Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities

Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over- as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings: Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification -- even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments -- does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research. Funding: The German Research Foundation, and the Interdisciplinary Centre for Clinical Research of the University of Munster.
Towards a major methodological shift in depression research by assessing continuous scores of recurrence of illness, lifetime and current suicidal behaviors and phenome features: focus on atherogenicity and adverse childhood experiences

Background: The binary major depressive disorder (MDD) diagnosis is inadequate and should never be used in research. Aims: The study's objective is to explicate our novel precision nomothetic strategy for constructing depression models based on adverse childhood experiences (ACEs), lifetime and current phenome, and biomarker (atherogenicity indices) scores. Methods: This study assessed recurrence of illness (ROI: namely recurrence of depressive episodes and suicidal behaviors), lifetime and current suicidal behaviors and the phenome of depression, neuroticism, dysthymia, anxiety disorders, and lipid biomarkers (including ApoA, ApoB, free cholesterol and cholesteryl esters, triglycerides, high density lipoprotein cholesterol) in 67 normal controls and 66 MDD patients. We computed atherogenic and reverse cholesterol transport indices. Results: We were able to extract one factor from a) the lifetime phenome of depression comprising ROI, and traits such as neuroticism, dysthymia and anxiety disorders, and b) the phenome of the acute phase (based on depression, anxiety and quality of life scores). PLS analysis showed that 55.7% of the variance in the lifetime + current phenome factor was explained by increased atherogenicity, neglect and sexual abuse, while atherogenicity partially mediated the effects of neglect. Cluster analysis generated a cluster of patients with major dysmood disorder, which was externally validated by increased atherogenicity and characterized by increased scores of all clinical features. Conclusions: The outcome of depression should not be represented as a binary variable (MDD or not), but rather as multiple dimensional scores based on biomarkers, ROI, subclinical depression traits, and lifetime and current phenome scores including suicidal behaviors.
Protocols for Maliciously Secure Multiparty Computation with an Honest Majority in Heterogeneous Network Settings

In this work, we present novel protocols over rings for semi-honest secure three-party and malicious four-party computation with one corruption. Our protocols are optimized for heterogeneous network settings. P 1 and P 2 do not need to communicate with each other, while P 1 and P 3 communicate only in the ofﬂine phase in a single communication round. Our four-party protocol additionally requires P 4 to only communicate with P 1 in the ofﬂine phase in a single communication round. Thus, only P 2 and P 3 need to share a low latency, high bandwidth channel to achieve fast runtimes. We list several applications where this property can be useful. For instance, if P 1 and P 2 want to perform a two-party computation but share a weak network link they can utilize an auxiliary P 3 with a strong network link to P 2 to accelerate the secure computation. Our three-party protocol requires 1 domain element of global communication in the ofﬂine phase and 2 elements of global communication in the online phase. Our four-party protocol requires 2 elements of global communication in the ofﬂine phase and 3 elements of global communication in the online phase. This protocol can provide fairness and guaranteed output delivery. The best previous four-party malicious protocols with one corruption require 6 elements of global communication. We also show different ways of how to transform our protocols into homogeneous protocols where parties communicate equally on all links at the same global communication complexity.
Prospects of Searches for Anomalous Hadronic Higgs Boson Decays at the LHeC

The future Large Hadron Electron Collider (LHeC) would enable collisions of an intense electron beam with either protons or heavy ions at the High Luminosity-Large Hadron Collider (HL- LHC). With a center of mass energy greater than a TeV and a high luminosity, the LHeC would be a new-generation collider for deep-inelastic scattering (DIS) and a significant facility for precise Higgs physics, complementing pp and electron-positron colliders. Anomalous hadronic decay signatures of the Higgs Boson, such as those into three or more partons, are challenging to detect at the LHC due to high background rates, but they may be observable at the LHeC. This paper presents the expected sensitivity of the LHeC for the decay channels $H \rightarrow 3jets$ and $H \rightarrow 4jets$, assuming an integrated luminosity of 1 ab$^{-1}$. Our analysis indicates that upper limits on the branching ratio of the Higgs Boson of 0.35 and 0.17 at 95% confidence limit for these processes are possible.
The Lindstrom's Characterizability of Abstract Logic Systems for Analytic Structures Based on Measures

In 1969, Per Lindstrom proved his celebrated theorem characterising the first-order logic and established criteria for the first-order definability of formal theories for discrete structures. K. J. Barwise, S. Shelah, J. Vaananen and others extended Lindstrom's characterizability program to classes of infinitary logic systems, including a recent paper by M. Dzamonja and J. Vaananen on Karp's chain logic, which satisfies interpolation, undefinability of well-order, and is maximal in the class of logic systems with these properties. The novelty of the chain logic is in its new definition of satisfability. In our paper, we give a framework for Lindstrom's type characterizability of predicate logic systems interpreted semantically in models with objects based on measures (analytic structures). In particular, Hajek's Logic of Integral is redefined as an abstract logic with a new type of Hajek's satisfiability and constitutes a maximal logic in the class of logic systems for describing analytic structures with Lebesgue integrals and satisfying compactness, elementary chain condition, and weak negation.
Heterogeneous robot teams with unified perception and autonomy: How Team CSIRO Data61 tied for the top score at the DARPA Subterranean Challenge

The DARPA Subterranean Challenge was designed for competitors to develop and deploy teams of autonomous robots to explore difficult unknown underground environments. Categorised in to human-made tunnels, underground urban infrastructure and natural caves, each of these subdomains had many challenging elements for robot perception, locomotion, navigation and autonomy. These included degraded wireless communication, poor visibility due to smoke, narrow passages and doorways, clutter, uneven ground, slippery and loose terrain, stairs, ledges, overhangs, dripping water, and dynamic obstacles that move to block paths among others. In the Final Event of this challenge held in September 2021, the course consisted of all three subdomains. The task was for the robot team to perform a scavenger hunt for a number of pre-defined artefacts within a limited time frame. Only one human supervisor was allowed to communicate with the robots once they were in the course. Points were scored when accurate detections and their locations were communicated back to the scoring server. A total of 8 teams competed in the finals held at the Mega Cavern in Louisville, KY, USA. This article describes the systems deployed by Team CSIRO Data61 that tied for the top score and won second place at the event.
Contracting edges to destroy a pattern: A complexity study

Given a graph G and an integer k, the objective of the $\Pi$-Contraction problem is to check whether there exists at most k edges in G such that contracting them in G results in a graph satisfying the property $\Pi$. We investigate the problem where $\Pi$ is `H-free' (without any induced copies of H). It is trivial that H-free Contraction is polynomial-time solvable if H is a complete graph of at most two vertices. We prove that, in all other cases, the problem is NP-complete. We then investigate the fixed-parameter tractability of these problems. We prove that whenever H is a tree, except for seven trees, H-free Contraction is W[2]-hard. This result along with the known results leaves behind three unknown cases among trees.
An overview of HMI off-disk flare observations

Context: White-light continuum observations of solar flares often have coronal counterparts, including the classical ``white-light prominence'' (WLP) phenomenon. Aims: Coronal emissions by flares, seen in white-light continuum, have only rarely been reported previously. We seek to use modern data to understand the morphology of WLP events. Methods: We have identified a set of 14 examples of WLP detected by the HMI (Heliospheric and Magnetic Imager) experiment on board SDO (the Solar Dynamics Observatory satellite), using a new on-line catalogue covering 2011-2017. These invariably accompanied white-light flares (WLF) emission from the lower atmosphere by flares near the limb, as identified by hard X-ray images from RHESSI (the Reuven Ramaty High Energy Spectroscopic Imager). HMI provides full Stokes information, and we have used the linear polarisations (Q~and~U) to distinguish Thomson scattering from cool material. Results: The event morphologies fit roughly into three categories: Ejection, Loop, and Spike, but many events show multiple phenomena. Conclusions: The coronal white-light continuum, observed by HMI analogously to the observations made by a coronagraph, detects many examples of coronal emission and dynamics. Using the Stokes linear polarisation, we estimate the masses of hot coronal plasma in 11 of the 14 events and find them to be similar to typical CME masses, but not exceeding 10$^{15}$\,g. We note that the HMI observations do not occult the bright solar disk and were not designed for coronal observations, resulting in relatively low signal-to-noise ratios. We therefore believe that future such observations with better optimisation will be even more fruitful.
Software principles and concepts applied in the implementation of cyber-physical systems for real-time advanced process control

Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this paper, we use simple examples to describe the software principles and concepts used in the implementation of such systems. The key software principles are 1) shared data in the form of a database, files, or shared memory, 2) timers and threads for concurrent periodic execution of tasks, and 3) network communication between the control system and the process, and communication between the control system and the internet, e.g., the cloud to enable remote monitoring and commands. We show how to implement such systems for Linux operating systems applying the C programming language and we also comment on the implementation using the Python programming language. Finally, we present a complete simulation experiment using a real-time simulator.
A Survey on Uncertainty Quantification for Deep Learning: An Uncertainty Source Perspective

Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make unexpected, incorrect, but overconfident predictions. This can cause serious consequences in high-stake applications, such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) aims to estimate the confidence of DNN predictions beyond prediction accuracy. In recent years, many UQ methods have been developed for DNNs. It is of great practical value to systematically categorize these UQ methods and compare their advantages and disadvantages. However, existing surveys mostly focus on categorizing UQ methodologies from a neural network architecture perspective or a Bayesian perspective and ignore the source of uncertainty that each methodology can incorporate, making it difficult to select an appropriate UQ method in practice. To fill the gap, this paper presents a systematic taxonomy of UQ methods for DNNs based on the types of uncertainty sources (data uncertainty versus model uncertainty). We summarize the advantages and disadvantages of methods in each category. We show how our taxonomy of UQ methodologies can potentially help guide the choice of UQ method in different machine learning problems (e.g., active learning, robustness, and reinforcement learning). We also identify current research gaps and propose several future research directions.
Robust Robot Planning for Human-Robot Collaboration

In human-robot collaboration, the objectives of the human are often unknown to the robot. Moreover, even assuming a known objective, the human behavior is also uncertain. In order to plan a robust robot behavior, a key preliminary question is then: How to derive realistic human behaviors given a known objective? A major issue is that such a human behavior should itself account for the robot behavior, otherwise collaboration cannot happen. In this paper, we rely on Markov decision models, representing the uncertainty over the human objective as a probability distribution over a finite set of objective functions (inducing a distribution over human behaviors). Based on this, we propose two contributions: 1) an approach to automatically generate an uncertain human behavior (a policy) for each given objective function while accounting for possible robot behaviors; and 2) a robot planning algorithm that is robust to the above-mentioned uncertainties and relies on solving a partially observable Markov decision process (POMDP) obtained by reasoning on a distribution over human behaviors. A co-working scenario allows conducting experiments and presenting qualitative and quantitative results to evaluate our approach.
The middle ultraviolet‐visible spectrum of H2 excited by electron impact

The electron-impact-induced emission spectrum of H 2 has been measured in the extended wavelength region 175-530 nm at a spectral resolution of 1.7 nm (full width at half maximum). The laboratory spectra observed in the middle ultraviolet (MUV) and visible spectral region are characterized by underlying H 2 (a 3 Σ g + → b 3 Σ b + ) continuum emission, together with many strong lines assigned to the radiative decay of the gerade singlet states of H 2 , and to members of the H Balmer series resulting from dissociative excitation of H 2 . Our calibrated MUV spectral data, obtained at 14, 19, and 100 eV electron-impact energies, provide absolute emission cross sections of these H 2 lines and will assist in the interpretation of planned Galileo ultraviolet spectrometer observations of Jupiter's aurora in this wavelength region.
The Dormant Neuron Phenomenon in Deep Reinforcement Learning

In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity. We demonstrate the presence of this phenomenon across a variety of algorithms and environments, and highlight its effect on learning. To address this issue, we propose a simple and effective method (ReDo) that Recycles Dormant neurons throughout training. Our experiments demonstrate that ReDo maintains the expressive power of networks by reducing the number of dormant neurons and results in improved performance.
Dependency Dialogue Acts - Annotation Scheme and Case Study

In this paper, we introduce Dependency Dialogue Acts (DDA), a novel framework for capturing the structure of speaker-intentions in multi-party dialogues. DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes the multi-relational response structure of dialogues in addition to the dialogue acts and rhetorical relations. It represents the functional, discourse, and response structure in multi-party multi-threaded conversations. A few key features distinguish DDA from existing dialogue annotation frameworks such as SWBD-DAMSL and the ISO 24617-2 standard. First, DDA prioritizes the relational structure of the dialogue units and the dialog context, annotating both dialog acts and rhetorical relations as response relations to particular utterances. Second, DDA embraces overloading in dialogues, encouraging annotators to specify multiple response relations and dialog acts for each dialog unit. Lastly, DDA places an emphasis on adequately capturing how a speaker is using the full dialog context to plan and organize their speech. With these features, DDA is highly expressive and recall-oriented with regard to conversation dynamics between multiple speakers. In what follows, we present the DDA annotation framework and case studies annotating DDA structures in multi-party, multi-threaded conversations.
A Novel Approach to Participant-Level Influence Calculation in Viral Cascades

Efforts to model viral cascades provide a vital view into how they form and spread. A range of methods, such as Multivariate Hawkes Processes or network inference algorithms, attempt to decompose cascades into constituent components via inference -- by constructing an underlying network of influence, or by generating direct pairwise influence measures between cascade participants. While these approaches provide detailed models of the generative mechanics underlying event sequences, their sophistication often comes at a steep computational cost that prevents them from being applied to large-scale datasets. This is particularly the case for Multivariate Hawkes Processes. In this work, we propose a novel, scalable method for generating individual-level influence measures across a set of cascades. Across real-world datasets, we demonstrate the alignment of this approach's calculations with the influence inferred by established methods, as well as the computational scalability of this method.
Two-photon Production in Low-velocity Shocks

The Galactic interstellar medium abounds in shocks with low velocities v s ≲ 70 km s−1. Some are descendants of higher velocity shocks, while others start off at low velocity (e.g., stellar bow shocks, intermediate velocity clouds, spiral density waves). Low-velocity shocks cool primarily via Lyα and two-photon continuum, augmented by optical recombination lines (e.g., Hα), forbidden lines of metals and free-bound emission, free–free emission. The dark far-ultraviolet (FUV) sky, aided by the fact that the two-photon continuum peaks at 1400 Å, makes the FUV band an ideal tracer of low-velocity shocks. GALEX FUV images reaffirm this expectation, discovering faint and large interstellar structure in old supernova remnants and thin arcs stretching across the sky. Interstellar bow shocks are expected from fast stars from the Galactic disk passing through the numerous gas clouds in the local interstellar medium within 15 pc of the Sun. Using the bests atomic data available to date, we present convenient fitting formulae for yields of Lyα, two-photon continuum, and Hα for pure hydrogen plasma in the temperature range of 104–105 K. The formulae presented here can be readily incorporated into time-dependent cooling models as well as collisional ionization equilibrium models.
An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)

We study the performance of a commercially available large language model (LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K. To our knowledge, this is the first independent evaluation of ChatGPT. We found that ChatGPT's performance changes dramatically based on the requirement to show its work, failing 20% of the time when it provides work compared with 84% when it does not. Further several factors about MWPs relating to the number of unknowns and number of operations that lead to a higher probability of failure when compared with the prior, specifically noting (across all experiments) that the probability of failure increases linearly with the number of addition and subtraction operations. We also have released the dataset of ChatGPT's responses to the MWPs to support further work on the characterization of LLM performance and present baseline machine learning models to predict if ChatGPT can correctly answer an MWP. We have released a dataset comprised of ChatGPT's responses to support further research in this area.
Continuously Adjustable Daytime Thermal Management of Thermoplastic Polyurethane Membrane Based on Strain Adjustable Reflectivity

The electricity-free nature of passive radiative cooling technologies is highly attractive in pursuing sustainable development. However, current cooling materials are often static, which makes it difficult to cope with the varying needs of all-weather and year-round thermal comfort management. Herein, we demonstrate an electrospinning thermoplastic polyurethane (Es-TPU) membrane, whose optical properties can be adjusted by simple mechanical stretching to induce a dynamic adjustment of its thermal management function. The excellent mechanical properties of the Es-TPU membrane allow to tune its cooling and heating powers of 118.25W/m 2 and 220.34 W/m 2 , respectively. In its cooling state (0% strain), the Es-TPU membrane shows a high and angular-independent reflectance of 96.0% in the 0.2-2.5 wavelength range and infrared emissivity (93.3%) in the atmospheric transparency window (8-13μm), reaching a cooling effect of 10°C at midday. The excellent mechanical properties of the Es-TPU membrane allows the continuous adjustment of reflectivity which can be reduced to 61% (ΔR=35.0%) when the strain is 80%, leading to a net temperature increase of 9.5°C. The strong haze, hydrophobicity and outstanding aging resistance exhibited by this scalable membrane hold high promise for achieving uniform illumination with tunable strength and efficient thermal management in practical application scenarios.