Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling colloid transport and retention in saturated porous media under unfavorable attachment conditions

A mathematical model is presented for colloid transport and retention in saturated porous media under unfavorable attachment conditions. The model accounts for colloid transport in the bulk aqueous phase and adjacent to the solid surface, and rates of colloid collision, interaction, release, and immobilization on the solid phase. Model parameters were estimated using (1) filtration theory; (2) calculated interaction energies in conjunction with the Maxwellian kinetic energy model of diffusion; (3) information about the velocity magnitude and distribution adjacent to the solid phase that was obtained from pore scale water flow simulations; (4) colloid and collector sizes; (5) the balance of applied hydrodynamic and resisting adhesive torques; and (6) time dependent filling of retention locations using a Langmuirian approach. The presented theory constrains the model parameters and output to physically realistic values in many instances, and minimizes the need for parameter optimization. Example simulations demonstrate that our modeling formulation is qualitatively consistent with observed trends for retention with colloid size and concentration, grain size, and velocity for many systems. The model provides a clear conceptual explanation for the causes of hyperexponential, exponential, uniform, and nonmonotonic retention profiles without invoking hypotheses with regard to colloid heterogeneity, aggregation, or multiple deposition rates. Furthermore, the model formulation and research presented herein helps to identify areas where additional research and theory development are still needed.
Two-dimensional nanoporous molecular networks: growth, dynamics and electron confinement

Fabrication of nanoarchitectures, understanding of their properties and control over them represents a major challenge and requires knowledge of solid state physics, supramolecular chemistry and material sciences. In this dissertation three important aspects of two-dimensional, surface-confined nanoporous networks are examined: dynamics, growth and electron confinement. Bi-molecular networks consisting of sexiphenyl-dicarbonitrile and N,N-diphenyl oxalic amide, which are coupled via two different bonding motifs of different strength, were found to exhibit intriguing dynamic behaviour: the one-dimensional (1D) diffusion of the molecules of the weaker bond sublattice is guided by the more stable multimolecular superstructure. Furthermore, threefold symmetric de novo synthesized molecules with terminal alkynes were investigated. For the first time it was shown how these building blocks can be linked by covalent bonding into nanoporous structures in two dimensions. Both the diffusion in bi-component networks and the formation of covalently bound templates are rationalized by computational modeling. A systematic study of the electron confinement and analysis of the band structures for different systems is also presented and an insight into the origin of the spectral line shapes could be gained.
Safety attributes of lactic acid bacteria isolated from fermented sausages.

Lactic acid bacteria (LAB) were isolated during the production and the ripening of Greek dry fermented sausages. Samples were taken at different stages, and 150 "wild' strains were isolated. The majority of the strains isolated were assigned to the species Lactobacillus plantarum biotype (1) (43.3 %) followed by Lb. curvatus, Lb. pentosus (10.7 %), Lb. brevis biotype (1) (8.7 %), Lactococcus lactis subsp. lactis biotype (1) (6.7 %) and Leuconostoc mesenteroides subsp. mesenteroid.es biotype (2) (5.3 %). The possibility of bacteriocin production was tested using the agar well diffusion assay (AWDA). One strain was found to produce bacteriocin (Leuconostoc mesenteroides E131) and its purification was attempted using precipitation with ammonium sulphate, cation exchange, and reverse phase chromatography. Moreover, the purification of curvaticin L442 was attemped, a bacteriocin produced by Lactobacillus curvatus L442, isolated from Greek traditional fermented sausage prepared without addition of starters. The bacteriocin was purified by 50% ammonium sulphate precipitation, cation exchange, reverse phase and gel filtration chromatography. Lb sakei I154, a bacteriocin producing strain isolated from Italian fermented sausage, and the semi-purified bacteriocin from Leuconostoc mesenteroides E131 were validated via industrial trials to evaluate whether the product (fermented sausages) maintains the technological characteristics and the traditional quality characteristics. Three fermentations under controlled conditions were conducted and at the end of these fermentations, products were sliced and packaged under controlled atmosphere (80% N2 + 20% CO2) and stored at 4+/-2 °C for 12 weeks to determine the shelf life of the product. Finally, from the previous industrial trials the proper production parameters were determined as well as the most effective packaging techniques, resulting in the conduction of a Standard Operation Procedures Guide concerning the whole production of traditional fermented sausages.
An Accountability Framework for Financial Statement Auditors and Related Research Questions

We address the following overarching questions: What kind of accountability framework could regulators use to (a) motivate auditors to improve audit quality, and (b) evaluate how well auditors have carried out their duties? We draw on research in accounting, economics, psychology, and neuroscience to critique the accountabilities, incentives, and learning opportunities embedded in auditors’ extant regulatory environment. We first establish that forward-looking estimates are the basis for most financial statement information and that some of these estimates are highly uncertain, which increases the challenges faced by auditors. We propose an accountability framework with two dimensions: rewards versus penalties and processes versus outcomes. We show that auditors’ current regulatory accountabilities generally are in the form of penalties rather than rewards and primarily depend on audit outcomes rather than attributes of auditors’ judgment processes. We provide evidence from a range of disciplines that questions the suitability of the present system for improving the quality of auditors’ judgments and the quality of evaluations of those judgments made by inspectors. We identify four potential changes for improvement in audit quality based on our framework. Each of these identified changes has an impact on one or both of the two dimensions in our framework. For each of these changes, we outline JDM research questions that could be addressed to inform our overarching questions and to provide empirical evidence to help refine our accountability framework and improve audit quality.
Room Classification on Floor Plan Graphs using Graph Neural Networks

We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the map. We experiment with House-GAN dataset that consists of floor plan maps in vector format and train multilayer perceptron and graph neural networks. Our results show that graph neural networks, specifically GraphSAGE and Topology Adaptive GCN were able to achieve accuracy of 80% and 81% respectively outperforming baseline multilayer perceptron by more than 15% margin.
Online Fairness-Aware Learning with Imbalanced Data Streams

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they"reject"minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).
Observational Evidence Points at AGB Stars as Possible Progenitors of CEMP-s and CEMP-r/s Stars

The origin of enhanced abundance of heavy elements observed in the surface chemical composition of carbon-enhanced metal-poor (CEMP) stars still remains poorly understood. Here, we present detailed abundance analysis of seven CEMP stars based on high-resolution (R ∼ 50,000) spectra that reveal enough evidence of asymptotic giant branch (AGB) stars being possible progenitors for these objects. For the objects HE 0110−0406, HE 1425−2052, and HE 1428−1950, we present for the first time a detailed abundance analysis. Our sample is found to consist of one metal-poor ([Fe/H] < −1.0) and six very metal-poor ([Fe/H] < −2.0) stars with enhanced carbon and neutron-capture elements. We have critically analyzed the observed abundance ratios of [O/Fe], [Sr/Ba], and [hs/ls] and examined the possibility of AGB stars being possible progenitors. The abundance of oxygen estimated in the program stars is characteristic of AGB progenitors except for HE 1429−0551 and HE 1447+0102. The estimated values of [Sr/Ba] and [hs/ls] ratios also support AGB stars as possible progenitors. The locations of the program stars in the absolute carbon abundance A(C) versus [Fe/H] diagram, along with the Group I objects, hint at the binary nature of the object. We have studied the chemical enrichment histories of the program stars based on abundance ratios [Mg/C], [Sc/Mn], and [C/Cr]. Using [C/N] and 12C/13C ratios, we have examined whether any internal mixing had modified their surface chemical compositions. Kinematic analysis shows that the objects HE 0110−0406 and HE 1447+0102 are thick-disk objects and the remaining five objects belong to the halo population of the Galaxy.
QED effects on Kerr black hole shadows immersed in uniform magnetic fields

In this work, taking the QED effect into account, we investigate the shadows of the Kerr black holes immersed in uniform magnetic fields through the numerical backward ray-tracing method. We introduce a dimensionless parameter $\Lambda$ to characterize the strength of magnetic fields and studied the influence of magnetic fields on the Kerr black hole shadows for various spins of the black holes and inclination angles of the observers. In particular, we find that the photon"hairs"appear near the left edge of the shadow in the presence of magnetic fields. The photon hairs may be served as a signature of the magnetic fields. We notice that the photon hairs become more evident when the strength of magnetic fields or the spin of the black hole becomes larger. In addition, we study the deformation of the shadows by bringing in quantitative parameters that can describe the position and shape of the shadow edge.
Statistical Learning in Speech: A Biologically Based Predictive Learning Model

Infants, adults, non-human primates and non-primates all learn patterns implicitly, and they do so across modalities. The biological evidence supports the hypothesis that the mechanism for this learning is general but computationally local. We hypothesize that the mechanism itself is predictive error-driven learning. We build on recent work that advanced a biologically plausible model of error backpropagation learning which proposes that higher order thalamic nuclei provide a locale for a temporal difference between top-down predictions and an actual event outcome. Our neural network based on that work also models the auditory cortex hierarchy of core, belt and parabelt and the caudal-rostral axis within regions. We simulated two studies showing statistical learning in infants, a seminal study using synthesized speech and a more recent study using human speech. Before simulating these studies the network was trained on spoken sentences from the TIMIT corpus to emulate infant's experience listening to random speech. The implemented neural network, learning only by predicting the next brief speech segment, learned in both simulations to predict in-word syllables better than next-word syllables showing that prediction could be the basis for word segmentation and thus statistical learning.
Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage

Cloud computing provides a powerful yet low-cost environment for distributed deep learning workloads. However, training complex deep learning models often requires accessing large amounts of data, which can easily exceed the capacity of local disks. Prior research often overlooks this training data problem by implicitly assuming that data is available locally or via low latency network-based data storage. Such implicit assumptions often do not hold in a cloud-based training environment, where deep learning practitioners create and tear down dedicated GPU clusters on demand, or do not have the luxury of local storage, such as in serverless workloads. In this work, we investigate the performance of distributed training that leverages training data residing entirely inside cloud storage buckets. These buckets promise low storage costs, but come with inherent bandwidth limitations that make them seem unsuitable for an efficient training solution. To account for these bandwidth limitations, we propose the use of two classical techniques, namely caching and pre-fetching, to mitigate the training performance degradation. We implement a prototype, DELI, based on the popular deep learning framework PyTorch by building on its data loading abstractions. We then evaluate the training performance of two deep learning workloads using Google Cloud's NVIDIA K80 GPU servers and show that we can reduce the time that the training loop is waiting for data by 85.6%-93.5% compared to loading directly from a storage bucket-thus achieving comparable performance to loading data directly from disk-while only storing a fraction of the data locally at a time. In addition, DELI has the potential of lowering the cost of running a training workload, especially on models with long per-epoch training times.
Comptes Rendus Mathématique

Let K be an imaginary quadratic ﬁeld where p splits. We study signed Selmer groups for non-ordinary modular forms over the anticyclotomic (cid:90) p -extension of K , showing that one inclusion of an Iwasawa main conjecture involving the p -adic L -function of Bertolini–Darmon–Prasanna implies that their µ -invariants vanish. This gives an alternative method to reprove a recent result of Matar on the vanishing of the µ -invariants of plus and minus signed Selmer groups for elliptic curves
Highly Versatile Broadband RF Photonic Fractional Hilbert Transformer Based on a Kerr Soliton Crystal Microcomb

We demonstrate an RF photonic fractional Hilbert transformer based on an integrated Kerr micro-comb source featuring a record low free spectral range of 48.9 GHz, yielding 75 microcomb lines across the C-band. By programming and shaping the comb lines according to calculated tap weights, we demonstrate that the Hilbert transformer can achieve tunable bandwidths ranging from 1.2 to 15.3 GHz, switchable centre frequencies from baseband to 9.5 GHz, and arbitrary fractional orders. We experimentally characterize the RF amplitude and phase response of the tunable bandpass and lowpass Hilbert transformers with 90 and 45-degree phase shift. The experimental results show good agreement with theory, confirming the effectiveness of our approach as a powerful way to implement the standard as well as fractional Hilbert transformers with broad and switchable processing bandwidths and centre frequencies, together with high reconfigurability and greatly reduced size and complexity.
FedPara: Low-rank Hadamard Product Parameterization for Efficient Federated Learning

To overcome the burdens on frequent model uploads and downloads during federated learning (FL), we propose a communication-efﬁcient re-parameterization, FedPara . Our method re-parameterizes the model’s layers using low-rank matrices or tensors followed by the Hadamard product. Different from the conventional low-rank parameterization, our method is not limited to low-rank constraints. Thereby, our FedPara has a larger capacity than the low-rank one, even with the same number of parameters. It can achieve comparable performance to the original models while requiring 2.8 to 10.1 times lower communication costs than the original models, which is not achievable by the traditional low-rank parameterization. Moreover, the efﬁciency can be further improved by combining our method and other efﬁcient FL techniques because our method is compatible with others. We also extend our method to a personalized FL application, pFedPara , which separates parameters into global and local ones. We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters.
Optically controllable magnetism in atomically thin semiconductors

We report evidence that ferromagnetic order in electrostatically doped, monolayer transition metal dichalcogenide (TMD) semiconductors can be stabilized and controlled at zero magnetic field by local optical pumping. We use circular dichroism (CD) in reflectivity from excitonic states as a spatially resolved probe of charge-carrier spin polarization. At electron densities ne ~ 1012 cm−2, a diffraction-limited, circularly polarized optical pump breaks symmetry between oppositely polarized magnetic states and stabilizes long-range magnetic order, with carrier polarization exceeding 80% over an 8 μm by 5 μm extent. In time-resolved measurements with pulsed optical excitation, we observe that magnetic interactions amplify the initial pump-induced spin polarization by more than an order of magnitude. The optical control of magnetism with local optical pumps will unlock advancements in spin and optical technologies and provides a versatile tool in the study of correlated phases in two-dimensional electron gases.
MINIMAL-MASS BLOW-UP SOLUTIONS FOR NONLINEAR SCHRÖDINGER EQUATIONS WITH GROWTH POTENTIALS

i ∂u ∂t +∆u+ |u| 4 N u− V u− Uu+ ω1|x| 1u− ω2|x| 2u− ξ · x|x|3u = 0 in R . From the classical argument, the solution with subcritical mass (‖u‖2 < ‖Q‖2) is global and bounded in X1, where X1 is the domain of −∆ + U + ω2|x|2 in L(R ) and Q is the ground state of the mass-critical problem. Therefore, we are interested in the existence and behaviour of blow-up solutions for the threshold (‖u‖2 = ‖Q‖2). Previous studies investigate the existence and behaviour of the critical-mass blow-up solution when the cases of algebraically tractable growth potential or no growth potential. In this paper, we construct a critical-mass blow-up solution for the equation with growth potentials which has no algebraic property.
An Analytical Stochastic Dynamic Programming Algorithm for Energy Storage Price Arbitrage

This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage. We formulate the price arbitrage problem using stochastic dynamic programming and model real-time prices as a Markov process. Then we propose an analytical solution algorithm using a piecewise linear approximation of the valueto-go function. Our method does not discretize control and state of the energy storage. Our solution algorithm achieves extreme computation performance and solves the proposed arbitrage problem for one operating day in less than one second on a personal computer. We demonstrate our approach using historical price data from four price zones in New York Independent System Operator, with case studies comparing the performance of different stochastic models and storage settings. Our results show that the proposed method captures 50% to 90% of arbitrage profit compared to perfect price forecasts. In particular, our method captures more than 80% of arbitrage profit in three out of the four price zones when considering batteries with more than two-hour duration and realistic degradation cost.
On Single and Multiple Representations in Dense Passage Retrieval

The advent of contextualised language models has brought gains in search effectiveness, not just when applied for re-ranking the output of classical weighting models such as BM25, but also when used directly for passage indexing and retrieval, a technique which is called dense retrieval. In the existing literature in neural ranking, two dense retrieval families have become apparent: single representation, where entire passages are represented by a single embedding (usually BERT's [CLS] token, as exemplified by the recent ANCE approach), or multiple representations, where each token in a passage is represented by its own embedding (as exemplified by the recent ColBERT approach). These two families have not been directly compared. However, because of the likely importance of dense retrieval moving forward, a clear understanding of their advantages and disadvantages is paramount. To this end, this paper contributes a direct study on their comparative effectiveness, noting situations where each method under/over performs w.r. t. each other, and w.r. t. a BM25 baseline. We observe that, while ANCE is more efficient than ColBERT in terms of response time and memory usage, multiple representations are statistically more effective than the single representations for MAP and MRR@10. We also show that multiple representations obtain better improvements than single representations for queries that are the hardest for BM25, as well as for definitional queries, and those with complex information needs.
Dark sector production via proton bremsstrahlung

Experiments using proton beams at high luminosity colliders and fixed target facilities provide impressive sensitivity to new light weakly coupled degrees of freedom. With these experiments in mind, we revisit the production of dark vectors and scalars via proton bremsstrahlung, making use of a model that describes the underlying nucleon scattering cross-section in the forward direction due to pomeron exchange. We compare the resulting distributions and rates with those obtained via variants of the Fermi-Weizsacker-Williams approximation, and provide production rate distributions for a range of beam energies, including those relevant for the proposed Forward Physics Facility at the High Luminosity-LHC.