Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for the epoch of reionisation with hera: Upper Limits on the Closure Phase Delay Power Spectrum

Radio interferometers aiming to measure the power spectrum of the redshifted 21 cm line during the Epoch of Reionisation (EoR) need to achieve an unprecedented dynamic range to separate the weak signal from overwhelming foreground emissions. Calibration inaccuracies can compromise the sensitivity of these measurements to the effect that a detection of the EoR is precluded. An alternative to standard analysis techniques makes use of the closure phase, which allows one to bypass antenna-based direction-independent calibration. Similarly to standard approaches, we use a delay spectrum technique to search for the EoR signal. Using 94 nights of data observed with Phase I of the Hydrogen Epoch of Reionization Array (HERA), we place approximate constraints on the 21 cm power spectrum at z = 7.7. We find at 95% confidence that the 21 cm EoR brightness temperature is ≤(372)2 ‘pseudo’ mK2 at 1.14 ‘pseudo’ h Mpc−1, where the ‘pseudo’ emphasises that these limits are to be interpreted as approximations to the actual distance scales and brightness temperatures. Using a fiducial EoR model, we demonstrate the feasibility of detecting the EoR with the full array. Compared to standard methods, the closure phase processing is relatively simple, thereby providing an important independent check on results derived using visibility intensities, or related.
Theory and Implementation of Complex-Valued Neural Networks

This work explains in detail the theory behind Complex-Valued Neural Network (CVNN), including Wirtinger calculus, complex backpropagation, and basic modules such as complex layers, complex activation functions, or complex weight initialization. We also show the impact of not adapting the weight initialization correctly to the complex domain. This work presents a strong focus on the implementation of such modules on Python using cvnn toolbox. We also perform simulations on real-valued data, casting to the complex domain by means of the Hilbert Transform, and verifying the potential interest of CVNN even for non-complex data.
Incremental $(1-\epsilon)$-approximate dynamic matching in $O(poly(1/\epsilon))$ update time

In the dynamic approximate maximum bipartite matching problem we are given bipartite graph $G$ undergoing updates and our goal is to maintain a matching of $G$ which is large compared the maximum matching size $\mu(G)$. We define a dynamic matching algorithm to be $\alpha$ (respectively $(\alpha, \beta)$)-approximate if it maintains matching $M$ such that at all times $|M | \geq \mu(G) \cdot \alpha$ (respectively $|M| \geq \mu(G) \cdot \alpha - \beta$). We present the first deterministic $(1-\epsilon )$-approximate dynamic matching algorithm with $O(poly(\epsilon ^{-1}))$ amortized update time for graphs undergoing edge insertions. Previous solutions either required super-constant [Gupta FSTTCS'14, Bhattacharya-Kiss-Saranurak SODA'23] or exponential in $1/\epsilon $ [Grandoni-Leonardi-Sankowski-Schwiegelshohn-Solomon SODA'19] update time. Our implementation is arguably simpler than the mentioned algorithms and its description is self contained. Moreover, we show that if we allow for additive $(1, \epsilon \cdot n)$-approximation our algorithm seamlessly extends to also handle vertex deletions, on top of edge insertions. This makes our algorithm one of the few small update time algorithms for $(1-\epsilon )$-approximate dynamic matching allowing for updates both increasing and decreasing the maximum matching size of $G$ in a fully dynamic manner.
How Effective is Multiple-Vantage-Point Domain Control Validation?

Multiple-vantage-point domain control validation (multiVA) is an emerging defense for mitigating BGP hijacking attacks against certificate authorities. While the adoption of multiVA is on the rise, little work has quantified its effectiveness against BGP hijacks in the wild. We bridge the gap by presenting the first analysis framework that measures the security of a multiVA deployment under real-world network configurations (e.g., DNS and RPKI). Our framework accurately models the attack surface of multiVA by 1) considering the attacks on DNS nameservers involved in domain validation, 2) considering deployed practical security techniques such as RPKI, 3) performing fine-grained internet-scale analysis to compute multiVA resilience (i.e., how difficult it is to launch a BGP hijack against a domain and get a bogus certificate under multiVA). We use our framework to perform a rigorous security analysis of the multiVA deployment of Let's Encrypt, using a dataset that consists of about 1 million certificates and 31 billion DNS queries collected over four months. Our analysis shows while DNS does enlarge the attack surface of multiVA, the of Let's Encrypt's multiVA deployment still offers an 88% median resilience against BGP hijacks, a notable improvement over 76% offered by single-vantage-point validation. RPKI, even in its current state of partial deployment, effectively mitigates BGP attacks and improves the security of the deployment by 15% as compared to the case without considering RPKI. Exploring 11,000 different multiVA configurations, we find that Let's Encrypt's deployment can be further enhanced to achieve a resilience of over 99% by using a full quorum policy with only two additional vantage points in different public clouds.
Trust-Region-Free Policy Optimization for Stochastic Policies

Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies in each iteration. The combination of the surrogate objective maximization and the trust region enforcement has been shown to be crucial to guarantee a monotonic policy improvement. However, solving a trust-region-constrained optimization problem can be computationally intensive as it requires many steps of conjugate gradient and a large number of on-policy samples. In this paper, we show that the trust region constraint over policies can be safely substituted by a trust-region-free constraint without compromising the underlying monotonic improvement guarantee. The key idea is to generalize the surrogate objective used in TRPO in a way that a monotonic improvement guarantee still emerges as a result of constraining the maximum advantage-weighted ratio between policies. This new constraint outlines a conservative mechanism for iterative policy optimization and sheds light on practical ways to optimize the generalized surrogate objective. We show that the new constraint can be effectively enforced by being conservative when optimizing the generalized objective function in practice. We call the resulting algorithm Trust-REgion-Free Policy Optimization (TREFree) as it is free of any explicit trust region constraints. Empirical results show that TREFree outperforms TRPO and Proximal Policy Optimization (PPO) in terms of policy performance and sample efficiency.
A Deep Dive Into the Computational Fidelity of High-Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems

Low energy barrier magnet (LBM) technology has recently been proposed as a candidate for accelerating algorithms based on energy minimization and probabilistic graphs because their physical characteristics have a one-to-one mapping onto the primitives of these algorithms. Many of these algorithms have a much higher tolerance for error compared to high-accuracy numerical computation. LBM, however, is a nascent technology, and devices show high sample-to-sample variability. In this letter, we take a deep dive into the overall fidelity afforded by this technology in providing computational primitives for these algorithms. We show, that while the computed results show finite deviations from zero-variability devices, the margin of error is almost always certifiable to a certain percentage. This suggests that LBM technology could be a viable candidate as an accelerator for popular emerging paradigms of computing.
InfoNCE Loss Provably Learns Cluster-Preserving Representations

The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e.g. the ``dogness'' of a dog, close to each other in the space generated by the representation. A common and successful approach for tackling this unsupervised learning problem is minimizing the InfoNCE loss associated with the training samples, where each sample is associated with their augmentations (positive samples such as rotation, crop) and a batch of negative samples (unrelated samples). To the best of our knowledge, it was unanswered if the representation learned by minimizing the InfoNCE loss preserves the underlying data clusters, as it only promotes learning a representation that is faithful to augmentations, i.e., an image and its augmentations have the same representation. Our main result is to show that the representation learned by InfoNCE with a finite number of negative samples is also consistent with respect to clusters in the data, under the condition that the augmentation sets within clusters may be non-overlapping but are close and intertwined, relative to the complexity of the learning function class.
Efficient Tomography of Non-Interacting-Fermion States

We give an efficient algorithm that learns a non-interacting fermion state, given copies of the state. For a system of $n$ non-interacting fermions and $m$ modes, we show that $O(m^3 n^2 \log(1/\delta) / \epsilon^4)$ copies of the input state and $O(m^4 n^2 \log(1/\delta)/ \epsilon^4)$ time are sufficient to learn the state to trace distance at most $\epsilon$ with probability at least $1 - \delta$. Our algorithm empirically estimates one-mode correlations in $O(m)$ different measurement bases and uses them to reconstruct a succinct description of the entire state efficiently.
$W$ boson mass shift, dark matter and $(g-2)_\ell$ in a scotogenic-Zee model

We present a singly charged scalar extension of the Scotogenic model, ScotoZee, which resolves the recently reported deviations in $W$ boson mass as well as lepton $g-2$. The model admits a scalar or a fermionic dark matter while realizing naturally small radiative neutrino masses. The mass splitting of $\sim 100$ GeV, required by the shift in $W$ boson mass, among the inert doublets fields can be evaded by its mixing with the singlet scalar, which is also key to resolving $(g-2)_\ell$ anomaly within $1\sigma$. We establish the consistency of this framework with dark matter relic abundance while satisfying constraints from charged lepton flavor violation, direct detection as well as collider bounds. The model gives predictions for the lepton flavor violating $\tau\to\ell\gamma$ processes testable in upcoming experiments.
Dynamo Action in Magnetohydrodynamics and Hall-Magnetohydrodynamics

The first direct numerical simulations of turbulent Hall dynamos are presented. The evolution of an initially weak and small-scale magnetic field in a system maintained in a stationary regime of hydrodynamic turbulence (by a stirring force at a macroscopic scale) is studied to explore the conditions for exponential growth of the magnetic energy. The Hall current is shown to have a profound effect on turbulent dynamo action; it can strongly enhance or suppress the generation of the large-scale magnetic energy depending on the relative values of the length scales of the system.
The main galaxy groups from the SDSS data release 5

Aims. We investigate the correlations between galaxy properties and environment. Methods. From the main galaxy sample of the SDSS Data Release 5, we have extracted 11 163 groups at the linking length R0 = 0.2 × ¯ −1/3 = 1.97 Mpc by the three-dimensional friends-of-friends (FOF) algorithm, where ¯ n is the mean galaxy density, and compare basic properties of member galaxies of main galaxy groups with those of the main galaxy sample and isolated galaxies in each redshift bin. Results. The luminosities and sizes of galaxies are almost independent of environment, but morphologies and colors are correlated with environment-especially galaxy morphologies.
An Image Retrieval Method Based on Hu Invariant Moment and Improved Annular Histogram

Image retrieval methods based on annular histogram of feature points are calculation efficient, invariant to image rotation and translation transform. However, these methods have two main disadvantages. One is that the annular histogram can’t describe the spatial distribution of feature points accurately, thus different images may have similar annular histogram. Another one is the methods based on feature points cannot describe the shape information of object. These disadvantages affect the retrieval accuracy to a certain extent. In this paper, an image retrieval method based on Hu invariant moments and improved annular histogram is proposed. Firstly, edge of image is detected and feature points are calculated based on the edge curvature. Then, image features are described based on both the edge and points. Annular histogram combined with standard deviation ellipse method is used to describe the spatial distribution of feature points. Hu invariant moment of the edge is used to represent the object’s shape information. Finally, the similarity is measured based on both the point feature and the shape feature. Experiment results show that the proposed method can improve the image retrieval precision effectively. DOI: http://dx.doi. org/10.5755/j01.eee. 19.10. 3925
Do funds prefer longitudinal persistence for high and low earnings?

ABSTRACT This study examines whether securities investment fund ownership correlates with longitudinal persistence for high and low earnings. Using longitudinal data analysis methods, including a Cox proportional hazards regression model and a hierarchical linear regression model, we obtain two main findings: (1) For a low level of earnings, fund ownership not only reduces the likelihood of earnings decline but also increases the likelihood of an upward trend in earnings; (2) For a high level of earnings, fund ownership reduces not only the likelihood of earnings decline but also the likelihood of a downward trend in earnings. Our study provides evidence that funds emphasize firms’ long-term earnings persistence when they choose stocks.
Interpolation of electromagnetic fields from nonuniform samples

A nonuniform sampling algorithm for practical applications must be computationally manageable, stable, and accurate from the point of view of truncation errors. It is more advisable to recover the uniform samples from the irregularly spaced ones than to resort to a direct interpolation formula. Accordingly, once the uniform samples have been determined, the function values can be efficiently recovered by means of the optimal sampling expansions of central type, which minimize the truncation error for a given number of retained samples. Therefore, the main problem is the development of an efficient and stable algorithm to reconstruct the uniform sample values from the irregularly spaced ones. An algorithm is developed for the 1D case; it can be extended to any 2D case for which a uniform sampling representation is available. To test the algorithm performance, a set of numerical experiments has been carried out relevant to the reconstruction of the electric field radiated by a nonfocusing planar circular array symmetric with respect to the x axis. <<ETX>>
On the number of antichains in multilevelled ranked posets

The asymptotics of the number of antichains in some multilevelled ranked posets is obtained. As a consequence, the asymptotics of the number of monotone Boolean functions, the number of selfdual monotone Boolean functions, the number of monotone fuzzy (0,l)-functions, the number of Boolean functions possessing the property < A^ > are obtained.
New Approach for Stage–Discharge Relationship: Gene-Expression Programming

This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modeling stage–discharge relationship. The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models.
Using silhouette coherence for 3D image-based object modeling under circular motion

We present a new concept of silhouette coherence applied to a set of silhouettes generated by a 3D object. The proposed silhouette coherence concept is used in the problem of camera calibration for image-based 3D object modeling under circular motion. We ﬁrst discuss the silhouette coherence notion and deﬁne a practical criterion to estimate it. This criterion depends both on the silhouettes and on the parameters of the cameras that generated them. These parameters can be optimized for the 3D modeling problem by maximizing the overall silhouette coherence. We present various experimental results illustrating the efﬁciency of the proposed silhouette coherence criterion.
Being innovative, fun, and green? Hedonic and environmental motivations in the use of green innovations

ABSTRACT This paper seeks to determine the decision-making route relating to hedonic and environmental motivation in green innovation adoption and to show how two similar green innovations can motivate consumers differently. It also aims to determine the effect of domain-specific innovativeness (DSI) on emotions and green identity on environmental motivations. The paper focuses on two types of green transport innovations: shared e-bikes and e-scooters. Four models were tested using structural equation modelling based on survey data from 800 shared e-bike and e-scooter users. The results reveal that the decision to use shared e-bikes follows a cognitive route, while shared e-scooter use follows an affective route. Additionally, findings show that DSI significantly affects positive emotions in the use of both shared microvehicles. However, green identity only impacts the environmental motivations in shared e-bike use.
PhD Abstract

It has been proposed that the southernmost coast of South Africa was a uniquely important region in later human evolution, in part due to the abundance of nutritious and dense coastal resources. Yet, strong contrasts between its Middle Stone Age (MSA) and subsequent Later Stone Age (LSA) archaeological records cast uncertainty on the significance of shellfish utilisation for processes of human evolution. Furthermore, climatic shifts are frequently advanced as drivers of widespread technological change, but few climate proxy records correspond adequately to the archaeological evidence at a temporal or spatial level to allow them to be unambiguously linked to human cultural activity. This thesis presents a temporally and spatially distributed record of near-shore seasonal sea surface temperatures (SSTs) from serial δO shell measurements of archaeological Turbo sarmaticus opercula. In addition, the annual timing of shellfish harvesting captured in the temperature signal of the shells’ final few growth increments is indicative of hunter-gatherers’ scheduling of foraging behaviours and of their social organisation. A non-destructive method for evaluating the preservation of the original aragonitic shell carbonate using Fourier transform infrared spectrometry with an attenuated reflectance attachment is presented, allowing for robust SST reconstructions. Near-shore SST reconstructions are presented from the LSA levels of Nelson Bay Cave and Byneskranskop 1, along with new radiocarbon chronologies spanning the terminal Pleistocene and the Holocene, and from the MSA deposits at Pinnacle Point 5–6 and Klasies River Main site, dated to periods in Marine Isotope Stage (MIS) 5 and across the MIS5/4 transition. These reconstructions confirm the utility of near-shore SST records for terrestrial climate reconstructions, indicating shifts in summer rainfall during the Holocene and across MIS5/4. The annual timing of shellfish foraging is shown to be markedly different between the MSA and LSA, permitting more nuanced assessments of foraging behaviours within the MSA and LSA.
Disruption of the Aspergillus fumigatus RNA interference machinery alters the conidial transcriptome

The RNA interference (RNAi) pathway has evolved numerous functionalities in eukaryotes, with many on display in Kingdom Fungi. RNAi can regulate gene expression, facilitate drug resistance, or even be altogether lost to improve growth potential in some fungal pathogens. In the WHO fungal priority pathogen, Aspergillus fumigatus, the RNAi system is known to be intact and functional. To extend our limited understanding of A. fumigatus RNAi, we first investigated the genetic variation in RNAi-associated genes in a collection of 217 environmental and 83 clinical genomes, where we found that RNAi components are conserved even in clinical strains. Using endogenously expressed inverted-repeat transgenes complementary to a conditionally essential gene (pabA) or a nonessential gene (pksP), we determined that a subset of the RNAi componentry is active in inverted-repeat transgene silencing in conidia and mycelium. Analysis of mRNA-seq data from RNAi double-knockout strains linked the A. fumigatus dicer-like enzymes (DclA/B) and RNA-dependent RNA polymerases (RrpA/B) to regulation of conidial ribosome biogenesis genes; however, surprisingly few endogenous small RNAs were identified in conidia that could explain this broad change. Although RNAi was not clearly linked to growth or stress response defects in the RNAi knockouts, serial passaging of RNAi knockout strains for six generations resulted in lineages with diminished spore production over time, indicating that loss of RNAi can exert a fitness cost on the fungus. Cumulatively, A. fumigatus RNAi appears to play an active role in defense against double-stranded RNA species alongside a previously unappreciated housekeeping function in regulation of conidial ribosomal biogenesis genes.
From Cell-Lines to Cancer Patients: Personalized Drug Synergy Prediction

Combination drug therapies are effective treatments for cancer. However, the genetic heterogeneity of the patients and exponentially large space of drug pairings pose significant challenges for finding the right combination for a specific patient. Current in silico prediction methods can be instrumental in reducing the vast number of candidate drug combinations. However, existing powerful methods are trained with cancer cell line gene expression data, which limits their applicability in clinical settings. While synergy measurements on cell lines models are available at large scale, patient-derived samples are too few to train a complex model. On the other hand, patient-specific single-drug response data are relatively more available. In this work, we propose a deep learning framework, Personalized Deep Synergy Predictor (PDSP), that enables us to use the patient-specific single drug response data for customizing patient drug synergy predictions. PDSP is first trained to learn synergy scores of drug pairs and their single drug presonses for a given cell line using drug structures and large scale cell line gene expression data. Then, the model is fine-tuned for patients with their patient gene expression data and associated single drug response measured on the patient ex vivo samples. In this study, we evaluate PDSP on data from three leukemia patients and observe that it improves the prediction accuracy by 27% compared to models trained on cancer cell line data. PDSP is built and available at https://github.com/hikuru/PDSP