Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflections on Business Research: Working Backwards, Forwards, Sideways

This reflective thought paper considers approaches to doing business research that are discussed in the extant literature. The purpose is to stimulate debate by reflecting on the process of developing a research methodology, drawing on evidence from my own experiences of conducting research into social media in football. The paper questions the ‘top-down’ linear approach encouraged by research methods texts and usually evident in the presentation of research (Theses, Journals, and Conferences). It is suggested that the developing of a research methodology can be ‘bottom-up’ and iterative, thus posing challenges for the researcher to present such work in the more common style of a post-hoc rationalisation. In doing so, the paper makes a contribution to business research methodology and invites fellow researchers to share their views and experiences, for the benefit of early career researchers.
Engineering of Form Factor in Rotationally Symmetric Photonic Cavities for Faster Axion Searches

Form factor can be engineered in photonic cavities such that it dramatically increases the scan speed of axion haloscope detectors. Using higher-order modes of a photonic cavity, we can probe axions at larger mass ranges beyond the reach of current detectors that rely on conventional copper cavities. The main idea is to produce a higher-order mode with longitudinal field distribution that has relatively high amplitude away from both the cavity axis and the cavity walls such that it results in a relatively large form factor and maintains a highquality factor. The detector scan speed significantly increases as it is proportional to the square of the form factor multiplied by the quality factor.
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios

Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. A victim classifier will predict to an attacker-desired target class whenever a test sample is embedded with the same backdoor pattern (BP) that was used to poison the classifier's training set. Detecting whether a classifier is backdoor attacked is not easy in practice, especially when the defender is, e.g., a downstream user without access to the classifier's training set. This challenge is addressed here by a reverse-engineering defense (RED), which has been shown to yield state-of-the-art performance in several domains. However, existing REDs are not applicable when there are only {\it two classes} or when {\it multiple attacks} are present. These scenarios are first studied in the current paper, under the practical constraints that the defender neither has access to the classifier's training set nor to supervision from clean reference classifiers trained for the same domain. We propose a detection framework based on BP reverse-engineering and a novel {\it expected transferability} (ET) statistic. We show that our ET statistic is effective {\it using the same detection threshold}, irrespective of the classification domain, the attack configuration, and the BP reverse-engineering algorithm that is used. The excellent performance of our method is demonstrated on six benchmark datasets. Notably, our detection framework is also applicable to multi-class scenarios with multiple attacks. Code is available at https://github.com/zhenxianglance/2ClassBADetection.
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes, its theoretical characterization remains elusive. To the best of our knowledge, this work establishes the first theoretical analysis for the known iterative self-training paradigm and proves the benefits of unlabeled data in both training convergence and generalization ability. To make our theoretical analysis feasible, we focus on the case of one-hidden-layer neural networks. However, theoretical understanding of iterative self-training is non-trivial even for a shallow neural network. One of the key challenges is that existing neural network landscape analysis built upon supervised learning no longer holds in the (semi-supervised) self-training paradigm. We address this challenge and prove that iterative self-training converges linearly with both convergence rate and generalization accuracy improved in the order of $1/\sqrt{M}$, where $M$ is the number of unlabeled samples. Experiments from shallow neural networks to deep neural networks are also provided to justify the correctness of our established theoretical insights on self-training.
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search

We develop a new measure of the exploration/exploitation trade-oﬀ in inﬁnite-horizon reinforcement learning problems called the occupancy information ratio (OIR), which is comprised of a ratio between the inﬁnite-horizon average cost of a policy and the entropy of its long-term state occupancy measure. The OIR ensures that no matter how many trajectories an RL agent traverses or how well it learns to minimize cost, it maintains a healthy skepticism about its environment, in that it deﬁnes an optimal policy which induces a high-entropy occupancy measure. Diﬀerent from earlier information ratio notions, OIR is amenable to direct policy search over parameterized families, and exhibits hidden quasiconcavity through invocation of the perspective transformation. This feature ensures that under appropriate policy parameterizations, the OIR optimization problem has no spurious stationary points, despite the overall problem’s nonconvexity. We develop for the ﬁrst time policy gradient and actor-critic algorithms for OIR optimization based upon a new entropy gradient theorem, and establish both asymptotic and non-asymptotic convergence results with global optimality guarantees. In experiments, these methodologies outperform several deep RL baselines in problems with sparse rewards, where many trajectories may be uninformative and skepticism about the environment is crucial to success.
Can Model Compression Improve NLP Fairness

Model compression techniques are receiving increasing attention; however, the effect of compression on model fairness is still under explored. This is the first paper to examine the effect of distillation and pruning on the toxicity and bias of generative language models. We test Knowledge Distillation and Pruning methods on the GPT2 model and found a consistent pattern of toxicity and bias reduction after model distillation; this result can be potentially interpreted by existing line of research which describes model compression as a regularization technique; our work not only serves as a reference for safe deployment of compressed models, but also extends the discussion of"compression as regularization"into the setting of neural LMs, and hints at the possibility of using compression to develop fairer models.
Survey of Multiple Populations in Globular Clusters among Very-low-mass Stars

Recent work has shown that near-infrared (NIR) Hubble Space Telescope (HST) photometry allows us to disentangle multiple populations (MPs) among M dwarfs of globular clusters (GCs) and to investigate this phenomenon in very-low-mass (VLM) stars. Here, we present the color–magnitude diagrams of nine GCs and the open cluster NGC 6791 in the F110W and F160W bands of HST, showing that the main sequences (MSs) below the knee are either broadened or split, thus providing evidence of MPs among VLM stars. In contrast, the MS of NGC 6791 is consistent with a single population. The color distribution of M dwarfs dramatically changes between different GCs, and the color width correlates with the cluster mass. We conclude that the MP ubiquity, variety, and dependence on GC mass are properties common to VLM and more-massive stars. We combined UV, optical, and NIR observations of NGC 2808 and NGC 6121 (M4) to identify MPs along with a wide range of stellar masses (∼0.2–0.8 ⊙ ), from the MS turnoff to the VLM regime, and measured, for the first time, their mass functions (MFs). We find that the fraction of MPs does not depend on the stellar mass and that their MFs have similar slopes. These findings indicate that the properties of MPs do not depend on stellar mass. In a scenario where the second generations formed in higher-density environments than the first generations, the possibility that the MPs formed with the same initial MF would suggest that it does not depend on the environment.
Low-density phase diagram of the three-dimensional electron gas

Variational and diffusion quantum Monte Carlo methods are employed to investigate the zero-temperature phase diagram of the three-dimensional homogeneous electron gas at very low density. Fermi fluid and body-centered cubic Wigner crystal ground state energies are determined using Slater-Jastrow-backflow and Slater-Jastrow many-body wave functions at different densities and spin polarizations in finite simulation cells. Finite-size errors are removed using twist-averaged boundary conditions and extrapolation of the energy per particle to the thermodynamic limit of infinite system size. Unlike previous studies, our results show that the electron gas undergoes a first-order quantum phase transition directly from a paramagnetic fluid to a body-centered cubic crystal at density parameter $r_\text{s} = 86.6(7)$, with no region of stability for an itinerant ferromagnetic fluid. However there is a possible magnetic phase transition from an antiferromagnetic crystal to a ferromagnetic crystal at $r_\text{s}=93(3)$.
Deep Attention-based Supernovae Classification of Multiband Light Curves

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multiband light curves is a challenging task due to the highly irregular cadence, long time gaps, missing values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light curves. We offer three main contributions: (1) Based on temporal modulation and attention mechanisms, we propose a deep attention model (TimeModAttn) to classify multiband light curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. (2) We propose a model for the synthetic generation of SN multiband light curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pretrained using synthetic light curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other deep learning models, based on recurrent neural networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-F 1score from ≈. 525 to ≈. 596. When training the BRF with synthetic data, this model achieved a similar performance to the TimeModAttn model proposed while still maintaining extra advantages. (3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.
Fractional Calculus and Time-Fractional Differential Equations: Revisit and Construction of a Theory

For fractional derivatives and time-fractional differential equations, we construct a framework on the basis of operator theory in fractional Sobolev spaces. Our framework provides a feasible extension of the classical Caputo and the Riemann–Liouville derivatives within Sobolev spaces of fractional orders, including negative ones. Our approach enables a unified treatment for fractional calculus and time-fractional differential equations. We formulate initial value problems for fractional ordinary differential equations and initial boundary value problems for fractional partial differential equations to prove well-posedness and other properties.
Satisficing Paths and Independent Multiagent Reinforcement Learning in Stochastic Games

In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system. Due to the decentralization of information, it is challenging to design independent learners that drive play to equilibrium. This paper investigates the feasibility of using satisficing dynamics to guide independent learners to approximate equilibrium in stochastic games. For $\epsilon \geq 0$, an $\epsilon$-satisficing policy update rule is any rule that instructs the agent to not change its policy when it is $\epsilon$-best-responding to the policies of the remaining players; $\epsilon$-satisficing paths are defined to be sequences of joint policies obtained when each agent uses some $\epsilon$-satisficing policy update rule to select its next policy. We establish structural results on the existence of $\epsilon$-satisficing paths into $\epsilon$-equilibrium in both symmetric $N$-player games and general stochastic games with two players. We then present an independent learning algorithm for $N$-player symmetric games and give high probability guarantees of convergence to $\epsilon$-equilibrium under self-play. This guarantee is made using symmetry alone, leveraging the previously unexploited structure of $\epsilon$-satisficing paths.
EVALUATION OF WATER DAMAGE OF ASPHALT CONCRETE MIXTURES USING THE ENVIRONMENTAL CONDITIONING SYSTEM (ECS) (WITH DISCUSSION)

This study was aimed at determining the factors that most influence water sensitivity of asphalt paving mixtures. A logical approach was to study the fundamental properties of asphalt and aggregate, and develop a series of tests that would rate or screen various combinations for probability of successful performance. The basic factors that influence compacted mixtures such as permeability, time, rate of wetting or saturation, aging, etc., would then be evaluated for a range of mixtures. Eventually, a water conditioning and testing procedure would be recommended for testing by various user agencies prior to final standardization. However, the schedule and interaction with other SHRP contractors required that development of procedures and testing continue more or less concurrently.
Postbiotics as potential promising tools for SARS‐CoV‐2 disease adjuvant therapy

The SARS‐CoV‐2 pandemic defines the global health tension of our time. There are several continuous efforts to find a definitive cure in this regard. According to some adverse effects and problems of customary SARS‐CoV‐2 disease therapies, bioactive compounds, for example probiotics‐derived metabolites (postbiotics) have been accomplishing supreme importance by investigators for adjuvant cures in patients with SARS‐CoV‐2. Postbiotics inhibit angiotensin‐converting enzyme 2 activity and stimulate the immune system. They also boost the SARS‐CoV‐2 disease treatment effectiveness and reduce its adverse effects in SARS‐CoV‐2 illness patients as a result of antiviral and anti‐inflammatory effects. Furthermore, postbiotics having unique features such as high safety, high shelf life and stability to the gastrointestinal tract can be utilized as hopeful instruments for both adjuvant and inhibition strategies in SARS‐CoV‐2 patients with no earnest unfavourable adverse effects. The concept of postbiotics and their biocompatible characteristics are comprehensively discussed in the present review while highlighting the bilateral relationship between postbiotic biometabolites and respiratory tract infection with a special look at the potential biological role in the inactivation of SARS‐CoV‐2 and reduction of related inflammatory pathways.
Niche breadth and elevational range size: a comparative study on Middle-European Brassicaceae species

High-elevation species are predicted to have larger elevational ranges compared with species of lower elevations. The reasoning is that temperature variability is greater at higher elevation, selecting for wider niche breadth and more plastic genotypes. We used macroevolutionary comparisons involving 90 Brassicaceae species of the central Alps to test for associations among median elevation of occurrence, elevational range size and thermal variability over space and time on the one hand, and their associations with performance breadth or trait plasticity on the other hand. Performance breadth and trait plasticity were estimated by raising replicate plants per species under three temperature treatments (mild, recurrent frost, recurrent heat). Against prediction, we found that mid-elevation species had the largest elevational ranges, and their ranges were associated with increased spatial thermal variability. Nevertheless, variability in the thermal regime was positively associated neither with niche breadth nor with plasticity. Evidence for adaptive constraints was limited to a trade-off between acclimation-based increases in frost and heat resistance, and phylogenetic niche conservatism for median elevation of occurrence and temporal thermal variability. Results suggest that large elevational range size is associated with divergent adaptation within species, but not with more niche breadth or trait plasticity. This article is part of the theme issue ‘Species’ ranges in the face of changing environments (part I)’.
Attitudes about Paid Parental Leave in the United States

ABSTRACT Paid parental leave offerings in the United States are relatively rare and unequal. Yet, little is known about public opinions about paid leave and the factors that distinguish adults’ attitudes about them. With the use of data from the General Social Survey, we investigated attitudes about paid parental leave availability, preferred lengths of paid leave offerings, and government funding of leave in the United States. We found overwhelming support for paid parental leave availability, an average preference for four months of paid leave offerings, and common support for at least some government funding for leaves. Older and more politically conservative individuals were consistently less supportive of paid parental leave availability, longer lengths of leave, and government funding of leave. Women, supporters of dual-earner expectations, black individuals, and those who were not working in paid labor were typically more supportive of generous paid parental leave offerings. These findings suggest that there have been longstanding desires for more widespread and generous paid parental leave offerings in the United States but that this has not yet been sufficient to prompt widely applicable policy changes across the nation.
Inflammation in the COVID-19 airway is due to inhibition of CFTR signaling by the SARS-CoV-2 Spike protein

Background SARS-CoV-2-contributes to sickness and death in COVID-19 patients partly by inducing a hyper-proinflammatory immune response in the host airway. This hyper- proinflammatory state involves activation of signaling by NFκB and ENaC, and expression of high levels of cytokines and chemokines. Post-infection inflammation may contribute to “Long COVID”, and there are long term consequences for acute severe COVID-19, which double or triple the chances of dying from any cause within a year. Enhanced signaling by NFκB and ENaC also marks the airway of patients suffering from cystic fibrosis, a lethal proinflammatory genetic disease due to inactivating mutations in the CFTR gene. We therefore hypothesized that inflammation in the COVID-19 airway might be due to inhibition of CFTR signaling by SARS- CoV-2 Spike protein. Methods This hypothesis was tested using the hTERT-transformed BCi-NS1.1 basal stem cell, previously derived from small airway epithelia, which were differentiated into a model of small airway epithelia on an air-liquid-interface (ALI). CyclicAMP-activated CFTR chloride channel activity was measured using an Ussing Chamber. Cell surface-CFTR was labeled with the impermeant biotin method. Results Exposure of differentiated airway epithelia to SARS-CoV-2 Spike protein resulted in loss of CFTR protein expression. As hypothesized, TNFα/NFκB signaling was activated, based on increased protein expression of TNFR1, the TNFα receptor; TRADD, the first intracellular adaptor for the TNFα/TNFR1 complex; phosphorylated IκBα, and the chemokine IL8. ENaC activity was also activated, based on specific changes in molecular weights for α and γ ENaC. Exposure of the epithelia to viral Spike protein suppressed cAMP-activated CFTR chloride channel activity. However, 30 nM concentrations of cardiac glycoside drugs ouabain, digitoxin and digoxin, prevented loss of channel activity. ACE2 and CFTR were found to co- immunoprecipitate (co-IP) in both basal cells and epithelia, suggesting that the mechanism for Spike-dependent CFTR loss might involve ACE2 as a bridge between Spike and CFTR. In addition, Spike exposure to the epithelia resulted in failure of endosomal recycling to return CFTR to the plasma membrane, suggesting that failure of CFTR recovery from endosomal recycling might be a mechanism for spike-dependent loss of CFTR. Conclusion Based on experiments with this model of small airway epithelia, we predict that inflammation in the COVID-19 airway may be mediated by inhibition of CFTR signaling by SARS-CoV-2 Spike protein, thus inducing a CFTR-null, cystic fibrosis-like clinical phenotype.
Clinical and Structural Differences in Delusions Across Diagnoses: A Systematic Review

Delusions are marked, fixed beliefs that are incongruent with reality. Delusions, with comorbid hallucinations, are a hallmark of certain psychotic disorders (e.g., schizophrenia). Delusions can present transdiagnostically, in neurodegenerative (e.g., Alzheimer's disease and fronto-temporal dementia), nervous system disorders (e.g., Parkinson's disease) and across other psychiatric disorders (e.g., bipolar disorder). The burden of delusions is severe and understanding the heterogeneity of delusions may delineate a more valid nosology of not only psychiatric disorders but also neurodegenerative and nervous system disorders. We systematically reviewed structural neuroimaging studies reporting on delusions in four disorder types [schizophrenia (SZ), bipolar disorder (BP), Alzheimer's disease (AD), and Parkinson's disease (PD)] to provide a comprehensive overview of neural changes and clinical presentations associated with delusions. Twenty-eight eligible studies were identified. This review found delusions were most associated with gray matter reductions in the dorsolateral prefrontal cortex (SZ, BP, and AD), left claustrum (SZ and AD), hippocampus (SZ and AD), insula (SZ, BP, and AD), amygdala (SZ and BP), thalamus (SZ and AD), superior temporal gyrus (SZ, BP, and AD), and middle frontal gyrus (SZ, BP, AD, and PD). However, there was a great deal of variability in the findings of each disorder. There is some support for the current dopaminergic hypothesis of psychosis, but we also propose new hypotheses related to the belief formation network and cognitive biases. We also propose a standardization of assessments to aid future transdiagnostic study approaches. Future studies should explore the neural and biological underpinnings of delusions to hopefully, inform future treatment.