Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Average SParsity

—The Average Sparsity regularisation prior was recently leveraged for compressive imaging in the convex optimisation algorithm SARA, and shown to provide superior reconstruction quality in comparison to simple sparsity [1]. We discuss an approach for introducing Average Sparsity in a Bayesian algorithm for compressive imaging. Compressed sensing (CS) enables the accurate recovery of sparse signals from well-designed sub-Nyquist sampling. The main stream signal reconstruction approaches rely on convex optimisation, in particular on the Basis Pursuit (BP) problem searching for the signal with minimum (cid:96) 1 -norm representation in the sparsity basis, simultaneously satisfying the data constraint. Among many variations of BP, the Average Sparsity regularisation prior was recently lever-aged for compressive imaging in the convex optimisation algorithm SARA, and shown to provide superior reconstruction quality in comparison to simple sparsity [1]. Average sparsity exploits the hypothesis that a natural image is simultaneously sparse in multiple bases, thus providing a more powerful structured sparsity prior. Bayesian reconstruction algorithms have also been proposed. In this case, prior information will be encapsulated into a prior distribution function. Bayesian methods offer several advantages. Here, posterior evaluation will yield the full distribution of the signal rather than just a point estimate. Importantly, in a parametric Bayesian approach, prior parameters can be attached to the signal parameter space and estimated simultaneously. This yields a completely automatic approach that avoids ﬁne-tuning of the algorithms, as is the case in convex optimisation (and greedy) approaches. To our knowledge, no Bayesian approach has made use of average sparsity so far. We discuss an approach for introducing Average Sparsity in a Bayesian algorithm for compressive imaging. The key
Preliminary Results on the Gravitational Slingshot Effect and the Population of Hyperbolic Meteoroids at Earth

Interstellar meteoroids, solid particles arriving from outside our Solar System, are not easily distinguished from local meteoroids. A velocity above the escape velocity of the Sun is often used as an indicator of a possible interstellar origin. We demonstrate that the gravitational slingshot effect, resulting from the passage of local meteoroid near a planet, can produce hyperbolic meteoroids at the Earth s orbit with excess velocities comparable to those expected of interstellar meteoroids.
Segmentation of street light networks

This thesis studies the segmentation of deployments consisting of street lights equipped with wireless networking capabilities. This allows information on the performance of street lights to be communicated via wireless multi-hop networking to one central unit. The goal is to find a partition of street lights and positions of the so-called segment controllers, optimising network performance in terms of end-to-end delay, failure rate and duty cycle. We first derived a mathematical model based on a connectivity graph from this model. Although there is a wide variety of graph partitioning methods available in literature, these were shown to be unfit for our problem, because the wireless performance measures do not trivially translate into a objective function based on graph structure. We verified this claim by investigating two conventional graph partitioning algorithms in the first part of this thesis, namely spectral clustering and k-medoids. Instead, we resorted to a relatively new type of partitioning algorithms, namely the local algorithms. These algorithms optimise conductance of clusters, which is defined as the ratio between the number of external and internal edges. The local algorithms by Spielman-Teng and Andersen-Peres construct clusters step by step, only taking into account the local structure of the graph. This gave us the opportunity to influence the construction process in order to generate good quality clusters with respect to the network performance. Furthermore, we obtained insight into both local algorithms by applying the algorithms to stylised graphs and we stated some general observations on the algorithms. In order to complete the partitioning algorithm based on these local methods, we developed a series of auxiliary routines. Finally, the compared the network performance of the old algorithms and the novel algorithms, by the means of a self-built simulation. Using the simulation, we were also able to provide a list of graph properties which serve as goals or objectives when partitioning a deployment of street lights. The general outcome of these comparison of algorithms was that k-medoids, under some assumptions, optimises for delay, whereas the local algorithms perform better on failure rate and duty cycle.
Advancing the visualization of pure water transport in porous materials by fast, talbot interferometry-based multi-contrast x-ray micro-tomography

The spatio-temporal distribution (4D) of water in porous materials plays a fundamental role in many natural and technological processes. The dynamics of water transport is strongly entangled with the material’s pore-scale structure. Understanding their correlation requires imaging simultaneously the 4D water distribution and the porous microstructure. To date, 4D images with high temporal and spatial resolution have been mainly acquired by attenuation-based X-ray micro-tomography, whereby pure water is substituted by saline solutions with high atomic number components to improve image contrast. The use of saline solutions is however not always desirable, as the altered fluid properties may affect the transport process as well or, as it is the case for hydrating cement-based materials, they may modify the chemical reactions and their kinetics. In this study, we aimed at visualizing pure water transport in porous building materials by a new implementation of fast Talbot interferometry-based multi-contrast X-ray micro-tomography at the P07 beamline of the Helmholtz-Zentrum Geesthacht at DESY. We report results from a mortar specimen imaged at three different stages during evaporative drying. We show the possibility of visualizing simultaneously the microstructure and the pore-scale water redistribution by the phase contrast images. In addition, different solid material phases are clearly distinguished in these images. The higher contrast between water and the porous substrate, achievable in the phase contrast images, compared with the attenuation ones, empowers new analysis and allows investigating the correlation between the drying process and the porous microstructure. The approach offers the possibility of studying other chemically inert or reactive water transport processes without any chemical or physical perturbation of the processes themselves.
Effect of Leverage, Firm Size, Institutional Ownership, and Profitability on CSR Disclosure in Consumer Goods Companies in Indonesia

When investing in a company, investors frequently consider a number of criteria, such as financial success, brand or reputation, track record, and social effect through corporate social responsibility (CSR). This study seeks to quantify the impact of profitability, leverage, business size, and institutional ownership on the disclosure of corporate social responsibility. This study's sample is comprised of consumer products firms listed on the Indonesia Stock Exchange for the period of 2018– 2020. The error correction model is the methodology utilized in this investigation. The long-term effects of leverage and business size on CSR disclosure were unfavorable and significant, according to the findings. The institutional ownership variable has a positive but not statistically significant effect, but the profitability variable has a statistically significant positive effect. On the other hand, the leverage variable has a substantial negative effect on CSR disclosure in the short term. While the scale of the organization has no negative influence of any significance, The influence of the institutional ownership variable is positive but insignificant. The profitability variable has a substantial beneficial impact.
U NDERSTANDING S HARPNESS -A WARE M INIMIZATION

Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations. SAM significantly improves generalization in various settings, however, existing justifications for its success do not seem conclusive. First, we analyze the implicit bias of SAM over diagonal linear networks, and prove that it always chooses a solution that enjoys better generalisation properties than standard gradient descent. We also provide a convergence proof of SAM for non-convex objectives when used with stochastic gradients and empirically discuss the convergence and generalization behavior of SAM for deep networks. Next, we discuss why SAM can be helpful in the noisy label setting where we first show that it can help to improve generalization even for linear classifiers. Then we discuss a gradient reweighting interpretation of SAM and show a further beneficial effect of combining SAM with a robust loss. Finally, we draw parallels between overfitting observed in learning with noisy labels and in adversarial training where SAM also improves generalization. This connection suggests that, more generally, techniques from the noisy label literature can be useful to improve robust generalization.
Combined Lineage Tracing and scRNA-seq Reveals Unexpected First Heart Field 1 Predominance of Human iPSC Differentiation 2 3 4

Combined Lineage Tracing and scRNA-seq Reveals Unexpected First Heart Field 1 Predominance of Human iPSC Differentiation 2 3 4 Francisco X. Galdos, Carissa Lee, Soah Lee, Sharon Paige, William Goodyer, 5 Sidra Xu, Tahmina Samad, Gabriela V. Escobar, Adrija Darsha, Aimee Beck, 6 Rasmus O. Bak, Matthew Porteus, Sean M. Wu 7 8 9 10 Stanford Cardiovascular Institute, 2 Institute for Stem Cell Biology and Regenerative 11 Medicine, Stanford University, Division of Pediatric Cardiology, Department of 12 Pediatrics 4 Division of Cardiovascular of Medicine, Department of Medicine, 13 Department of Pediatrics, Stanford University School of Medicine, Stanford CA 14 Department of Biomedicine, Aarhus University, Aarhus, Denmark, 7 University of 15 California San Diego School of Medicine, Biopharmaceutical Convergence, 16 Department of Pharmacy, Sungkyunkwan University 17 18 19 20 21 *Correspondance: 22 Sean M. Wu, MD, PhD 23 G1120 Lokey Stem Cell Building 24 265 Campus Drive 25 Stanford, CA 94305 26 smwu@stanford.edu 27 28 29 30
Physics of the Dark Universe

Gravitational waves (GWs) can be employed as standard sirens that will soon measure the Hubble constant with sufficient precision to weigh in on the ∼ 5 σ Hubble tension. Most GW sources will have no identified electromagnetic counterpart, leading to uncertainty in the redshift of the source, and in turn a degeneracy between host galaxy distance, redshift, and H 0 . In the case where no electromagnetic counterparts are identified, it has been proposed that a statistical canvassing of candidate GW hosts, found in a large galaxy survey for example, can be used to accurately constrain the Hubble constant. We study and simulate this ‘‘galaxy voting" method to compute H 0 . We find that the Hubble constant posterior is in general biased relative to the true value even when making optimistic assumptions about the statistical properties of the sample. Using the MICECAT light-cone catalog, we find that the bias in the H 0 posteriors depends on the realization of the underlying galaxy sample and the precision of the GW source distance measurement.
What Factors Stimulate Impulse Buying? A Study on Live Commerce Users

: The emergence of live streaming commerce has become evidence that the e-commerce has developed rapidly since Covid-19 pandemic. Consumers who shop at live commerce mostly don’t have a previous plan. This behavior is certainly influenced by various stimuli created by sellers. This study aims to investigate factors in stimulating purchase decisions. The sample used is 100 respondents. Data analysis used is Partial Least Square. The results indicate that interactivity has a significant effect on arousal, and leads to impulse buying. Attractiveness and perceived scarcity have no effect on arousal. This study implies that sellers should focus on improving the way they communicate with consumers. Hence, consumers can feel the togetherness and intimacy and enable impulse purchase to occur.
The inﬂuence of large woody debris on post-wildﬁre debris ﬂow sediment storage

Debris ﬂows transport large quantities of water and granular material, such as sediment and wood, and this mixture can have devastating effects on life and infrastructure. The proportion of large woody debris (LWD) incor-porated into debris ﬂows can be enhanced in forested areas recently burned by wildﬁre because wood recruitment into channels accelerates in burned forests. In this study, using four small watersheds in the Gila National Forest, New Mexico, which burned in the 2020 Tadpole Fire, we explored new approaches to estimate debris ﬂow velocity based on LWD characteristics and the role of LWD in debris ﬂow volume retention. To understand debris ﬂow volume model predictions, we examined two models for debris ﬂow volume estimation: (1) the current volume prediction model used in US Geological Survey debris ﬂow hazard assessments and (2) a regional model developed to predict the sediment yield associated with debris-laden ﬂows. We found that the regional model better matched the magnitude of the observed sediment at the terminal fan, indicating the utility of regionally calibrated pa-rameters for debris ﬂow volume prediction. However, large wood created sediment storage upstream from the terminal fan, and this volume was of the same magnitude as the total debris ﬂow volume stored at the terminal fans. Using ﬁeld and lidar data we found that sediment retention by LWD is largely controlled by channel reach slope and a ratio of LWD length to channel width between 0.25 and 1. Finally, we demonstrated a method for estimating debris ﬂow velocity based on estimates of the critical velocity required to break wood, which can be used in future ﬁeld studies to estimate minimum debris ﬂow velocity values.
Lithospheric Structure and Melting Processes in Southeast Australia: New Constraints From Joint Probabilistic Inversions of 3D Magnetotelluric and Seismic Data

The thermochemical structure of the lithosphere controls melting mechanisms in the mantle, as well as the location of volcanism and ore deposits. Obtaining reliable images of the lithosphere structure, and its complex interactions with the asthenosphere, requires the joint inversion of multiple data sets and their associated uncertainties. In particular, the combination of seismic velocity and electrical conductivity, along with proxies for bulk composition and elusive minor phases, represents a crucial step toward fully understanding large‐scale lithospheric structure and melting processes. We apply a novel probabilistic approach for joint inversions of 3D magnetotelluric and seismic data to image the lithosphere beneath southeast Australia. The results show a highly heterogeneous lithosphere with deep conductivity anomalies that correlate with the location of Cenozoic volcanism. In regions where the conductivities have been at odds with sub‐lithospheric temperatures and seismic velocities, we observe that the joint inversion provides conductivity values consistent with other observations. The results reveal a strong relationship between metasomatized regions in the mantle and (a) boundaries of geological provinces, elucidating the subduction‐accretion process in the region; (b) distribution of leucitite and basaltic magmatism; (c) independent geochemical data, and (d) a series of lithospheric steps which constitute areas prone to generating small‐scale instabilities in the asthenosphere. This scenario suggests that shear‐driven upwelling and edge‐driven convection are the primary mechanisms for melting in eastern Australia, contrary to the conventional notion of mantle plume activity. Our study presents an integrated lithospheric model for southeastern Australia and provides valuable insight into the mechanisms driving surface geological processes.
Possible Imprints of Cold-mode Accretion on the Present-day Properties of Disk Galaxies

Recent theoretical studies suggest that a significant part of the primordial gas accretes onto forming galaxies as narrow filaments of cold gas without building a shock and experiencing heating. Using a simple model of disk galaxy evolution that combines the growth of dark matter halos predicted by cosmological simulations with a hypothetical form of cold-mode accretion, we investigate how this cold-accretion mode affects the formation process of disk galaxies. It is found that the shock-heating and cold-accretion models produce compatible results for low-mass galaxies owing to the short cooling timescale in such galaxies. However, cold accretion significantly alters the evolution of disk galaxies more massive than the Milky Way and puts observable fingerprints on their present properties. For a galaxy with a virial mass , the scale length of the stellar disk is larger by 41% in the cold-accretion model than in the shock-heating model, with the former model reproducing the steep rise in the size–mass relation observed at the high-mass end. Furthermore, the stellar component of massive galaxies becomes significantly redder (0.66 in u − r at ), and the observed color–mass relation in nearby galaxies is qualitatively reproduced. These results suggest that large disk galaxies with red optical colors may be the product of cold-mode accretion. The essential role of cold accretion is to promote disk formation in the intermediate-evolution phase ( ) by providing the primordial gas having large angular momentum and to terminate late-epoch accretion, quenching star formation and making massive galaxies red.
Comparative Generalization Bounds for Deep Neural Networks

In this work, we investigate the generalization capabilities of deep neural networks. We introduce a novel measure of the effective depth of neural networks, defined as the first layer at which sample embeddings are separable using the nearest-class center classifier. Our empirical results demonstrate that, in standard classification settings, neural networks trained using Stochastic Gradient Descent (SGD) tend to have small effective depths. We also explore the relationship between effective depth, the complexity of the training dataset, and generalization. For instance, we find that the effective depth of a trained neural network increases as the proportion of random labels in the data rises. Finally, we derive a generalization bound by comparing the effective depth of a network with the minimal depth required to fit the same dataset with partially corrupted labels. This bound provides non-vacuous predictions of test performance and is found to be empirically independent of the actual depth of the network.
The Joint Efforts of Indonesian and Australian Governments in Countering Terrorism: Intelligence Cooperation

: Terrorism has become a global threat particularly since the 9/11 September tragedy. In Indonesia, acts of terrorism have become a great concern among many stakeholders of the Indonesian government particularly in the aftermath of the Bali Bombing incidents in the beginning of 2000s. The incidents took many lives, and many of them were Australian citizens. As a result, Australia offered Indonesia assistance in the form of cooperation in a variety of areas, including infrastructure and forensics training for police officers. This article aims to analyze the depth and dynamics of Indonesia – Australia intelligence cooperation that contribute to reduce the acts of terrorism in Indonesia. Employing the perspectives of international cooperation and human security, the article argues that the primary driving factor that facilitates Indonesia and Australia cooperation in the areas of counterterrorism and intelligence that the countries face the same terrorism threats that, if not anticipated comprehensively, would endanger the security stability in respective countries and in the Asia-Pacific region.
Production and characterization of flour from tambaqui ( Colossoma macropomum )

Fish farming is an activity that brings many economic and social benefits to Brazil, however, it generates large volumes of waste in fish processing, and the inadequate management of these residues is a concerning environmental problem. On the other hand, these residues are an economical source of proteins and other nutrients, allowing their use in obtaining by-products, such as the preparation of flours that can be included in animal feed when processed correctly. Therefore, this study aimed to evaluate and compare the potential for flour production from tambaqui and baboon residues using two treatments: flour production with cooking (T1) and flour production without cooking (T2). The flour yields obtained from the residues of the different treatments were 13.5% and 18.0% for baboon and 12.4% and 18.0% for tambaqui (T1 and T2, respectively). The highest protein contents were observed in the flours obtained from tambaqui residues. Microbiological analyses showed the absence of Salmonella spp in the flour samples, complying with current legislation. Based on this study, it is concluded that the obtained flours meet the pre-established standards of crude protein and moisture, complying with the criteria established by RIISPOA for second-quality flours. Furthermore, these flours represent a promising alternative for the reuse of fish residues, which are often improperly discarded in the environment, causing significant negative impacts.
POTENTIAL DISTRIBUTION OF THE CARACAL ( CARACAL CARACAL SCHREBER, 1776) UNDER CLIMATE CHANGE

The Intergovernmental Panel on Climate Change (IPCC) predicts an increase in global temperatures of between 1.48 °C and 5.88 °C during the 21st century. Using climatic habitat suitability (climate envelope) models, we assessed the potential influence of climate change on the range of the caracal (Caracal caracal Schreber) based on the IPCC’s future climatic scenarios. According to our model, the caracal faces probable local extinction risks in future warming scenarios. The results of this study indicated that the caracal’s response to climate change was dependent on its adaptive likelihood and the present and future probability of climate change. The caracal’s suitability exhibited trends toward local extinction in the future. We suggested that its placement on the IUCN Red List be reassessed. The caracal abundance is predicted to become dramatically reduced in the Mediterranean region but to increase towards the east and south of Africa due to climatic conditions in the future. Bioclimatic envelope models do not account for non-climatic factors such as land use, biotic interactions, human interference, and dispersal or history, and results should therefore be seen as first approximations of the potential magnitude of effect of future climatic change for the caracal species.