Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Supernovae Quench Star Formation in High-z Galaxies?

JWST is providing a unique opportunity to directly study the feedback processes regulating star formation (SF) in early galaxies. The two z > 5 quiescent systems (JADES-GS-z7-01-QU and MACS0417-z5BBG) detected so far show a recent starburst after which SF is suppressed. To clarify whether such quenching is due to supernova (SN) feedback, we have developed a minimal physical model. We derive a condition on the minimum star formation rate, SFR min , lasting for a time interval Δt b , required to quench SF in a galaxy at redshift z, with gas metallicity Z, and hosted by a halo of mass M h . We find that lower (z, Z, M h ) systems are more easily quenched. We then apply the condition to JADES-GS-z7-01-QU (z = 7.3, M ⋆ = 108.6 M ⊙) and MACS0417-z5BBG (z = 5.2, M ⋆ = 107.6 M ⊙) and find that SN feedback largely fails to reproduce the observed quenched SF history. Alternatively, we suggest that SF is rapidly suppressed by radiation-driven dusty outflows sustained by the high specific star formation rates (43 and 25 Gyr−1, respectively) of the two galaxies. Our model provides a simple tool to interpret the SF histories of post-starburst galaxies and unravel quenching mechanisms from incoming JWST data.
A Hierarchical Random Effects State-space Model for Modeling Brain Activities from Electroencephalogram Data

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a potential biomarker for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address nonstationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of Major Depressive Disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.
Deep Generative Models of Music Expectation

A prominent theory of affective response to music revolves around the concepts of surprisal and expectation. In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences. To date, however, these models have been limited to compute exact probabilities through hand-crafted features or restricted to linear models which are likely not sufficient to represent the complex conditional distributions present in music. In this work, we propose to use modern deep probabilistic generative models in the form of a Diffusion Model to compute an approximate likelihood of a musical input sequence. Unlike prior work, such a generative model parameterized by deep neural networks is able to learn complex non-linear features directly from a training set itself. In doing so, we expect to find that such models are able to more accurately represent the 'surprisal' of music for human listeners. From the literature, it is known that there is an inverted U-shaped relationship between surprisal and the amount human subjects 'like' a given song. In this work we show that pre-trained diffusion models indeed yield musical surprisal values which exhibit a negative quadratic relationship with measured subject 'liking' ratings, and that the quality of this relationship is competitive with state of the art methods such as IDyOM. We therefore present this model a preliminary step in developing modern deep generative models of music expectation and subjective likability.
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture

Transformer-based models still face the structural limitation of fixed context length in processing long sequence input despite their effectiveness in various fields. While various external memory techniques were introduced, most previous techniques fail to avoid fateful forgetting, where even the most important memories are inevitably forgotten after a sufficient number of time steps. We designed Memoria, a memory system for artificial neural networks, drawing inspiration from humans and applying various neuroscientific and psychological theories related to memory. Experimentally, we demonstrated the effectiveness of Memoria in tasks such as sorting and language modeling, surpassing conventional techniques.
Simple-sum giant graviton expansions for orbifolds and orientifolds

We study giant graviton expansions of the superconformal index of 4d orbifold/orientifold theories. In general, a giant graviton expansion is given as a multiple sum over wrapping numbers. It has been known that the expansion can be reduced to a simple sum for the ${\cal N}=4$U(N) SYM by choosing appropriate expansion variables. We find such a reduction occurs for a few examples of orbifold and orientifold theories: $\mathbb {Z}_k$ orbifold and orientifolds with O3 and O7. We also argue that for a quiver gauge theory associated with a toric Calabi-Yau 3-fold the simple-sum expansion works only if the toric diagram is a triangle, that is, the Calabi-Yau is an orbifold of $\mathbb {C}^3$.
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations

Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting. Further, to counter the emerging section injection attacks, we propose a graph-based section-dependent information extraction scheme for software representation. The proposed scheme leverages aggregated information within various sections in the software to enable robust malware detection and mitigate adversarial settings. Our experimental results show that traditional malware detection models are ineffective against adversarial threats. However, the attack surface can be largely reduced by eliminating the volatile information. Therefore, we propose simple-yet-effective methods to mitigate the impacts of binary manipulation attacks. Overall, our graph-based malware detection scheme can accurately detect malware with an area under the curve score of 88.32\% and a score of 88.19% under a combination of binary manipulation attacks, exhibiting the efficiency of our proposed scheme.
Investigation of Spatial Risk Factors for RVF Disease Occurrence Using Remote Sensing & GIS—A Case Study: Sinnar State, Sudan

Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and negative implications on human and animal health. This study was conducted to verify the factors which influenced the spatial pattern of Rift Valley Fever occurrence and identified the high risk areas for the occurrence of the disease at Sinner State, Sudan. The normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall data in addition to the point data of RVF clinical cases in humans were used in this study. In order to identify the RVF high risk areas, remote sensing data and rainfall data were integrated in a GIS with other information including, soil type, water body, DEM (Digital Elevation Model), and animal routes and analyzed using Spatial Analysis tools. The information on clinical cases was used for verification. The Normalized Difference Vegetation Index (NDVI) was used to describe vegetation patterns of the study area by calculating the mean NDVI. The results of the study showed that, RVF risk increased with the increase in vegetation cover (high NDVI values), and increase in rainfall, which both provided suitable conditions for disease vectors breeding and a good indicator for RVF epizootics. The study concluded that, identification of high risk area for RVF disease improved the understanding of the spatial distribution of the disease and helped in locating the areas where disease was likely to be endemic and therefore preparedness measures should be taken. The identification represents the first step of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation. Further detailed studies are recommended in this domain.
Minimal quantum dot based Kitaev chain with only local superconducting proximity effect

The possibility to engineer a Kitaev chain in quantum dots coupled via superconductors has recently emerged as a promising path toward topological superconductivity and possibly nonabelian physics. Here, we show that it is possible to avoid some of the main experimental hurdles on this path by using only local proximity effect on each quantum dot in a geometry that resembles a two-dot version of the proposal in New J. Phys. 15 045020 (2013). There is no need for narrow superconducting couplers, additional Andreev bound states, or spatially varying magnetic fields; it suffices with spin-orbit interaction and a constant magnetic field, in combination with control of the superconducting phase to tune the relative strengths of elastic cotunneling and an effective crossed-Andreev-reflection-like process generated by higher-order tunneling. We use a realistic spinful, interacting model and show that high-quality Majorana bound states can be generated already in a double quantum dot.
Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning

Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric vision with its advantages in guiding agents to perform daily tasks in complex environments. In this paper, we propose an interpretable and generalizable visual planning framework consisting of i) a novel Substitution-based Concept Learner (SCL) that abstracts visual inputs into disentangled concept representations, ii) symbol abstraction and reasoning that performs task planning via the self-learned symbols, and iii) a Visual Causal Transition model (ViCT) that grounds visual causal transitions to semantically similar real-world actions. Given an initial state, we perform goal-conditioned visual planning with a symbolic reasoning method fueled by the learned representations and causal transitions to reach the goal state. To verify the effectiveness of the proposed model, we collect a large-scale visual planning dataset based on AI2-THOR, dubbed as CCTP. Extensive experiments on this challenging dataset demonstrate the superior performance of our method in visual task planning. Empirically, we show that our framework can generalize to unseen task trajectories, unseen object categories, and real-world data. Further details of this work are provided at https://fqyqc.github. io/ConTranPlan/.
Role of Spatial Coherence in Diffractive Optical Neural Networks

Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. However, previous experimental demonstrations of DONNs have only been performed using coherent light, which is not present in the natural world. Here, we study the role of spatial optical coherence in DONN operation. We propose a numerical approach to efficiently simulate DONNs under input illumination with arbitrary spatial coherence and discuss the corresponding computational complexity using coherent, partially coherent, and incoherent light. We also investigate the expressive power of DONNs and examine how coherence affects their performance. In particular, we show that under fully incoherent illumination, the DONN performance cannot surpass that of a linear model. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits using light with varying spatial coherence.
A method for the segmentation of very high spatial resolution images of forested landscapes

Pixel-by-pixel image analysis methods are not applicable when using VHR image material in multisource forest inventory applications. One possible solution to this problem is to define the units of image analysis by means of image segmentation. The paper presents a two-phase segmentation method (COS) based on segmentation in the feature space and co-occurrence region merging (CRM). The resulting segments were tested in the spectral feature extraction, and the estimation of plot-level total volume and volumes by tree species. The study material consisted of an AISA spectrometer image and 254 relascope field data plots. Two different segmentations were derived and the performance of segment-based feature sets were compared to that of a feature set extracted from the local neighbourhood of the field plots. Cross-validation techniques and a k-nn estimator were applied in the estimation tests. The estimation results which had the smallest rms errors were achieved with segment-based features in the cases of total volume and the volume of deciduous species. In the cases of pine and spruce, the features from the local neighbourhood performed best. The study suggests that COS produces segments which can be used as units for further image analysis.
HST FUV Spectroscopy of Super Star Cluster A in the Green Pea Analog Mrk 71: Revealing the Presence of Very Massive Stars

Mrk 71 is a low-metallicity (Z = 0.16 Z ☉) starburst region in the local dwarf galaxy NGC 2366, hosting two super star clusters (SSCs A and B), and it is recognized as a Green Pea (GP) analog with SSC A responsible for the GP properties. We present STIS and FOS far-ultraviolet (FUV) spectra of the embedded SSC Mrk 71-A obtained with the Hubble Space Telescope. The STIS FUV spectrum shows the characteristic features of very massive stars (VMS; masses >100 M ⊙) and we derive an age of 1 ± 1 Myr by comparison with the Charlot & Bruzual suite of spectral population synthesis models with upper mass limits of 300 and 600 M ⊙. We compare the STIS spectrum with all known SSC spectra exhibiting VMS signatures: NGC 5253-5, R136a, NGC 3125-A1, and the z = 2.37 Sunburst cluster. We find that the cluster mass-loss rates and wind velocities, as characterized by the C iv P Cygni profiles and the He ii emission line strengths, are very similar over Z = 0.16–0.4 Z☉. This agrees with predictions that the optically thick winds of VMS will be enhanced near the Eddington limit and show little metallicity dependence. We find very strong damped Lyα absorption with N(H i) =1022.2 cm−2 associated with Mrk 71-A. We discuss the natal environment of this young SSC in terms of radiatively driven winds, catastrophic cooling, and recent models where the cluster is surrounded by highly pressurized clouds with large neutral columns.
Digital Ethics in Federated Learning

The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gained significant attention for its potential in privacy preservation and learning efficiency enhancement. In this paper, we highlight the digital ethics concerns that arise when human-centric devices serve as clients in FL. More specifically, challenges of game dynamics, fairness, incentive, and continuity arise in FL due to differences in perspectives and objectives between clients and the server. We analyze these challenges and their solutions from the perspectives of both the client and the server, and through the viewpoints of centralized and decentralized FL. Finally, we explore the opportunities in FL for human-centric IoT as directions for future development.
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision

Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.
Making urban flood resilience more operational: current practice

Urbanisation and climate change have increased both the probability and the severity of flooding. The risk paradigm that follows shifts from traditional to more integrated approaches, since one of the main tasks emerging for flood managers is the development of resilient cities. The concept of urban resilience is becoming more and more important, despite the numerous challenges that interfere with its implementation. The goal of this research is to study how engineering and other design disciplines can improve flood resilience on an urban neighbourhood scale. This paper presents the analysis of an overview of research aimed at operationalising urban resilience to floods. This literature highlights the fact that almost no references can be found concerning studies focused on operationalising flood resilience at the neighbourhood level and particularly through urban design practices. Indeed, through interviews conducted with several stakeholders, it was confirmed that clear cooperation between flood resilience and urban design practices at the neighbourhood level does not yet exist.
Selecting the best compositions of a wheelchair basketball team: a data-driven approach

Wheelchair basketball, regulated by the International Wheelchair Basketball Federation, is a sport designed for individuals with physical disabilities. This paper presents a data-driven tool that effectively determines optimal team line-ups based on past performance data and metrics for player effectiveness. Our proposed methodology involves combining a Bayesian longitudinal model with an integer linear problem to optimise the line-up of a wheelchair basketball team. To illustrate our approach, we use real data from a team competing in the Rollstuhlbasketball Bundesliga, namely the Doneck Dolphins Trier. We consider three distinct performance metrics for each player and incorporate uncertainty from the posterior predictive distribution of the longitudinal model into the optimisation process. The results demonstrate the tool's ability to select the most suitable team compositions and calculate posterior probabilities of compatibility or incompatibility among players on the court.
Dissonance in Harmony: The UV/Optical Periodic Outbursts of ASASSN-14ko Exhibit Repeated Bumps and Rebrightenings

ASASSN-14ko was identified as an abnormal periodic nuclear transient with a potential decreasing period. Its outbursts in the optical and UV bands have displayed a consistent and smooth “fast rise and slow decay” pattern since its discovery, which has recently experienced an unexpected alteration in the last two epochs, as revealed by our proposed high-cadence Swift observations. The new light curve profiles show a bump during the rising stages and a rebrightening during the declining stages, making them much broader and symmetrical than the previous ones. In the last two epochs, there is no significant difference in the X-ray spectral slope compared to the previous one, and its overall luminosity is lower than those of the previous epochs. The energy released in the early bump and rebrightening phases (∼1050 erg) could be due to collision of the stripped stream from partial tidal disruption events with an expanded accretion disk. We also discussed other potential explanations, such as disk instability and star–disk collisions. Further high-cadence multiwavelength observations of subsequent cycles are encouraged to comprehend the unique periodic source with its new intriguing features.
Double-Plume Integral Models for Near-Field Mixing in Multiphase Plumes

We present a generalized integral model for multiphase plumes in stratified ambient conditions based on the double-plume approach, where the plume is composed of a rising, multiphase core plume surrounded by a counterflowing outer ring plume of dense fluid. The generalized model captures as limiting cases the current approaches in the literature, including two-fluid and mixed-fluid equations, continuous and discrete detrainment, dispersed-phase mass transfer, and two models for entrainment in the counterflow region. These modeling approaches are compared and validated against both laboratory and field-scale data. In unstratified conditions, all model formulations perform equally well. In stratification, entrainment in the counterflow region is best represented by correlation to the inner plume velocity instead of the difference between the inner and outer plume velocities. The vertical distribution of the exchange between the inner and outer plumes in the models differs from that measured in the prototype due to enhanced entrainment at the detrainment zone and forced entrainment from the collapsing intrusion layer. Nonetheless, the models predict well the length scales and volume fluxes at the detrainment zone and intrusion layer. Applications are demonstrated for reservoir air bubble plumes. The mass transfer and near-field mixing in the double-plume integral model prove sufficiently accurate to predict the depth of maximum plume rise (both the locations of total dissolution of the bubbles and the maximum height of the decelerating plume) and the volume flux, dissolved constituent mass flux, and trap height of the intrusion.