Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating Discrimination in Insurance with Wasserstein Barycenters

The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective. In this article, we first investigate why predictions are a necessity within the industry and why correcting biases is not as straightforward as simply identifying a sensitive variable. We then propose to ease the biases through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications.
Freeze-in at stronger coupling

Predictivity of many non-thermal dark matter (DM) models is marred by the gravitational production background. This problem is ameliorated in models with lower reheating temperature $T_R$, which allows for dilution of gravitationally produced relics. We study the freeze-in dark matter production mechanism in the thermal bath with the electroweak scale temperature. The process is Boltzmann-suppressed if the dark matter mass is above $T_R$. In this case, the coupling to the thermal bath has to be significant to account for the observed dark matter relic density. As a result, the direct DM detection experiments already probe such freeze-in models, excluding significant parts of parameter space. The forthcoming experiments will explore this framework further, extending to lower couplings and higher reheating temperatures.
Self-doped flat band and spin-triplet superconductivity in monolayer 1T-TaSe$_{2-x}$Te$_{x}$

Two-dimensional van der Waals materials have become an established platform to engineer flat bands which can lead to strongly-correlated emergent phenomena. In particular, the family of Ta dichalcogenides in the 1\textit{T} phase presents a star-of-David charge density wave that creates a flat band at the Fermi level. For TaS$_2$ and TaSe$_2$ this flat band is at half filling leading to a magnetic insulating phase. In this work, we theoretically demonstrate that ligand substitution in the TaSe$_{2-x}$Te$_x$ system produces a transition from the magnetic insulator to a non-magnetic metal in which the flat band gets doped away from half-filling. For $x\in[{0.846},{1.231}]$ the spin-polarized flat band is self-doped and the system becomes a magnetic metal. In this regime, we show that attractive interactions promote three different spin-triplet superconducting phases as a function of $x$, corresponding to a nodal f-wave and two topologically-different chiral p-wave superconducting phases. Our results establish monolayer TaSe$_{2-x}$Te$_{x}$ as a promising platform for correlated flat band physics leading to unconventional superconducting states.
A Comparison of the Standardized Prediction Error Criterion with Other ARCH Model Selection Criteria

In this report, two important issues that arise in the evaluation of the standardized prediction error criterion (SPEC) model selection method are investigated in the context of a simulated options market. The first refers to the question of whether the performance of the SPEC algorithm is sensitive to the size of the sample used and the second to that of how the SPEC algorithm compares with other methods of model selection that measure the accuracy of the ARCH models to forecast the realized intra-day volatility.
EmTract: Extracting Emotions from Social Media

We develop an open-source tool (EmTract) that extracts emotions from social media text tailed for financial context. To do so, we annotate ten thousand short messages from a financial social media platform (StockTwits) and combine it with open-source emotion data. We then use a pre-tuned NLP model, DistilBERT, augment its embedding space by including 4,861 tokens (emojis and emoticons), and then fit it first on the open-source emotion data, then transfer it to our annotated financial social media data. Our model outperforms competing open-source state-of-the-art emotion classifiers, such as Emotion English DistilRoBERTa-base on both human and chatGPT annotated data. Compared to dictionary based methods, our methodology has three main advantages for research in finance. First, our model is tailored to financial social media text; second, it incorporates key aspects of social media data, such as non-standard phrases, emojis, and emoticons; and third, it operates by sequentially learning a latent representation that includes features such as word order, word usage, and local context. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We show that firm-specific investor emotions are predictive of daily price movements. Our findings show that emotions and market dynamics are closely related, and we provide a tool to help study the role emotions play in financial markets.
A Gradient Descent-Ascent Method for Continuous-Time Risk-Averse Optimal Control

In this paper, we consider continuous-time stochastic optimal control problems where the cost is evaluated through a coherent risk measure. We provide an explicit gradient descent-ascent algorithm which applies to problems subject to non-linear stochastic differential equations. More specifically, we leverage duality properties of coherent risk measures to relax the problem via a smooth min-max reformulation which induces artificial strong concavity in the max subproblem. We then formulate necessary conditions of optimality for this relaxed problem which we leverage to prove convergence of the gradient descent-ascent algorithm to candidate solutions of the original problem. Finally, we showcase the efficiency of our algorithm through numerical simulations involving trajectory tracking problems and highlight the benefit of favoring risk measures over classical expectation.
Mid-T heat treatments on BCP’ed coaxial cavities at TRIUMF

Mid-T heat treatments in the range from 250°C to 400°C on superconducting radio-frequency (SRF) cavities have been shown to provide high quality factors that rise with applied rf field strength in high frequency, electro-polished (EP), elliptical cavities operating at 2 K, similar to nitrogen doped cavities. The rise in quality factor is attributed to a decrease in the temperature dependent part of the surface resistance RBCS. Until now, no results have been reported for these new treatments on quarter-wave resonators (QWR) and half-wave resonators (HWR). The TRIUMF multi-mode coaxial cavities are dedicated test cavities that allow frequency and temperature resolved performance characterization of treatments without changing environments, therefore providing an excellent test vehicle to test these new treatments with rf frequencies ranging from 200 to 1,200 MHz. In this paper, performance measurements from both QWR and HWR cavities are reported and their performance compared with four different treatments: baseline, a conventional 120°C low temperature bake for 48 h, and two mid-T bakes at 300°C and 400°C for 3 h. In addition, sample analysis using SEM, EDX and SIMS of witness samples is also shown. It is found that the mid-T bakes are not directly transferable to low frequency cavities. In the fundamental modes of the two test cavities, no performance gain over the baseline treatment nor a decreasing temperature dependent component with rising rf amplitude was observed. At frequencies above 1 GHz and low temperatures, the mid-T bakes show a reduced field dependence of RBCS compared to both the baseline and 120°C treatments.
Rotation-invariant web bases from hourglass plabic graphs

Webs give a diagrammatic calculus for spaces of tensor invariants. We introduce hourglass plabic graphs as a new avatar of webs, and use these to give the first rotation-invariant $U_q(\mathfrak{sl}_4)$-web basis, a long-sought object. The characterization of our basis webs relies on the combinatorics of these new plabic graphs and associated configurations of a symmetrized six-vertex model. We give growth rules, based on a novel crystal-theoretic technique, for generating our basis webs from tableaux and we use skein relations to give an algorithm for expressing arbitrary webs in the basis. We also discuss how previously known rotation-invariant web bases can be unified in our framework of hourglass plabic graphs.
Impact of recent MINERvA measurement of the antineutrino-proton scattering cross section on the generalized parton distributions

We investigate the impact of the new measurement of the antineutrino-proton scattering cross-section from the MINERvA Collaboration on generalized parton distributions (GPDs), particularly the polarized GPDs denoted as $\widetilde{H}^q$. To achieve this, we perform some QCD analyses of the MINERvA data, in addition to all available data of the proton's axial form factors. We demonstrate that MINERvA data lead to consistent results with other related experimental data, confirming the universality of GPDs. Our results indicate that MINERvA data can impose new constraints on GPDs, particularly on $\widetilde{H}^q$. Our predictions for the proton's axial charge radius, WACS cross-section, and axial form factor show good consistency with those of other studies and measurements. This leads us to conclude that the result of a more comprehensive analysis, considering all related experimental data, is not only reasonable but also more reliable, even in light of existing tensions among the data. The present study can be considered as a guideline for performing a new and comprehensive QCD global analysis of GPDs including the MINERvA measurements like that presented in Phys. Rev. D \textbf{107}, 096005 (2023).
Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data

Alzheimer's disease (AD) is a prominent, worldwide, age-related neurodegenerative disease that currently has no systemic treatment. Strong evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers, astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large amount of Abeta is secreted by astrocytes, which contributes to the total Abeta deposition in the brain. This suggests that astrocytes may also play a role in AD, leading to increased attention to their dynamics and associated mechanisms. Therefore, in the present study, we developed and evaluated novel stochastic models for Abeta growth using ADNI data to predict the effect of astrocytes on AD progression in a clinical trial. In the AD case, accurate prediction is required for a successful clinical treatment plan. Given that AD studies are observational in nature and involve routine patient visits, stochastic models provide a suitable framework for modelling AD. Using the approximate Bayesian computation (ABC) approach, the AD etiology may be modelled as a multi-state disease process. As a result, we use this approach to examine the weak and strong influence of astrocytes at multiple disease progression stages using ADNI data from the baseline to 2-year visits for AD patients whose ages ranged from 50 to 90 years. Based on ADNI data, we discovered that the strong astrocyte effect (i.e., a higher concentration of astrocytes as compared to Abeta) could help to lower or clear the growth of Abeta, which is a key to slowing down AD progression.
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement

Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Nevertheless, it is still understood as an open question how to exploit the entire information encoded in them properly. We address this problem by considering an order based on (sets of) expectations of random variables mapping into such non-standard spaces. This order contains stochastic dominance and expectation order as extreme cases when no, or respectively perfect, cardinal structure is given. We derive a (regularized) statistical test for our proposed generalized stochastic dominance (GSD) order, operationalize it by linear optimization, and robustify it by imprecise probability models. Our findings are illustrated with data from multidimensional poverty measurement, finance, and medicine.
MQ-Coder Inspired Arithmetic Coder for Synthetic DNA Data Storage

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder.The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes.The main take away of this work is our arithmetic coder and it's integration into a performant image codec.
Evaluating Online Bandit Exploration In Large-Scale Recommender System

Bandit learning has been an increasingly popular design choice for recommender system. Despite the strong interest in bandit learning from the community, there remains multiple bottlenecks that prevent many bandit learning approaches from productionalization. One major bottleneck is how to test the effectiveness of bandit algorithm with fairness and without data leakage. Different from supervised learning algorithms, bandit learning algorithms emphasize greatly on the data collection process through their explorative nature. Such explorative behavior may induce unfair evaluation in a classic A/B test setting. In this work, we apply upper confidence bound (UCB) to our large scale short video recommender system and present a test framework for the production bandit learning life-cycle with a new set of metrics. Extensive experiment results show that our experiment design is able to fairly evaluate the performance of bandit learning in the recommender system.
Correlated phases in spin-orbit-coupled rhombohedral trilayer graphene

Recent experiments indicate that crystalline graphene multilayers exhibit much of the richness of their twisted counterparts, including cascades of symmetry-broken states and unconventional superconductivity. Interfacing Bernal bilayer graphene with a WSe$_2$ monolayer was shown to dramatically enhance superconductivity -- suggesting that proximity-induced spin-orbit coupling plays a key role in promoting Cooper pairing. Motivated by this observation, we study the phase diagram of spin-orbit-coupled rhombohedral trilayer graphene via self-consistent Hartree-Fock simulations, elucidating the interplay between displacement field effects, long-range Coulomb repulsion, short-range (Hund's) interactions, and substrate-induced Ising spin-orbit coupling. In addition to generalized Stoner ferromagnets, we find various flavors of intervalley coherent ground states distinguished by their transformation properties under electronic time reversal, ${\rm C}_3$ rotations, and an effective anti-unitary symmetry. We pay particular attention to broken-symmetry phases that yield Fermi surfaces compatible with zero-momentum Cooper pairing, identifying promising candidate orders that may support spin-orbit-enhanced superconductivity.
The deformed Inozemtsev spin chain

The Inozemtsev chain is an exactly solvable interpolation between the short-range Heisenberg and long-range Haldane-Shastry (HS) chains. In order to unlock its potential to study spin interactions with tunable interaction range using the powerful tools of integrability, the model's mathematical properties require better understanding. As a major step in this direction, we present a new generalisation of the Inozemtsev chain with spin symmetry reduced to U(1), interpolating between a Heisenberg xxz chain and the xxz-type HS chain, and integrable throughout. Underlying it is a new quantum many-body system that extends the elliptic Ruijsenaars system by including spins, contains the trigonometric spin-Ruijsenaars-Macdonald system as a special case, and yields our spin chain by 'freezing'. Our models have potential applications from condensed-matter to high-energy theory, and provide a crucial step towards a general theory for long-range integrability.
Analytical drain current model for long-channel gate-all-around negative capacitance transistors with a metal–ferroelectric–insulator–semiconductor structure

A carrier-based analytical drain current model was proposed for long-channel gate-all-around negative capacitance transistors with a metal–ferroelectric–insulator–semiconductor structure, which was derived by solving Poisson’s equation and a one-dimensional Landau–Khalatnikov equation. The electrostatic potential, gain of surface potential, and drain current were examined extensively by changing different device parameters, including the ferroelectric film thickness, channel radius, insulator layer thickness, and permittivity of the insulator layer. The device design methodologies are discussed in detail in this paper. A nonhysteretic transfer characteristic with a steep subthreshold swing (<60 mV/decade) was achieved at room temperature by optimizing the device parameters. The developed model is valid for all operation regions without any auxiliary variables or functions.
Temperature-dependent strain and temperature sensitivities of fused silica single mode fiber sensors with pulse pre-pump Brillouin optical time domain analysis

This paper reports a distributed temperature and strain sensor based on pulse pre-pump Brillouin optical time domain analysis. An uncoated, telecom-grade fused silica single-mode fiber as a distributed sensor was calibrated for its sensitivity coefficients under various strains and temperatures up to 800 °C. The Brillouin frequency of fiber samples changed nonlinearly with temperature and linearly with strain. The temperature sensitivity decreased from 1.113 to 0.830 MHz /°C in the range of 22–800 °C. The strain sensitivity was reduced from 0.054 to 0.042 MHz /με as the temperature increased from 22 to 700 °C and became unstable at higher temperatures due to creep effect. The strain measurement range was reduced from 19 100 to 6000 με in the temperature range of 22–800 °C due to fused silica’s degradation. The calibrated fiber optic sensor demonstrated adequate accuracy and precision for strain and temperature measurements and stable performance in heating–cooling cycles. It was validated in an application setting.
IJRDM-10-2021-0485_proof 1156..1181

Purpose – The study aims to propose a comprehensive overview of the Omnichannel phenomenon by identifying its theoretical foundations as well as future research directions. Design/methodology/approach – In order to systematize Omnichannel-centered contributions and identify future research directions for post-Covid-19, this study adopted amixed-method study, combining a systematic literature review, a bibliometric co-citation analysis and a panel discussion by field experts. Findings – In Study 1, the authors traced extant literature on Omnichannel back to its theoretical foundations, which led to the identification of four research areas in which the concept of Omnichannel is rooted. Contributions pertaining to the aforesaid research areas were discussed and submitted to a panel of experts (Study 2) after the lockdown periods. The experts gave various insights into both the past and future of Omnichannel research. Finally, a framework synthesizing theoretical foundations of Omnichannel, literature gaps and opportunities for future research is provided. Originality/value – To our knowledge, this is the first attempt to combine mixed methods study in Omnichannel research and to involve a panel of experts in order to discuss the findings of a literature review and evaluate future research directions. This choice allowed us to investigate both incumbent academic and managerial challenges raised by Omnichannel and to provide guidance for the post-pandemic recovery.
On k-cordial Labeling of Some Graphs

In this paper we present k-cordiality of one point union of some path, cycle and star related graphs. We prove that bistar graph Bm;n is k-cordial for all k, restricted square graph B 2 of Bistar is k-cordial for all k. We prove that one point union of cycle C3 with star graph K1;n and the comb Pn J K1 are k-cordial for all k.
Simplicity of Categories Defined by Symmetry Axioms

Abstract We consider two generalizations R 0w and R 0 of the usual symmetry axiom for topological spaces to arbitrary closure spaces and convergence spaces. It is known that the two properties coincide on Top and define a non-simple subcategory. We show that R0W defines a simple subcategory of closure spaces and R0 a non-simple one. The last negative result follows from the stronger statement that every epireflective subcategory of R0 Conv containing all T 1 regular topological spaces is not simple. Similar theorems are shown for the topological categories Fil and Mer.
Jordan Canonical Form for Solving the Fault Diagnosis and Estimation Problems

The suggested methods for solving fault diagnosis and estimation problems are based on the use of the Jordan canonical form. The diagnostic observer, virtual sensor, interval, and sliding mode observer design problems are considered. Algorithms have been developed to solve these problems for both linear and nonlinear systems, considering the presence of external disturbances and measurement noise. It has been shown that the Jordan canonical form allows reducing the dimensions of interval observers and virtual sensors, thus simplifying the design process in comparison to the identification canonical form. The theoretical results are illustrated through examples.