Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Temporal Variations in Base Flow Index Using Sporadic River Data: Application to the Bua Catchment, Malawi

This study investigated how sporadic river datasets could be used to quantify temporal variations in the base flow index (BFI). The BFI represents the baseflow component of river flow which is often used as a proxy indicator for groundwater discharge to a river. The Bua catchment in Malawi was used as a case study, whereby the smoothed minima method was applied to river flow data from six gauges (ranging from 1953 to 2009) and the Mann-Kendall (MK) statistical test was used to identify trends in BFI. The results showed that baseflow plays an important role within the catchment. Average annual BFIs > 0.74 were found for gauges in the lower reaches of the catchment, in contrast to lower BFIs < 0.54 which were found for gauges in the higher reaches. Minimal difference between annual and wet season BFI was observed, however dry season BFI was >0.94 across all gauges indicating the importance of baseflow in maintaining any dry season flows. Long term trends were identified in the annual and wet season BFI, but no evidence of a trend was found in the dry season BFI. Sustainable management of the investigated catchment should, therefore, account for the temporal variations in baseflow, with special regard to water resources allocation within the region and consideration in future scheme appraisals aimed at developing water resources. Further, this demonstration of how to work with sporadic river data to investigate baseflow serves as an important example for other catchments faced with similar challenges.
Wenlockian (Silurian) conodont biostratigraphy, depositional environments, and depositional history along the eastern flank of the Cincinnati Arch in southern Ohio

Representatives of 12 conodont species, including Oulodus equirectus n. sp. , have been recovered from the Silurian Lilley Formation, Lilley–Peebles transition unit, and Peebles Dolomite at six localities in southern Ohio. The Lilley, Lilley–Peebles transition unit, and Peebles belong in the Kockelella ranuliformis–K. amsdeni to Ozarkodina? crassa Chronozone and are late early to middle Wenlockian in age. The Lilley is conformable and transitional with the underlying Bisher Formation and overlying Peebles. The unconformity between the upper Niagaran Peebles and overlying lower Salinan Greenfield Dolomite represents middle Wenlockian to late Ludlovian or early Pridolian time. The Lilley, Lilley–Peebles transition unit, and Peebles were deposited in a gradually shoaling sea that was controlled by eustatic sea-level fall during the late early to middle Wenlockian. Uplift along the Cincinnati Arch, probably due to northward tilting of the arch, began during/after deposition of the Peebles in the middle Wenlockian. The combined effects of uplift and eustatic sea-level fall caused the sea to withdraw from southern Ohio. The Peebles was exposed and eroded prior to return of the sea to begin Greenfield deposition in the late Ludlovian or Pridolian (Ozarkodina remscheidensis Chronozone).
Analysis of ﬁnite sample size quantum hypothesis testing via martingale concentration inequalities

Martingale concentration inequalities constitute a powerful mathematical tool in the analysis of problems in a wide variety of ﬁelds ranging from probability and statistics to information theory and machine learning. Here we apply such inequalities to ﬁnite sample size quantum hypothesis testing, which is the problem of discriminating quantum states belonging to two diﬀerent sequences { ρ n } n and { σ n } n , for a ﬁnite n . We obtain upper bounds on the type II Stein-and Hoeﬀding errors, which, for i.i. d. states, are in general tighter than the corresponding bounds obtained by Audenaert, Mosonyi and Verstrate in [4]. Moreover, our bounds are also valid for sequences of non-i.i. d. states which satisfy certain conditions. We also use a martingale concentration inequality to obtain an upper bound on the second order asymptotics of the type II error exponent in the setting of quantum Stein’s lemma, for certain classes of states.
In silico Study to Identification of Potential SARS-CoV-2 Main Protease Inhibitors: Virtual Drug Screening and Molecular Docking with AutoDock Vina and Molegro Virtual Docker

Coronavirus disease 2019 (COVID-19) has emerged in Wuhan, China, and because of fast transmission, it has led to its extensive prevalence in almost all countries, which has made it a global crisis. Drug repurposing is considered a fast way to discover new applications of the current drugs. This study aims to recognize a possible small molecule as a primary protease inhibitor versus the main protease protein of SARS-CoV-2 by computational programs. Virtual screening procedures like using Molegro Virtual Docker, AutoDock Tool, and AutoDock Vina, were done for more than 1600 FDA-approved medicines downloaded from the ZINC database, were employed to characterize new implied molecule inhibitors for the recently published crystal structure of the main protease protein of SARS-CoV-2. Virtual screening results indicated, many drugs including ARBs, cephalosporins, some kinase inhibitors, HMG CoA reductase, and leukotriene receptor antagonist, may inhibit the main protease of SARS-COV-2. Velpatasvir, Molnupiravir, and Ivermectin were selected by virtual screening methods for further studies to find an efficient ligand for the treatment of COVID-19. Due to some other beneficial features, including anti-infectious, anti-inflammatory properties, and ADME profile, they could be a promising drug nominee for repurposing to the treatment of COVID-19. Velpatasvir was selected by some virtual screening methods for further studies to find a suitable ligand for the treatment of COVID-19. Furthermore, more studies need to approve this data and finally clinical trial needs to be done to examine the efficacy of Velpatasvir for the treatment of covid-19 as an anti-viral agent.
Action potential propagation and synchronisation in myelinated axons

With the advent of advanced MRI techniques it has become possible to study axonal white matter non-invasively and in great detail. Measuring the various parameters of the long-range connections of the brain opens up the possibility to build and refine detailed models of large-scale neuronal activity. One particular challenge is to find a mathematical description of action potential propagation that is sufficiently simple, yet still biologically plausible to model signal transmission across entire axonal fibre bundles. We develop a mathematical framework in which we replace the Hodgkin-Huxley dynamics by a leaky integrate-and-fire model with passive sub-threshold dynamics and explicit, threshold-activated ion channel currents. This allows us to study in detail the influence of the various model parameters on the action potential velocity and on the entrainment of action potentials between ephaptically coupled fibres without having to recur to numerical simulations. Specifically, we recover known results regarding the influence of axon diameter, node of Ranvier length and internode length on the velocity of action potentials. Additionally, we find that the velocity depends more strongly on the thickness of the myelin sheath than was suggested by previous theoretical studies. We further explain the slowing down and synchronisation of action potentials in ephaptically coupled fibres by their dynamic interaction. In summary, this study presents a solution to incorporate detailed axonal parameters into a whole-brain modelling framework. Author summary With more and more data becoming available on white-matter tracts, the need arises to develop modelling frameworks that incorporate these data at the whole-brain level. This requires the development of efficient mathematical schemes to study parameter dependencies that can then be matched with data, in particular the speed of action potentials that cause delays between brain regions. Here, we develop a method that describes the formation of action potentials by threshold activated currents, often referred to as spike-diffuse-spike modelling. A particular focus of our study is the dependence of the speed of action potentials on structural parameters. We find that the diameter of axons and the thickness of the myelin sheath have a strong influence on the speed, whereas the length of myelinated segments and node of Ranvier length have only a marginal effect. In addition to examining single axons, we demonstrate that action potentials between nearby axons can synchronise and slow down their propagation speed.
Molt and breeding ecology of black brant at the Tutakoke River colony

My dissertation addresses two fundamental components of avian biology: nesting ecology and molt dynamics. After the General Introduction (Chapter 1), the second chapter aims to describe body mass dynamics of brood rearing female brant. In geese, molt follows the most energetically demanding period in the annual cycle, breeding. Our goal in this chapter is to assess the competing hypotheses: (1) mass dynamics during molt represent adaptive mass loss versus (2) mass dynamics reflect environmental constraint on the nutrient balance during molt. We used 16 years of data on Black Brant (Branta bernicla nigricans; hereafter brant) at the Tutakoke River Colony (TRC) in western Alaska, USA, during which we recorded mass and molt stage to assess the two hypotheses for mass dynamics during molt. We used growth rates of goslings accompanying their parents as an index of nutrient availability. Body mass at the beginning and end of molt varied substantially among years as did the rate of mass gain during the molt. Both results are inconsistent with the hypothesis that female brant have evolved to achieve a target body mass at the end of the molt. Our finding that rate of mass gain during the molt was positively associated with growth rates of goslings across years is consistent with the hypothesis that nutrient availability to molting females influenced their rate of mass gain. There are many disadvantages of living in high densities of conspecifics including greater rates of disease and parasite transmission, increased competition for food, increased aggressive interactions with conspecifics and greater rates of extra-pair
An investigation into the simultaneous impact of climate change and land use modification on a tri-trophic species interaction

4 Poppy Lakeman-Fraser Three principal findings emerge from this investigation. Firstly, although climate and habitat fragmentation exhibit a mixture of effects on biota, when they interact, the net effects are all seemingly negative: abundance is reduced and the frequency of biotic interactions decrease. Secondly, these drivers combine in a non-additive fashion depending upon the trophic level and biotic measure assessed: antagonistically interacting to impact the plant species and species interactions and synergistically interacting to impact the insect species and abundance. Thirdly, the influence of interspecific relationships on dynamics within this study system is ostensibly comparable to the impact of anthropogenic pressures. This research suggests that, where possible, it is vitally important to investigate all known simultaneous drivers of change, to specify which taxonomic unit is being studied and to integrate biotic interactions when predicting the impacts on biodiversity in a changing world.
Program your (custom) SIMD instruction set on FPGA in C++

Field Programmable Gate Arrays (FPGAs) are more and more becoming a viable option for implementing data processing pipelines as their computing capacity as well as the access bandwidth be-tween host and device memory continue to increase. Unfortunately, nowadays hardware description languages are still mainly used for programming FPGAs which implies major limitations. To tackle this issue, our paper shows that the general-purpose parallel processing architecture SIMD (Single Instruction Multiple Data) is a perfect match for FPGAs. With this specific architecture, we are able to consider an FPGA as SIMD processing unit and the necessary SIMD instruction set can now be implemented in C++. As we will present, this offers a lot of advantages if both software (SIMDified query processing) and hardware can be written consistently in C++.
Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition

Generative Adversarial Networks (GANs) have emerged as a powerful tool for generating high-fidelity content. This paper presents a new training procedure that leverages Neural Architecture Search (NAS) to discover the optimal architecture for image generation while employing the Maximum Mean Discrepancy (MMD) repulsive loss for adversarial training. Moreover, the generator network is compressed using tensor decomposition to reduce its computational footprint and inference time while preserving its generative performance. Experimental results show improvements of 34% and 28% in the FID score on the CIFAR-10 and STL-10 datasets, respectively, with corresponding footprint reductions of 14 × and 31 × compared to the best FID score method reported in the literature. The implementation code is available at: https://github.com/PrasannaPulakurthi/MMD-AdversarialNAS .
NL2SQL is a solved problem... Not!

The development of natural language (NL) interfaces for databases has been notably shaped by the rise of Large Language Models (LLMs), which provide an easy way to automate the translation of NL queries into structured SQL queries. While LLMs bring valuable technical advancements, this paper stresses that achieving Enterprise-Grade NL2SQL is still far from being resolved, necessitating extensive novel research in various domains. We present insights from two competing teams dedicated to delivering reliable enterprise-grade NL2SQL technology, shedding light on challenges faced in real-world applications, including handling complex schemata, dealing with ambiguity in natural language statements, and incorporating it in our benchmarking methodologies and responsible AI considerations. While this paper may raise more questions than it answers, its aim is to act as a catalyst for a fruitful discussion on the topic. Additionally, it provides a practical pathway for the community to develop enterprise-grade NL2SQL solutions.
Investigating the Security and Accessibility of Voyage Data Recorder Data using a USB attack

— Voyage Data Recorders (VDR) or 'black boxes' for ships hold critical navigational and sensor data that can be used as evidence in an investigation. These systems have proven extremely useful in determining the cause of several previous shipping accidents. Considering the importance of the VDR and the increasing number of cyber-attacks in the maritime sector, the likelihood of it being attacked is high. This paper examines the security and accessibility of VDR data through a malicious USB device. A USB device is used after a series of tests, detailed in this paper, found it to be a viable way to compromise a VDR system. Intensive penetration testing was performed on a VDR, and this paper presents the four key highlights from the authors’ tests. The results show that real-world VDR data might not be secure from an insider threat with little to no cyber knowledge, and future VDRs may open that up to more outsider attackers. For a device like VDR, where confidentiality, integrity and availability of data are critical, a cyber-attack could therefore lead to serious repercussions.
Observation of shallow anisotropy on local earthquake records at the Garner Valley, Southern California, downhole array

We study very shallow (<200 m) S-wave anisotropy effects on local earthquake seismograms recorded by a vertical array of accelerometers at Garner Valley (Southern California). Using seismic events recorded by sensors buried at depth in the crystalline bedrock (220 m), in weathered crystalline rocks (22 m), and in sediments (15, 6, and 0 m), we analyze the S-wave vertical travel times and polarizations at different depths. Hodograms plotted for horizontal components at S onset exhibit a clear linear and constant polarization independent of both earthquake location and mechanism. This alignment varies with depth and is N 325° ± 13° at 220 m while N 25° ± 20° at 22, 15, 6, and 0 m. (a) For the deep propagation (>220 m), the S-wave direction of polarization at 220 m is identical to the observation obtained on a crystalline outcrop at the KNW USGS stations, 7 km away. Studies conducted on this site concluded to a rock fabric anisotropy due to the alignment of minerals and/or microcracks along the lineation direction. (b) For the shallow (<220 m) propagation, we observe a N 0° ± 20° fast-axis anisotropy with an 8 ± 2% magnitude between 220 and 22 m. The associated Sfast-Sslow travel-time delays roughly correspond to half the dominant period of the signal, and the subsurface linear polarization is interpreted as a degenerated ellipse. The north ± 20° anisotropy fast axis coincides with the maximum horizontal compressive stress associated with the San Andreas fault system. The shallow anisotropy extension corresponds to slow velocities associated with altered granite. It suggests either a stress-induced anisotropy due to pore/crack deformation in an altered (e.g., hydrothermally) medium, or the superposition of two crystalline units having different lineation directions. These data show that in presence of slow 0- to 200-m subsurface layers, shallow anisotropy strongly affects the earthquake surface records in the usual frequency range, below 30 Hz, used in short-period seismology.
Population parameters and exploitation rate of Sarotherodon melanotheron melanotheron rüppell, 1852 (Cichlidae) in Lake Toho, Benin

Aspects of the population dynamics of Sarotherodon melanotheron melanotheron, which is a commercially important fish species in Lake Toho, were described based on 1558 specimens collected monthly between January and December 2012 from artisanal captures. The size at first maturity was estimated at 6.9 cm for males and 7.7 cm for females. The asymptotic length obtained by analyzing frequency data with FiSATII software (FAO-ICLARM) has been estimated at 21.5 cm. Further, the von Bertalanffy growth coefficient was 0.58 year-1 and the overall growth performance index was 2.43. The estimates of the total, natural and fishing mortalities were 1.95, 1.37 and 0.58 year -1 respectively. The current exploitation rate was 0.30 and as such, remains below the maximum exploitation rate which was 0.55 according to the selection ogive procedure. However, this value is superior to the exploitation rate at 50% of its unexploited biomass (E0.5 = 0.27). Therefore, a reduction of the current exploitation rate is needed by, at least, 10% to maintain a sufficient biomass. The size at first capture was estimated at 6.7 cm which is lower than the size at first maturity for both sexes, but especially for females. These parameters clearly reflect a current trend of overexploitation.
Commitment and Cyclic Strategies in Multi-Objective Games

Multi-objective games present a natural framework for studying strategic interactions between rational individuals concerned with more than one objective. We explore both the impact of commitment on the equilibria as well as the learning behaviour of agents in such games. It is well known that in single-objective normal-form games, committing to a future strategy can never be worse than the utility from a Nash equilibrium. We show that this property does not hold in multi-objective games. On the other hand, we are able to construct games in which commitment is beneficial for both players, highlighting the nuances that commitment introduces. Furthermore, we find that optimal commitment can induce the same joint-action distribution as a cyclic Nash equilibrium and show that such cyclic Nash equilibria may exist even when no Nash equilibrium exists. We evaluate these characteristics in a learning setting, to explore whether this behaviour can be expected in applications as well. We find that all proposed theorems can empirically be observed from the learning dynamics. In addition, we observe that commitment can lead to the same joint-action distribution as in a cyclic Nash equilibrium, but that it is not guaranteed when there are multiple best-responses for the follower.
Adiposity, in ﬂ ammation, and working memory: Evidence for a vicious cycle

Overweight and obesity constitute the ﬁ fth leading cause of preventable deaths worldwide. One pathway through which excess weight contributes to poor health outcomes is via in ﬂ ammatory activity and changes in cognitive processes. Prior theory has proposed a vicious cycle whereby obesity potentiates in ﬂ ammatory activity, which alters cognitive processes such as working memory, which in turn leads to a reduced ability to self-regulate and therefore manage weight. However, to date no longitudinal studies have examined this potential dynamic. In the current study, we addressed this gap by assessing the relations among fat mass, C-reactive protein (CRP), and working memory across time in a large sample of 8536 children followed through adolescence in the Avon Longitudinal Study of Parents and Children in the United Kingdom. Adiposity was quanti ﬁ ed via dual emission x-ray absorptiometry (DEXA) at ages 9 and 15.5 years old, and in ﬂ ammatory activity was indexed via circulating serum C-reactive protein (CRP) levels assessed with a high-sensitivity assay at those same ages. Working memory was assessed between these two time points, at age 10, permitting examination of the temporal relations between working memory, adiposity, and in ﬂ ammatory activity. As hypothesized, we found that fat mass predicted later poor working memory, and this association was statistically mediated by CRP. Further, we found that poor working memory predicted greater subsequent fat mass and CRP, and the link between working memory and subsequent CRP was partially mediated by fat mass. These results thus could be taken to suggest the existence of a vicious cycle of mutually amplifying adiposity, in ﬂ ammatory activity, and poor working memory over time.
Multi-agent RMax for Multi-Agent Multi-Armed Bandits

In this paper, we provide PAC bounds for best-arm identification in multi-agent multi-armed bandits (MAMABs), via an algorithm we call multi-agent RMax (MARMax). In a MAMAB, the reward structure is expressed as a coordination graph, i.e., the total team reward is a sum over local reward functions that are conditioned on the actions of a subset of the agents. Assuming that these local rewards functions are bounded, we derive a PAC( 𝛿,𝜀 ) learning algorithm that achieves an accuracy of 𝜀 relative to the maximum team reward, with a number of required samples at most linear in the size of the largest reward reward function with probability 1 − 𝛿 . Furthermore, we show that in practice MARMax performs much better than this theoretical upper bound on the sample complexity by an order of magnitude. We further improve on the empirical result by tempering the optimism used in MARMax, resulting in a new algorithm that we call MAVMax. We show empirically that MAVMax further cuts down the number of required samples by around 30% w.r. t. MARMax.