Miracle-dz/UniFAR-FTUs huggingface dataset:
The two-way knowledge interaction interface between humans and neural networks

Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the"language"for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN.
Blockchain-based Decentralized Time Lock Machines: Automated Reveal of Time-sensitive Information

Conditional Information Reveal (CIR) automates the release of information upon meeting specific pre-defined conditions, such as time or location. This paper advances the understanding and implementation of CIR by introducing a new paradigm to highlight the security challenges in CIR design, and proposes a decentralized architecture as a design guideline for secure CIR systems. Furthermore, in the context of time-sensitive data sharing, this paper proposes a practical timed-release cryptography system employing the proposed architecture and a novel verifiable secret sharing scheme. Key achievements of this study include the creation of an open-source prototype for practical deployment and a comprehensive system evaluation that highlights the enhanced security and efficiency of the proposed system. Furthermore, the paper delves into the application of this system in E-voting scenarios, illustrating its capacity to secure and ensure fair electronic voting processes.
YOIO: You Only Iterate Once by mining and fusing multiple necessary global information in the optical flow estimation

Occlusions pose a significant challenge to optical flow algorithms that even rely on global evidences. We consider an occluded point to be one that is imaged in the reference frame but not in the next. Estimating the motion of these points is extremely difficult, particularly in the two-frame setting. Previous work only used the current frame as the only input, which could not guarantee providing correct global reference information for occluded points, and had problems such as long calculation time and poor accuracy in predicting optical flow at occluded points. To enable both high accuracy and efficiency, We fully mine and utilize the spatiotemporal information provided by the frame pair, design a loopback judgment algorithm to ensure that correct global reference information is obtained, mine multiple necessary global information, and design an efficient refinement module that fuses these global information. Specifically, we propose a YOIO framework, which consists of three main components: an initial flow estimator, a multiple global information extraction module, and a unified refinement module. We demonstrate that optical flow estimates in the occluded regions can be significantly improved in only one iteration without damaging the performance in non-occluded regions. Compared with GMA, the optical flow prediction accuracy of this method in the occluded area is improved by more than 10%, and the occ_out area exceeds 15%, while the calculation time is 27% shorter. This approach, running up to 18.9fps with 436*1024 image resolution, obtains new state-of-the-art results on the challenging Sintel dataset among all published and unpublished approaches that can run in real-time, suggesting a new paradigm for accurate and efficient optical flow estimation.
Quantum Probability Theoretic Asset Return Modeling: A Novel Schrödinger-Like Trading Equation and Multimodal Distribution

Quantum theory provides a comprehensive framework for quantifying uncertainty, often applied in quantum finance to explore the stochastic nature of asset returns. This perspective likens returns to microscopic particle motion, governed by quantum probabilities akin to physical laws. However, such approaches presuppose specific microscopic quantum effects in return changes, a premise criticized for lack of guarantee. This paper diverges by asserting that quantum probability is a mathematical extension of classical probability to complex numbers. It isn't exclusively tied to microscopic quantum phenomena, bypassing the need for quantum effects in returns.By directly linking quantum probability's mathematical structure to traders' decisions and market behaviors, it avoids assuming quantum effects for returns and invoking the wave function. The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns.Utilizing Fourier decomposition, we derive a Schr\"odinger-like trading equation, where each term explicitly corresponds to implications of market trading. The equation indicates discrete energy levels in financial trading, with returns following a normal distribution at the lowest level. As the market transitions to higher trading levels, a phase shift occurs in the return distribution, leading to multimodality and fat tails. Empirical research on the Chinese stock market supports the existence of energy levels and multimodal distributions derived from this quantum probability asset returns model.
A SymTFT for Continuous Symmetries

Symmetry is a powerful tool for studying dynamics in QFT as they provide selection rules, constrain RG flows, and allow for simplified dynamics. Currently, our understanding is that the most general form of symmetry is described by categorical symmetries which can be realized via Symmetry TQFTs or ``SymTFTs. "In this paper, we show how the framework of the SymTFT, which is understood for discrete symmetries (i.e. finite categorical symmetries), can be generalized to continuous symmetries. In addition to demonstrating how $U(1)$ global symmetries can be incorporated into the paradigm of the SymTFT, we apply our formalism to construct the SymTFT for the $\mathbb{Q}/\mathbb{Z}$ non-invertible chiral symmetry in $4d$ theories, demonstrate how symmetry fractionalization is realized SymTFTs, and conjecture the SymTFT for general continuous $G^{(0)}$ global symmetries.
Topological superconductivity induced by a Kitaev spin liquid

We study the effective low-energy fermionic theory of the Kondo-Kitaev model to leading order in the Kondo coupling. Our main goal is to understand the nature of the superconducting instability induced in the proximate metal due to its coupling to spin fluctuations of the spin liquid. The special combination of the low-energy modes of a graphene-like metal and the form of the interaction induced by the Majorana excitations of the spin liquid furnish chiral superconducting order with $p_x + \mathrm{i} p_y$ symmetry. Computing its response to a $\mathrm{U}(1)$ gauge field moreover shows that this superconducting state is topologically non-trivial, characterized by a first Chern number of $\pm 2$.
“A Call to Action” - Mental Illness and Self-Injurious Behavior Occuring in Jails & Prisons

Prisons and jails have become a default mental health system for a significant portion of society. The high prevalence and complexity of mental illness in the correctional system is discussed with reference to a specific issue, inmate self-injury. Self-injurious behaviors are now ubiquitous in correctional institutions and a challenge for administrators. The etiology, prevalence and manifestation of self-injurious behaviors occurring in incarcerated settings is presented. This discussion is linked to a broader “Call to Action” for administrators that includes setting programmatic goals within the context of a mental health crisis (and few resources), consuming empirical research, developing programs for inmates, and addressing staff stress and resilience when working with inmates who are mentally ill.
Using Feminist Geography to Understand Feelings of Safety and Neighborhood Image

Healthy neighborhoods come about via a confluence of factors, including but not limited to walkability, public spaces, feelings of safety, sense of community, and neighborhood reputation. While this research set out to understand why people choose their neighborhoods, a deeper pattern emerged related to feelings of safety, especially for women, and neighborhood reputation. This paper uses feminist geography as a critical lens to understand how power relations often exacerbate negative perceptions of safety and, as such, negative perceptions of a neighborhood's community health. Feminist geography as a lens has practical implications based on these findings for how urban planners and administrators design public spaces. Findings are based on a content analysis of interviews with 75 people throughout the U.S. Stories highlight how feelings of safety could affect neighborhood image and reputation.
Artificial Intelligence Based Suicide Prediction

Suicidal thoughts and behaviors are an international public health problem contributing to 800,000 annual deaths and up to 25 million nonfatal suicide attempts. In the United States, suicide rates have increased steadily for two decades, reaching 47,000 per year and surpassing annual motor vehicle deaths. This trend has prompted government agencies, healthcare systems, and multinational corporations to invest in artificial intelligence-based suicide prediction algorithms. This article describes these tools and the underexplored risks they pose to patients and consumers. AI-based suicide prediction is developing along two separate tracks. In “medical suicide prediction,” AI analyzes data from patient medical records. In “social suicide prediction,” AI analyzes consumer behavior derived from social media, smartphone apps, and the Internet of Things (IoT). Because medical suicide prediction occurs within the context of healthcare, it is governed by the Health Information Portability and Accountability Act (HIPAA), which protects patient privacy; the Federal Common Rule, which protects the safety of human research subjects; and general principles of medical ethics. Medical suicide prediction tools are developed methodically in compliance with these regulations, and the methods of its developers are published in peer-reviewed academic journals. In contrast, social suicide prediction typically occurs outside the healthcare system where it is almost completely unregulated. Corporations maintain their suicide prediction methods as proprietary trade secrets. Despite this lack of transparency, social suicide predictions are deployed globally to affect people’s lives every day. Yet little is known about their safety or effectiveness. Though AI-based suicide prediction has the potential to improve our understanding of suicide while saving lives, it raises many risks that have been underexplored. The risks include stigmatization of people with mental illness, the transfer of sensitive personal data to third-parties such as advertisers and data brokers, unnecessary involuntary confinement, violent confrontations with police, exacerbation of mental health conditions, and paradoxical increases in suicide risk.
Investigating the Link between Community Planning and Engagement and Health Care Delivery Using the National Health Security Preparedness Index

The underlying question for this research is does stronger social capital in a state contribute to stronger health preparedness which then contributes to better health management generally? This study investigates the theory that the types of community planning and engagement involved in health system preparedness and other civic endeavors positively contributes to day-to-day health care operations. To test this theory, data from the Robert Wood Johnson National Health Security Preparedness Index was used. The analyses conducted did find statistically significant relationships between subdomains of community planning and engagement and health care delivery. Subdomains of countermeasures management and information management were found to be statistically significant as well. These findings suggest that particular aspects of community engagement and social capital should be focused on in order to promote improved health performance in different aspects of the field.
Computationally guided exploration of borosin biosynthetic strategies

Borosins are ribosomally synthesized and post-translationally modified peptides containing backbone α-N-methylations. Identification of borosin precursor peptides is difficult because (1) there are no conserved sequence elements among borosin precursor peptides and (2) the biosynthetic gene clusters contain numerous domain architectures and peptide fusions. To tackle this problem, we updated the genome mining tool RODEO to automatically evaluate putative borosin BGCs and identify precursor peptides. Enabled by the new borosin module, we analyzed all borosin BGCs found in available sequence data and assigned precursor peptides to previously orphan borosin methyltransferases. Additionally, we bioinformatically predict and experimentally characterize a new fused borosin domain architecture, in which the modified core is N-terminal to the methyltransferase domain. Finally, we demonstrate that a borosin precursor peptide is the native substrate of shewasin A, a previously characterized pepsin-like aspartic peptidase whose native biological function was unknown.
Exploratory and risk-taking behaviours in coexisting rodents

There has been an increasing interest in modelling the influence of animal personality on species interactions within ecosystems. Animal personality traits associated with dispersal, movement within a home range and risk-taking, including docility and exploration, have been shown to influence an array of environmental variables including seed dispersal and habitat availability. Despite growing interest however, little information is available to model the effects of differences in personality phenotypes among coexisting species. Since coexisting or sympatric species often compete for resources, differences in movement patterns can help mitigate the impact of intra- and interspecific competition. We used two standardized behavioural tests with three species of coexisting rodents in Algonquin Provincial Park, Ontario, Canada to measure exploration and docility personality phenotypes. To evaluate personalities, we modelled plastic changes in behaviours within species and phenotypic variation in behavioural strategies among species. We show empirical evidence to support differences in personality phenotypes in coexisting species and consider the importance of alternative personality strategies in shaping community dynamics.
Ultrasound-Driven Programmable Artificial Muscles

Despite their promise for technological and medical innovations, the advancement of artificial muscles faces challenges in dynamic programmability, wireless control, scalability, and responsiveness. Ultrasound offers a promising yet underutilized solution, providing effective and robust actuation. We introduce an innovative design paradigm for soft artificial muscles, utilizing tens of thousands of resonant microbubbles for targeted activation, enabling a wide range of programmable motions. We developed a comprehensive theoretical model and conducted various applications: from actuating soft grippers and powering centimeter-scale biomimetic stingraybots for propulsion to demonstrating the multimodal shape transformation of a functional surface and equipping static objects with transformative robotic skins for mobility. Our approach is marked by its compactness, high force intensity, rapid responsiveness, and lightweightness. The customizable, scalable design of our artificial muscles offers both immediate and long-term impact on robotics, wearable technology, haptics, prosthetic development, and the development of avant-garde biomedical devices like soft surgical tools. One-Sentence Summary We introduced a first-of-its-kind, programmable artificial muscle using ultrasound-activated microbubbles, providing a compact, responsive, and scalable solution.
Mind the instructions: reward cues are liked first, wanted later

Current theories propose that mental effort is invested only when the anticipated benefits, such as rewards, outweigh the associated costs, like task difficulty. Yet, it remains unclear whether this motivational and mitigating aspect of reward processing is reflected in the evaluation of reward/difficulty cues as such, and to what extent it depends on task experience. In a pre-registered experiment (N=84), we used the affect misattribution procedure (AMP) to gauge affective evaluations of nonword cues predicting reward and task difficulty levels. Contrary to previous studies, the AMP was administered at the outset, after cue instructions, and after the cues were used in a random dot motion (RDM) task. Compared to baseline, cues predicting a larger reward were evaluated more positively after RDM task experience, and most importantly, already after cue instructions, with no difference between the two phases. This evaluative effect manifested in increased performance after larger reward cues in the RDM task. Our results suggest that AMP effects may generally capture performance expectations which are independent of task experience. Importantly, these instructed expectations of reward and difficulty play a crucial role in the evaluation and subsequent investment of mental effort.
Deep rhizospheres extend the nitrogen cycle meters below the base of soil into weathered bedrock

Nitrogen is the most limiting nutrient to forest productivity worldwide. Recently, it has been established that diverse ecosystems source a substantial fraction of their water from weathered bedrock, leading to questions about whether root-driven nitrogen cycling extends into weathered bedrock as well. In this study, we specifically examined nitrogen dynamics using specialized instrumentation distributed across a 16 m weathered bedrock vadose zone (WBVZ) underlying an old growth forest in northern California where the rhizosphere—composed of plant roots and their associated microbiome—extends meters into rock. We documented total dissolved nitrogen (TDN), dissolved organic carbon (DOC), inorganic N (ammonium and nitrate) and CO2 and O2 gasses every 1.5 m to 16 m depth for two years. We found that biologically available nitrogen in the weathered bedrock rhizosphere was comparable in concentration to temperate forest soils and primarily organic. TDN concentrations in the WBVZ exhibited distinct patterns with depth and were correlated with periods of increased whole-ecosystem metabolic activity as well as stream discharge, suggesting competing rhizosphere and leaching processes in the fate of TDN in the WBVZ. Carbon isotope composition of the DOC suggests that dissolved organic matter in the WBVZ is primarily derived from fresh plant sources. We conclude that N cycling in the WBVZ is driven by an active rhizosphere meters below the base of soil and represents an important and overlooked component of deeply rooted ecosystems that must be incorporated into future models and theory of ecosystem function.
Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The network’s nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.
Two-photon all-optical electrophysiology for the dissection of larval zebrafish brain functional connectivity

One of the most audacious goals of modern neuroscience is unraveling the complex web of causal relations underlying the activity of neuronal populations on a whole-brain scale. This endeavor, prohibitive just a couple of decades ago, has recently become within reach owing to the advancements in optical methods and the advent of genetically encoded indicators/actuators. These techniques, applied to the translucent larval zebrafish have enabled recording and manipulation of the activity of extensive neuronal populations spanning the entire vertebrate brain. Here, we present the conception of a custom two-photon optical system, coupling light-sheet imaging and 3D excitation with acousto-optic deflectors for simultaneous high-speed volumetric recording and optogenetic stimulation. By employing a zebrafish line with pan-neuronal expression of both the calcium reporter GCaMP6s and the red-shifted opsin ReaChR, we implemented a crosstalk-free, noninvasive all-optical approach and applied it to the reconstruction of the functional connectivity of the left habenula.