compare ASD subjects with typically developing peers. Subsequently, we extract 63 significant features to fully describe the patterns. These features can describe correlations between ASD diagnosis and gaze patterns. Lastly, using these features as prior knowledge, we train multiple predictive machine learning models to predict and diagnose ASD based on their gaze behaviors. To evaluate our method, we apply our method to three ASD datasets. The experimental and visualization results demonstrate the improvements of clustering algorithms in the analysis of unique gaze patterns in ASD children. Additionally, these predictive machine learning models achieved state-of-the-art prediction performance ($81\%$ AUC) in the field of automatically constructed gaze point features for ASD diagnosis. Our code is available at \url{https://github.com/username/projectname}.

The emergence of large language models (LLMs) has significantly impacted various fields, from natural language processing to sectors like medicine and finance. However, despite their rapid proliferation, the applications of LLMs in telecommunications remain limited, often relying on general-purpose models that lack domain-specific specialization. This lack of specialization results in underperformance, particularly when dealing with telecommunications-specific technical terminology and their associated mathematical representations. This paper addresses this gap by first creating and disseminating Tele-Data, a comprehensive dataset of telecommunications material curated from relevant sources, and Tele-Eval, a large-scale question-and-answer dataset tailored to the domain. Through extensive experiments, we explore the most effective training techniques for adapting LLMs to the telecommunications domain, ranging from examining the division of expertise across various telecommunications aspects to employing parameter-efficient techniques. We also investigate how models of different sizes behave during adaptation and analyze the impact of their training data on this behavior. Leveraging these findings, we develop and open-source Tele-LLMs, the first series of language models ranging from 1B to 8B parameters, specifically tailored for telecommunications. Our evaluations demonstrate that these models outperform their general-purpose counterparts on Tele-Eval while retaining their previously acquired capabilities, thus avoiding the catastrophic forgetting phenomenon.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have the potential to cause real-world impact. Policymakers, model providers, and other researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such agents to help mitigate cyberrisk and investigate opportunities for penetration testing. Toward that end, we introduce Cybench, a framework for specifying cybersecurity tasks and evaluating agents on those tasks. We include 40 professional-level Capture the Flag (CTF) tasks from 4 distinct CTF competitions, chosen to be recent, meaningful, and spanning a wide range of difficulties. Each task includes its own description, starter files, and is initialized in an environment where an agent can execute bash commands and observe outputs. Since many tasks are beyond the capabilities of existing LM agents, we introduce subtasks for each task, which break down a task into intermediary steps for a more detailed evaluation. To evaluate agent capabilities, we construct a cybersecurity agent and evaluate 8 models: GPT-4o, OpenAI o1-preview, Claude 3 Opus, Claude 3.5 Sonnet, Mixtral 8x22b Instruct, Gemini 1.5 Pro, Llama 3 70B Chat, and Llama 3.1 405B Instruct. Without subtask guidance, agents leveraging Claude 3.5 Sonnet, GPT-4o, OpenAI o1-preview, and Claude 3 Opus successfully solved complete tasks that took human teams up to 11 minutes to solve. In comparison, the most difficult task took human teams 24 hours and 54 minutes to solve. All code and data are publicly available at https://cybench.github.io, Comment: 78 pages, 6 figures

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

Methods are lacking to handle the problem of survival analysis in the presence of an interval-censored covariate, specifically the case in which the conditional hazard of the primary event of interest depends on the occurrence of a secondary event, the observation time of which is subject to interval censoring. We propose and study a flexible class of discrete-time parametric survival models that handle the censoring problem through joint modeling of the interval-censored secondary event, the outcome, and the censoring mechanism. We apply this model to the research question that motivated the methodology, estimating the effect of HIV status on all-cause mortality in a prospective cohort study in South Africa.

In this paper, we study the problem of distributed transmission control and video streaming optimization for unmanned aerial vehicles (UAVs) operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground/aerial nodes at the physical (PHY) layer, (ii) limited-size queues with delay-constrained packet arrival at the medium access control (MAC) layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the throughput by optimizing the fading threshold. To this end, we jointly analyze the queue-related packet loss probabilities (i.e., buffer overflow and time threshold event) as well as the outage probability due to the low signal-to-interference-plus-noise ratio (SINR). We introduce the Distributed Transmission Control (DTC) algorithm that maximizes the throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed peak signal-to-noise ratio (PSNR) optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the Joint Distributed Video Transmission and Encoder Control (JDVT-EC) algorithm that enhances the PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios., Comment: Submitted to IEEE Transactions on Communications

The SKA Observatory, currently in the construction phase, will have two of the world's largest radio telescopes when completed in 2028. The scale of the project introduces unique challenges for the telescope software design and implementation at all levels, from user interfacing software down to the lower-level control of individual telescope elements. The Observation Execution Tool (OET) is part of the Observation Science Operations (OSO) suite of applications and is responsible for orchestrating the highest level of telescope control through the execution of telescope control scripts. One of the main challenges for the OET is creating a design that can robustly run concurrent observations on multiple subarrays while remaining responsive to the user. The Scaled Agile Framework (SAFe) development process followed by the SKA project also means the software should be allow to iterative implementation and easily accommodate new and changing requirements. This paper concentrates on the design decisions and challenges in the development of the OET, how we have solved some of the specific technical problems and details on how we remain flexible for future requirements., Comment: 6 pages, 2 figures, submitted to 2024 SPIE Astronomical Telescopes + Instrumentation, Software and Cyberinfrastructure for Astronomy VIII conference

The Observatory Science Operations (OSO) subsystem of the SKAO consists of a range of complex tools which will be used to propose, design, schedule and execute observations. Bridging the gap between the science and telescope domains is the key responsibility of OSO, requiring considerations of usability, performance, availability and accessibility, amongst others. This paper describes the state of the observatory software as we approach construction milestones, how the applications meet these requirements using a modern technology architecture, and challenges so far.

Political actors frequently manipulate redistricting plans to gain electoral advantages, a process commonly known as gerrymandering. To address this problem, several states have implemented institutional reforms including the establishment of map-drawing commissions. It is difficult to assess the impact of such reforms because each state structures bundles of complex rules in different ways. We propose to model redistricting processes as a sequential game. The equilibrium solution to the game summarizes multi-step institutional interactions as a single dimensional score. This score measures the leeway political actors have over the partisan lean of the final plan. Using a differences-in-differences design, we demonstrate that reforms reduce partisan bias and increase competitiveness when they constrain partisan actors. We perform a counterfactual policy analysis to estimate the partisan effects of enacting recent institutional reforms nationwide. We find that instituting redistricting commissions generally reduces the current Republican advantage, but Michigan-style reforms would yield a much greater pro-Democratic effect than types of redistricting commissions adopted in Ohio and New York., Comment: 20 pages, 8 figures, 1 table, plus references and appendices

We study new identities related to the sums of adjacent terms in the Pell sequence, defined by $P_{n} := 2P_{n-1}+P_{n-2}$ for $ n\geq 2$ and $P_{0}=0, P_{1}=1$, and generalize these identities for many similar sequences. We prove that the sum of $N>1$ consecutive Pell numbers is a fixed integer multiple of another Pell number if and only if $4\mid N$. We consider the generalized Pell $(k,i)$-numbers defined by $p(n) :=\ 2p(n-1)+p(n-k-1) $ for $n\geq k+1$, with $p(0)=p(1)=\cdots =p(i)=0$ and $p(i+1)=\cdots = p(k)=1$ for $0\leq i\leq k-1$, and prove that the sum of $N=2k+2$ consecutive terms is a fixed integer multiple of another term in the sequence. We also prove that for the generalized Pell $(k,k-1)$-numbers such a relation does not exist when $N$ and $k$ are odd. We give analogous results for the Fibonacci and other related second-order recursive sequences., Comment: 33 Pages. Comments welcome!

Epidermal skin sensors have emerged as a promising approach for continuous and noninvasive monitoring of vital health signals, but to maximize their performance, these sensors must integrate seamlessly with the skin, minimizing impedance while maintaining the skin's natural protective and regulatory functions.In this study, we introduce an imperceptible sweat sensor that achieves this seamless skin integration through interpenetrating networks formed by a porous, ultra-thin, ultra-high molecular weight polyethylene (UHMWPE) nanomembrane. Upon attachment to the skin by van der Waals force, the amphiphilic sweat extrudates infuse into the interconnected nanopores inside the hydrophobic UHWMPE nanomembrane, forming "pseudo skin" nanochannels for continuous sweat perspiration. This integration is further enhanced by the osmotic pressure generated during water evaporation. Leveraging the efficient transport of biomarkers through the "skin" channels within the porous membrane, we developed an organic electrochemical transducer (OECT) cortisol sensor via in-situ synthesis of a molecularly imprinted polymer (MIP) and poly(3,4 ethylenedioxythiophene) (PEDOT) within the nanomembrane. This demonstrates the capability to detect cortisol concentrations from 0.05 to 0.5 {\mu}M for seamless monitoring of stress levels. This work represents a significant advancement in self-adhesive sweat sensors that offer imperceptible and real-time non-invasive health monitoring capabilities.

For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates tailored statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution., Comment: The first two authors contributed equally to this work. Main text: 20 pages, 1 figure, 4 tables. Supplement: 14 pages, 8 figures, 0 tables

We continue our study of the representations of the Reflection Equation Algebra (=REA) on Hilbert spaces, focusing again on the REA constructed from the $R$-matrix associated to the standard $q$-deformation of $GL(N,\mathbb{C})$ for $0

Radios are essential for the operations of modern police departments, and they function as both a collaborative communication technology and a sociotechnical system. However, little prior research has examined their usage or their connections to individual privacy and the role of race in policing, two growing topics of concern in the US. As a case study, we examine the Chicago Police Department's (CPD's) use of broadcast police communications (BPC) to coordinate the activity of law enforcement officers (LEOs) in the city. From a recently assembled archive of 80,775 hours of BPC associated with CPD operations, we analyze text transcripts of radio transmissions broadcast 9:00 AM to 5:00 PM on August 10th, 2018 in one majority Black, one majority white, and one majority Hispanic area of the city (24 hours of audio) to explore three research questions: (1) Do BPC reflect reported racial disparities in policing? (2) How and when is gender, race/ethnicity, and age mentioned in BPC? (3) To what extent do BPC include sensitive information, and who is put at most risk by this practice? (4) To what extent can large language models (LLMs) heighten this risk? We explore the vocabulary and speech acts used by police in BPC, comparing mentions of personal characteristics to local demographics, the personal information shared over BPC, and the privacy concerns that it poses. Analysis indicates (a) policing professionals in the city of Chicago exhibit disproportionate attention to Black members of the public regardless of context, (b) sociodemographic characteristics like gender, race/ethnicity, and age are primarily mentioned in BPC about event information, and (c) disproportionate attention introduces disproportionate privacy risks for Black members of the public., Comment: Accepted in the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW '24)

Detecting hate speech and offensive language is essential for maintaining a safe and respectful digital environment. This study examines the limitations of state-of-the-art large language models (LLMs) in identifying offensive content within systematically perturbed data, with a focus on Chinese, a language particularly susceptible to such perturbations. We introduce \textsf{ToxiCloakCN}, an enhanced dataset derived from ToxiCN, augmented with homophonic substitutions and emoji transformations, to test the robustness of LLMs against these cloaking perturbations. Our findings reveal that existing models significantly underperform in detecting offensive content when these perturbations are applied. We provide an in-depth analysis of how different types of offensive content are affected by these perturbations and explore the alignment between human and model explanations of offensiveness. Our work highlights the urgent need for more advanced techniques in offensive language detection to combat the evolving tactics used to evade detection mechanisms., Comment: 10 pages,5 Tables, 2 Figures

Providing timely support and intervention is crucial in mental health settings. As the need to engage youth comfortable with texting increases, mental health providers are exploring and adopting text-based media such as chatbots, community-based forums, online therapies with licensed professionals, and helplines operated by trained responders. To support these text-based media for mental health--particularly for crisis care--we are developing a system to perform passive emotion-sensing using a combination of keystroke dynamics and sentiment analysis. Our early studies of this system posit that the analysis of short text messages and keyboard typing patterns can provide emotion information that may be used to support both clients and responders. We use our preliminary findings to discuss the way forward for applying AI to support mental health providers in providing better care., Comment: 5 pages, 1 figure, to be published in DIS Companion '24

Large scale neural models show impressive performance across a wide array of linguistic tasks. Despite this they remain, largely, black-boxes - inducing vector-representations of their input that prove difficult to interpret. This limits our ability to understand what they learn, and when the learn it, or describe what kinds of representations generalise well out of distribution. To address this we introduce a novel approach to interpretability that looks at the mapping a model learns from sentences to representations as a kind of language in its own right. In doing so we introduce a set of information-theoretic measures that quantify how structured a model's representations are with respect to its input, and when during training that structure arises. Our measures are fast to compute, grounded in linguistic theory, and can predict which models will generalise best based on their representations. We use these measures to describe two distinct phases of training a transformer: an initial phase of in-distribution learning which reduces task loss, then a second stage where representations becoming robust to noise. Generalisation performance begins to increase during this second phase, drawing a link between generalisation and robustness to noise. Finally we look at how model size affects the structure of the representational space, showing that larger models ultimately compress their representations more than their smaller counterparts., Comment: 6 pages, 3 Figures

We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predicting a local edit operation that could be applied to the input program to improve its similarity to the target. In order to apply this scheme for domains that lack program annotations, we develop a self-supervised learning approach that integrates this edit network into a bootstrapped finetuning loop along with a network that predicts entire programs in one-shot. Our joint finetuning scheme, when coupled with an inference procedure that initializes a population from the one-shot model and evolves members of this population with the edit network, helps to infer more accurate visual programs. Over multiple domains, we experimentally compare our method against the alternative of using only the one-shot model, and find that even under equal search-time budgets, our editing-based paradigm provides significant advantages.

In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users (SUs) to opportunistically utilize detected "spectrum holes". Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and training a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose a novel architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. Finally, in the spectrum scheduling stage, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users. To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station~(BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices., Comment: This is a preprint version submitted to IEEE Transactions on Machine learning in Communications and Networking. arXiv admin note: text overlap with arXiv:2308.05036

The ability to edit 3D assets from natural language presents a compelling paradigm to aid in the democratization of 3D content creation. However, while natural language is often effective at communicating general intent, it is poorly suited for specifying precise manipulation. To address this gap, we introduce ParSEL, a system that enables controllable editing of high-quality 3D assets from natural language. Given a segmented 3D mesh and an editing request, ParSEL produces a parameterized editing program. Adjusting the program parameters allows users to explore shape variations with a precise control over the magnitudes of edits. To infer editing programs which align with an input edit request, we leverage the abilities of large-language models (LLMs). However, while we find that LLMs excel at identifying initial edit operations, they often fail to infer complete editing programs, and produce outputs that violate shape semantics. To overcome this issue, we introduce Analytical Edit Propagation (AEP), an algorithm which extends a seed edit with additional operations until a complete editing program has been formed. Unlike prior methods, AEP searches for analytical editing operations compatible with a range of possible user edits through the integration of computer algebra systems for geometric analysis. Experimentally we demonstrate ParSEL's effectiveness in enabling controllable editing of 3D objects through natural language requests over alternative system designs.

The divide between weak and strong lensing is of course artificial, in that both regimes are manifestations of the same physical phenomenon: gravity bending the path of light. Nevertheless, these two regimes have to a large extent been treated separately, with different methods. This review traces the development of methods combining weak-lensing and strong-lensing data for joint lens-mass reconstruction, with a particular emphasis on cluster lenses, where both effects occur. We conclude that so-called inverse methods have been successful in merging the two regimes insofar data analysis is concerned. However, a number of improvements seem to be needed. First, not many studies include weak lensing data beyond shear. In light of the unprecedented quality of the data of JWST and future surveys, this is a clear point of improvement. Especially so, since flexion terms have proven useful in determining sub-structures. Second, considering the amount of data available, and the complexity of non-parametric lenses, automating the processes of lens-mass reconstruction would be beneficial. Towards this end, invoking machine learning seems like a promising way forward. The silence of the literature on this latter point is in fact somewhat surprising., Comment: 14 pages, 2 figures

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

The magnetic fields that emerge from beneath the solar surface and permeate the solar atmosphere are the key drivers of space weather and, thus, understanding them is important to human society. Direct observations, used to measure magnetic fields, can only probe the magnetic fields in the photosphere and above, far from the regions the magnetic fields are being enhanced by the solar dynamo. Solar gamma rays produced by cosmic rays interacting with the solar atmosphere have been detected from GeV to TeV energy range, and revealed that they are significantly affected by solar magnetic fields. However, much of the observations are yet to be explained by a physical model. Using a semi-analytic model, we show that magnetic fields at and below the photosphere with a large horizontal component could explain the $\sim$1 TeV solar gamma rays observed by HAWC. This could allow high-energy solar gamma rays to be a novel probe for magnetic fields below the photosphere., Comment: 8 pages, 5 figures, comments are welcome

Gravitational lensing occurs as the path of light from distant celestial bodies is distorted due to gravitational attraction by other celestial bodies, whose mass is partly invisible, being so-called dark matter. When observed through a gravitational lens, distant galaxies appear distorted. A lot of research activity goes into mapping the dark matter in the universe through graviational lensing. However, the mathematical models are complicated, and calculations both time consuming and tideous, if manual. In this paper we discuss how we may combine the Roulette formalism due to Clarkson with machine learning for automatic, local estimation of the lense potential in strong lenses. We also present a framework of open source software for generating datasets and validating results. -- Gravitasjonslinsing er fenomenet der ljos fr{\aa} fjerne himmellegeme vert avb{\o}ygd av tyngdekraften fr{\aa} andre himmellegeme, som ofte ikkje er fullt synlege fordi mykje av massen er m{\o}rk materie. Observert gjennom ei gravitasjonslinse, framst{\aa}r fjerne galaksar som forvrengde. Der er mykje forskingsaktivitet som freistar {\aa} karleggja m{\o}rk materie ved {\aa} studera linseeffektar, men dei matematiske modellane er kompliserte og utrekningane krev i dag mykje manuelt arbeide som er sv{\ae}rt tidkrevjande. I denne artikkelen dr{\o}ftar me korleis me kan kombinera rouletteformalismen {\aa}t Chris Clarkson med maskinl{\ae}ring for automatisk, lokal estimering av linsepotentialet i sterke linser, og me presenterer eit rammeverk med programvare i open kjeldekode for {\aa} generera datasett og validera resultat., Comment: 14 pages, in Norwegian language, 6 figures

Semiconductor heterojunctions have gained significant attention for efficient optoelectronic devices owing to their unique interfaces and synergistic effects. Interaction between charge carriers with the heterojunction plays a crucial role in determining device performance, while its spatial-temporal mapping remains lacking. In this study, we employ scanning ultrafast electron microscopy (SUEM), an emerging technique that combines high spatial-temporal resolution and surface sensitivity, to investigate photocarrier dynamics across a Si/Ge heterojunction. Charge dynamics are selectively examined across the junction and compared to far bulk areas, through which the impact of the built-in potential, band offsets, and surface effects is directly visualized. In particular, we find that the heterojunction drastically modifies the hot photocarrier diffusivities by up to 300%. These findings are further elucidated with insights from the band structure and surface potential measured by complementary techniques. This work demonstrates the tremendous effect of heterointerfaces on charge dynamics and showcases the potential of SUEM in characterizing realistic devices.

As Artificial Intelligence ecosystems become increasingly entangled within our everyday lives, designing systems that are ethical, inclusive and socially just is more vital than ever. It is well known that AI can algorithmic biases that reflect, extend and exacerbate our existing systemic injustices. Yet, despite most teenagers interacting with AI daily, only few have the opportunity to learn how it works and its socio-technical complexities. This is a particularly salient issue for marginalized communities. BIPOC teens are often misrepresented throughout AI development and implementation, but they are also less likely to receive STEM education.In response to these unprecedented socio-technical challenges and calls for more critical approaches to child-centered AI design and education, we explore how we can leverage co-speculative design practices to help scaffold BIPOC youth (ages 14-17) critiques of existing AI systems and support the re-imagining of more just AI futures. Drawing on Harway's Situated Knowledges and Speculative Fabulations, these workshops highlight the unique ways marginalized youth perceive AI as having social and ethical implications and how they envision alternative worlds with AI. Our case study describes three 2 hour sessions of a larger 8 week black-led AI STEM program. Analysis includes, data from pre-post surveys, workshop recordings, focus group discussions, learning artifacts, and field notes. We contribute 1) a discussion of how youth perceive AI as having social and ethical implications, 2) a nuanced understanding of how speculative approaches can be leveraged to support youth engagement with complex socio-technical issues and 3) enable youth to open up new AI possibilities in a world absent of techno-capitalist values., Comment: This paper has been peer-reviewed and presented at the CHI 2024 Workshop on Child-centred AI Design, May 11, 2024, Honolulu, HI, USA

Temporal credit assignment in reinforcement learning is challenging due to delayed and stochastic outcomes. Monte Carlo targets can bridge long delays between action and consequence but lead to high-variance targets due to stochasticity. Temporal difference (TD) learning uses bootstrapping to overcome variance but introduces a bias that can only be corrected through many iterations. TD($\lambda$) provides a mechanism to navigate this bias-variance tradeoff smoothly. Appropriately selecting $\lambda$ can significantly improve performance. Here, we propose Chunked-TD, which uses predicted probabilities of transitions from a model for computing $\lambda$-return targets. Unlike other model-based solutions to credit assignment, Chunked-TD is less vulnerable to model inaccuracies. Our approach is motivated by the principle of history compression and 'chunks' trajectories for conventional TD learning. Chunking with learned world models compresses near-deterministic regions of the environment-policy interaction to speed up credit assignment while still bootstrapping when necessary. We propose algorithms that can be implemented online and show that they solve some problems much faster than conventional TD($\lambda$)., Comment: ICML 2024 version

To address the limitations of current hate speech detection models, we introduce \textsf{SGHateCheck}, a novel framework designed for the linguistic and cultural context of Singapore and Southeast Asia. It extends the functional testing approach of HateCheck and MHC, employing large language models for translation and paraphrasing into Singapore's main languages, and refining these with native annotators. \textsf{SGHateCheck} reveals critical flaws in state-of-the-art models, highlighting their inadequacy in sensitive content moderation. This work aims to foster the development of more effective hate speech detection tools for diverse linguistic environments, particularly for Singapore and Southeast Asia contexts.