Miracle-dz/UniFAR-FTUs huggingface dataset:
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals

Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A growing body of work documents how these AI systems often fail to improve services in practice. These failures can often be traced to decisions made during the early stages of AI ideation and design, such as problem formulation. However, today, we lack systematic processes to support effective, early-stage decision-making about whether and under what conditions to move forward with a proposed AI project. To understand how to scaffold such processes in real-world settings, we worked with public sector agency leaders, AI developers, frontline workers, and community advocates across four public sector agencies and three community advocacy groups in the United States. Through an iterative co-design process, we created the Situate AI Guidebook: a structured process centered around a set of deliberation questions to scaffold conversations around (1) goals and intended use or a proposed AI system, (2) societal and legal considerations, (3) data and modeling constraints, and (4) organizational governance factors. We discuss how the guidebook's design is informed by participants' challenges, needs, and desires for improved deliberation processes. We further elaborate on implications for designing responsible AI toolkits in collaboration with public sector agency stakeholders and opportunities for future work to expand upon the guidebook. This design approach can be more broadly adopted to support the co-creation of responsible AI toolkits that scaffold key decision-making processes surrounding the use of AI in the public sector and beyond.
Weak Lensing Constraints on Dark Matter-Baryon Interactions with $N$-Body Simulations and Machine Learning

We investigate the elastic scattering cross section between dark matter and protons using the DES Year 3 weak lensing data. This scattering induces a dark acoustic oscillation structure in the matter power spectra. To address non-linear effects at low redshift, we utilize principal component analysis alongside a limited set of $N$-body simulations, improving the reliability of our matter power spectrum prediction. We further perform a robust Markov Chain Monte Carlo analysis to derive the upper bounds on the DM-proton elastic scattering cross-section, assuming different velocity dependencies. Our results, presented as the first Frequentist upper limits, are compared with the ones obtained by Bayesian approach. Compared with the upper limits derived from the Planck cosmic microwave background data, our findings from DES Year 3 data exhibit improvements of up to a factor of five. In addition, we forecast the future sensitivities of the China Space Station Telescope, the upcoming capabilities of this telescope could improve the current limits by approximately one order of magnitude.
Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compression

This paper provides a comprehensive study on features and performance of different ways to incorporate neural networks into lifting-based wavelet-like transforms, within the context of fully scalable and accessible image compression. Specifically, we explore different arrangements of lifting steps, as well as various network architectures for learned lifting operators. Moreover, we examine the impact of the number of learned lifting steps, the number of channels, the number of layers and the support of kernels in each learned lifting operator. To facilitate the study, we investigate two generic training methodologies that are simultaneously appropriate to a wide variety of lifting structures considered. Experimental results ultimately suggest that retaining fixed lifting steps from the base wavelet transform is highly beneficial. Moreover, we demonstrate that employing more learned lifting steps and more layers in each learned lifting operator do not contribute strongly to the compression performance. However, benefits can be obtained by utilizing more channels in each learned lifting operator. Ultimately, the learned wavelet-like transform proposed in this paper achieves over 25% bit-rate savings compared to JPEG 2000 with compact spatial support.
This paper is included in the Proceedings of the 29th USENIX Security Symposium.

To defeat security threats such as man-in-the-middle (MITM) attacks, Bluetooth Low Energy (BLE) 4.2 and 5.x introduced a Secure Connections Only (SCO) mode, under which a BLE device can only accept secure pairing such as Passkey Entry and Numeric Comparison from an initiator, e.g., an Android mobile. However, the BLE speciﬁcation does not require the SCO mode for the initiator, and does not specify how the BLE programming framework should implement this mode. In this paper we show that the BLE programming framework of the initiator must properly handle SCO initiation, status management, error handling, and bond management; otherwise severe ﬂaws can be exploited to perform downgrade attacks, forcing the BLE pairing protocols to run in an insecure mode without user’s awareness. To validate our ﬁndings, we have tested 18 popular BLE commercial products with 5 Android phones. Our experimental results proved that MITM attacks (caused by down-grading) are possible to all these products. More importantly, due to such system ﬂaws from the BLE programming framework, all BLE apps in Android are subject to our downgrade attacks. To defend against our attacks, we have built a prototype for the SCO mode on Android 8 atop Android Open Source Project (AOSP). Finally, in addition to Android, we also ﬁnd all major OSes including iOS, macOS, Windows, and Linux do not support the SCO mode properly. We have reported the identiﬁed BLE pairing vulnerabilities to Blue-tooth Special Interest Group, Google, Apple, Texas Instruments, and Microsoft.
Decreases in liver cT1 accurately reflect histological improvement induced by therapies in MASH: a multi-centre pooled cohort analysis.

Background & Aims: Iron corrected T1 (cT1) is an MRI derived biomarker of liver disease activity. Emerging data suggest a change in cT1 of >=80 ms reflects histological improvement. We aimed to validate the association between the >=80 ms decline in cT1 and histological improvement, specifically the resolution of MASH. Methods: A retrospective analysis of study participants from three interventional clinical trials with histologically confirmed MASH (n = 150) who underwent multi-parametric MRI to measure cT1 (LiverMultiScan) and biopsies at baseline and end of study. Histological responders were defined using the four criteria: (1) a decrease in NAFLD Activity score (NAS) >= 2 with no worsening in fibrosis, (2) a decrease in fibrosis >=1 stage with no worsening in NAS, (3) both a NAS decrease >=2 and a fibrosis decrease >=1, and (4) MASH resolution with no worsening in fibrosis. Difference in the magnitude of change in cT1 between responders and non-responders was assessed. Results: Significant decreases in cT1 were observed in responders for all the histological criteria. The optimal threshold for separating responders from non-responders was 0.73 for MASH resolution (64ms-73ms for the other criteria), in close agreement with the predefined threshold of 80ms. The largest decrease was observed for those achieving MASH resolution, and was 119ms, compared to 43ms for non-responders. Those achieving an >=80 ms drop in cT1 were substantially more likely to achieve histological response with odds ratios ranging from 2.7 to 6.3. Conclusions: These results demonstrate that a drop in cT1 of >=80 ms was associated with histological response supporting the utility of cT1 to predict clinical improvement in patients undergoing therapeutic intervention.
Benefits and Limitations of Real Options Analysis for the Practice of River Flood Risk Management

Decisions on long‐lived flood risk management (FRM) investments are complex because the future is uncertain. Flexibility and robustness can be used to deal with future uncertainty. Real options analysis (ROA) provides a welfare‐economics framework to design and evaluate robust and flexible FRM strategies under risk or uncertainty. Although its potential benefits are large, ROA is hardly used in todays' FRM practice. In this paper, we investigate benefits and limitations of a ROA, by applying it to a realistic FRM case study for an entire river branch. We illustrate how ROA identifies optimal short‐term investments and values future options. We develop robust dike investment strategies and value the flexibility offered by additional room for the river measures. We benchmark the results of ROA against those of a standard cost‐benefit analysis and show ROA's potential policy implications. The ROA for a realistic case requires a high level of geographical detail, a large ensemble of scenarios, and the inclusion of stakeholders' preferences. We found several limitations of applying the ROA. It is complex. In particular, relevant sources of uncertainty need to be recognized, quantified, integrated, and discretized in scenarios, requiring subjective choices and expert judgment. Decision trees have to be generated and stakeholders' preferences have to be translated into decision rules. On basis of this study, we give general recommendations to use high discharge scenarios for the design of measures with high fixed costs and few alternatives. Lower scenarios may be used when alternatives offer future flexibility.
Differential proliferation regulates multi-tissue morphogenesis during embryonic axial extension: Integrating viscous modeling and experimental approaches

The study of how mechanical interactions and different cellular behaviors affect tissues and embryo shaping has been and remains an important challenge in biology. Axial extension is a morphogenetic process that results in the acquisition of the elongated shape of the vertebrate embryonic body. Several adjacent tissues are involved in the process, including the tissues that form the spinal cord and musculoskeletal system: the neural tube and the paraxial mesoderm, respectively. Although we have a growing understanding of how each of these tissues elongates, we still need to fully understand the morphogenetic consequences of their growth and mechanical interactions. In this study, we develop a 2D multi-tissue continuum-based mathematical model to simulate and study how differential growth, tissue biophysical properties, and mechanical interactions affect the morphogenesis of the embryonic body during axial extension. Our model captures the long-term dynamics of embryonic posterior tissues previously observed in vivo by time-lapse imaging of bird embryos. It reveals the underestimated influence of differential tissue proliferation rates in inter-tissue interaction and shaping by capturing the relative impact of this process on tissue dynamics. We verified the predictions of our model in quail embryos by showing that decreasing the rate of cell proliferation in the paraxial mesoderm affects long-term tissue dynamics and shaping of both the paraxial mesoderm and the neighboring neural tube. Overall, our work provides a new theoretical platform to consider the long-term consequences of tissue differential growth and mechanical interactions on morphogenesis.
Identification of Thioredoxin1 interacting proteins in neuronal cytoskeletal organization during autophagy

Thioredoxin1 (Trx1) is a major cytoplasmic thiol oxidoreductase protein involved in redox signaling. This function is rendered by a rapid electron transfer reaction during which Trx1 reduces its substrate and itself becomes oxidized. In this reaction, Trx1 forms a transient disulfide bond with the substrate which is unstable and therefore identification of Trx1 substrates is technically challenging. This process maintains the cellular proteins in a balanced redox state and ensures cellular homeostasis. Trx1 levels are reduced in some neurodegenerative diseases; therefore, understanding the interactions between Trx1 and its substrates in neurons could have significant therapeutic implications. We utilized a transgenic mouse model expressing a Flag-tagged mutant form of Trx1 that can form stable disulfide bonds with its substrates allowing identification of the Trx1 interacting proteins. The involvement of Trx1 has been suggested in autophagy, we aimed to investigate Trx1 substrate after pharmacologic induction of autophagy in primary hippocampal neurons. Treatment of primary neurons by rapamycin, a standard autophagy inducer, caused significant reduction of neurite outgrowth and alterations in the cytoskeleton. Through immunoprecipitation and mass spectrometry, we have identified 77 Trx1 interacting proteins which were associated with a wide range of cellular functions including a major impact on cytoskeletal organization. The results were confirmed in Trx1 knocked-down cells and in nucleofected primary neurons. Our study suggests a novel role for Trx1 in regulation of neuronal cytoskeleton organization, marking the first investigation of Trx1-interacting proteins in primary neurons and confirming the multifaceted role of Trx1 in physiological and pathological states.
Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.
A half-centre oscillator encodes sleep pressure

Oscillatory neural dynamics are an inseparable part of mammalian sleep. Characteristic rhythms are associated with different sleep stages and variable levels of sleep pressure, but it remains unclear whether these oscillations are passive mirrors or active generators of sleep. Here we report that sleep-control neurons innervating the dorsal fan-shaped body of Drosophila (dFBNs) produce slow-wave activity (SWA) in the delta frequency band (0.2–1 Hz) that is causally linked to sleep. The dFBN ensemble contains one or two rhythmic cells per hemisphere whose membrane voltages oscillate in anti-phase between hyperpolarized DOWN and depolarized UP states releasing bursts of action potentials. The oscillations rely on direct interhemispheric competition of two inhibitory half-centres connected by glutamatergic synapses. Interference with glutamate release from these synapses disrupts SWA and baseline as well as rebound sleep, while the optogenetic replay of SWA (with the help of an intersectional, dFBN-restricted driver) induces sleep. Rhythmic dFBNs generate SWA throughout the sleep–wake cycle—despite a mutually antagonistic ‘flip-flop’ arrangement with arousing dopaminergic neurons—but adjust its power to sleep need via an interplay of sleep history-dependent increases in dFBN excitability and homeostatic depression of their efferent synapses, as we demonstrate transcriptionally, structurally, functionally, and with a simple computational model. The oscillatory format permits a durable encoding of sleep pressure over long time scales but requires downstream mechanisms that convert the amplitude-modulated periodic signal into binary sleep–wake states.
Spatial control of sensory adaptation modulates mechanosensing in Pseudomonas aeruginosa

Sensory signaling pathways use adaptation to dynamically respond to changes in their environment. Here, we report the mechanism of sensory adaptation in the Pil-Chp mechanosensory system, which the important human pathogen Pseudomonas aeruginosa uses to sense mechanical stimuli during surface exploration. Using biochemistry, genetics, and cell biology, we discovered that the enzymes responsible for adaptation, a methyltransferase and a methylesterase, are segregated to opposing cell poles as P. aeruginosa explore surfaces. By coordinating the localization of both enzymes, we found that the Pil-Chp response regulators influence local receptor methylation, the molecular basis of bacterial sensory adaptation. We propose a model in which adaptation during mechanosensing spatially resets local receptor methylation, and thus Pil-Chp signaling, to modulate the pathway outputs, which are involved in P. aeruginosa virulence. Despite decades of bacterial sensory adaptation studies, our work has uncovered an unrecognized mechanism that bacteria use to achieve adaptation to sensory stimuli.
Male and female variability in response to chronic stress and morphine in C57BL/6J, DBA/2J, and their BXD progeny

Drug addiction is a multifactorial syndrome in which genetic predispositions and exposure to environmental stressors constitute major risk factors for the early onset, escalation, and relapse of addictive behaviors. While it is well known that stress plays a key role in drug addiction, the genetic factors that make certain individuals particularly sensitive to stress and thereby more vulnerable to becoming addicted are unknown. In an effort to test a complex set of gene x environment interactions—specifically gene x chronic stress—here we leveraged a systems genetics resource: BXD recombinant inbred mice (BXD5, BXD8, BXD14, BXD22, BXD29, and BXD32) and their parental mouse lines, C57BL/6J and DBA/2J. Utilizing the chronic social defeat stress (CSDS) and chronic variable stress (CVS) paradigms, we first showed sexual dimorphism in the behavioral stress response between the mouse strains. Further, we observed an interaction between genetic background and vulnerability to prolonged exposure to non-social stressors. Finally, we found that DBA/2J and C57BL/6J mice pre-exposed to stress displayed differences in morphine sensitivity. Our results support the hypothesis that genetic variation in predisposition to stress responses influences morphine sensitivity and is likely to modulate the development of drug addiction.
Optogenetic fMRI reveals therapeutic circuits of subthalamic nucleus deep brain stimulation

While deep brain stimulation (DBS) is widely employed for managing motor symptoms in Parkinson’s disease (PD), its exact circuit mechanisms remain controversial. To identify the neural targets affected by therapeutic DBS in PD, we analyzed DBS-evoked whole brain activity in female hemi-parkinsonian rats using function magnetic resonance imaging (fMRI). We delivered subthalamic nucleus (STN) DBS at various stimulation pulse repetition rates using optogenetics, allowing unbiased examinations of cell-type specific STN feed-forward neural activity. Unilateral STN optogenetic stimulation elicited pulse repetition rate-dependent alterations of blood-oxygenation-level-dependent (BOLD) signals in SNr (substantia nigra pars reticulata), GP (globus pallidus), and CPu (caudate putamen). Notably, these manipulations effectively ameliorated pathological circling behavior in animals expressing the kinetically faster Chronos opsin, but not in animals expressing ChR2. Furthermore, mediation analysis revealed that the pulse repetition rate-dependent behavioral rescue was significantly mediated by optogenetically induced activity changes in GP and CPu, but not in SNr. This suggests that the activation of GP and CPu are critically involved in the therapeutic mechanisms of STN DBS.
Single-cell approach dissecting agr quorum sensing dynamics in Staphylococcus aureus

Staphylococcus aureus both colonizes humans and causes severe virulent infections. Virulence is regulated by the agr quorum sensing system and its autoinducing peptide (AIP), with dynamics at the single-cell level across four agr-types – each defined by distinct AIP sequences and capable of cross-inhibition – remaining elusive. Employing microfluidics, time-lapse microscopy, and deep-learning image analysis, we uncovered significant differences in AIP sensitivity among agr-types. We observed bimodal agr activation, attributed to intergenerational phenotypic stability and influenced by AIP concentration. Upon AIP stimulation, agr-III showed AIP insensitivity, while agr-II exhibited increased sensitivity and prolonged generation time. Beyond expected cross-inhibition of agr-I by heterologous AIP-II and -III, the presumably cross-activating AIP-IV also inhibited agr-I. Community interactions across different agr-type pairings revealed four main patterns: stable or switched dominance, and delayed or stable dual activation, influenced by community characteristics. These insights underscore the potential of personalized treatment strategies considering virulence and genetic diversity.
Learning Rate Free Bayesian Inference in Constrained Domains

We introduce a suite of new particle-based algorithms for sampling on constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a unifying framework for several existing constrained sampling algorithms, including mirrored Langevin dynamics and mirrored Stein variational gradient descent. We demonstrate the performance of our algorithms on a range of numerical examples, including sampling from targets on the simplex, sampling with fairness constraints, and constrained sampling problems in post-selection inference. Our results indicate that our algorithms achieve competitive performance with existing constrained sampling methods, without the need to tune any hyperparameters.
Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins

Endorheic basins around the world are suffering from water and ecosystem crisis. To pursue sustainable development, quantifying the hydrological cycle is fundamentally important. However, knowledge gaps exist in how climate change and human activities influence the hydrological cycle in endorheic basins. We used an integrated ecohydrological model, in combination with systematic observations, to analyze the hydrological cycle in the Heihe River Basin, a typical endorheic basin in arid region of China. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012. The results showed that climate warming, which has led to greater precipitation, snowmelt, glacier melt, and runoff, is a favorable factor in alleviating water scarcity. Human activities, including ecological water diversion, cropland expansion, and groundwater overexploitation, have both positive and negative effects. The natural oasis ecosystem has been restored considerably, but the overuse of water in midstream and the use of environmental flow for agriculture in downstream have exacerbated the water stress, resulting in unfavorable changes in surface‐ground water interactions and raising concerns regarding how to fairly allocate water resources. Our results suggest that the water resource management in the region should be adjusted to adapt to a changing hydrological cycle, cropland area must be reduced, and the abstraction of groundwater must be controlled. To foster long‐term benefits, water conflicts should be handled from a broad socioeconomic perspective. The findings can provide useful information on endorheic basins to policy makers and stakeholders around the world.