Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative Analysis of Pharmacokinetic and Metabolomic Profiles for Predicting Metabolic Phenotype and Drug Exposure Caused by Sotorasib in Rats

Sotorasib is a novel targeted inhibitor of Kirsten rat sarcoma (KRAS) (G12C) that has shown exciting tumor-suppressing effects not only for single targeted agents but also for combination with immune checkpoint inhibitors. However, no integrative analysis of the pharmacokinetics (PK) and pharmacometabolomics (PM) of sotorasib has been reported to date. In the present study, a sensitive and robust high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) method was firstly developed and fully validated for the quantitation of sotorasib in rat plasma. After one-step protein precipitation, sotorasib and an internal standard (carbamazepine) were separated on a Waters XBrige C18 column (50 mm × 2.1 mm, 3.5 μm) and analyzed in electrospray ionization positive ion (ESI+) mode. The optimized method was fully validated according to guidance and was successfully applied for the PK study of sotorasib at a dose of 10 mg/kg. In addition, a longitudinal and transversal PM was employed and correlated with PK using partial least squares model and Pearson’s analysis. With multivariate statistical analysis, the selected six (AUC model) and nine (C max model) metabolites completely distinguished the high- and low-exposure groups after sotorasib treatment, which indicates that these potential biomarkers can predict drug exposure or toxicity. The results of this study will not only shed light on how sotorasib disturbs the metabolic profiles and the relationship between PK and PM but also offer meaningful references for precision therapy in patients with the KRAS (G12C) mutation.
Inflammatory Response to Regulated Cell Death in Gout and Its Functional Implications

Gout, a chronic inflammatory arthritis disease, is characterized by hyperuricemia and caused by interactions between genetic, epigenetic, and metabolic factors. Acute gout symptoms are triggered by the inflammatory response to monosodium urate crystals, which is mediated by the innate immune system and immune cells (e.g., macrophages and neutrophils), the NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome activation, and pro-inflammatory cytokine (e.g., IL-1β) release. Recent studies have indicated that the multiple programmed cell death pathways involved in the inflammatory response include pyroptosis, NETosis, necroptosis, and apoptosis, which initiate inflammatory reactions. In this review, we explore the correlation and interactions among these factors and their roles in the pathogenesis of gout to provide future research directions and possibilities for identifying potential novel therapeutic targets and enhancing our understanding of gout pathogenesis.
Social Disruption, Gun Buying, and Anti-System Beliefs

Gun ownership is a highly consequential political behavior. It often signifies a belief about the inadequacy of state-provided security and leads to membership in a powerful political constituency. As a result, it is important to understand why people buy guns and how shifting purchasing patterns affect the composition of the broader gun-owning community. We address these topics by exploring the dynamics of the gun-buying spike that took place during the COVID-19 pandemic, which was one of the largest in American history. We find that feelings of diffuse threat prompted many individuals to buy guns. Moreover, we show that new gun owners, even more than buyers who already owned guns, exhibit strong conspiracy and anti-system beliefs. These findings have substantial consequences for the subsequent population of gun owners and provide insight into how social disruptions can alter the nature of political groups.
A New Correction to the Rytov Approximation for Strongly Scattering Lossy Media

We propose a correction to the conventional Rytov approximation (RA) and investigate its performance for predicting wave scattering under strong scattering conditions. An important motivation for the correction and investigation is to help in the development of better models for inverse scattering. The correction is based on incorporating the high-frequency theory of inhomogeneous wave propagation for lossy media into RA formulation. We denote the technique as the extended RA for lossy media (xRA-LM). xRA-LM significantly improves upon the existing noniterative linear scattering approximations such as RA and Born approximation (BA) by providing a validity range for the permittivity of objects of up to 50 times greater than RA. We demonstrate the technique by providing results for predicting wave scattering from piecewise homogeneous scatterers in a 2-D region. Numerical investigation of the performance of xRA-LM for solving direct problems show that xRA-LM can accurately predict wave scattering by electrically large, low-loss scatterers with high complex relative permittivity ( $\epsilon _{r}> 50+5j$ ). To the best of our knowledge, this is the first noniterative, linear approximate wave scattering model which has a large validity range in terms of both permittivity and electrical size.
A Piecewise Learning Framework for Control of Unknown Nonlinear Systems with Stability Guarantees

We propose a piecewise learning framework for controlling nonlinear systems with unknown dynamics. While model-based reinforcement learning techniques in terms of some basis functions are well known in the literature, when it comes to more complex dynamics, only a local approximation of the model can be obtained using a limited number of bases. The complexity of the identiﬁer and the controller can be considerably high if obtaining an approximation over a larger domain is desired. To overcome this limitation, we propose a general piecewise nonlinear framework where each piece is responsible for locally learning and controlling over some region of the domain. We obtain rigorous uncertainty bounds for the learned piecewise models. The piecewise afﬁne (PWA) model is then studied as a special case, for which we propose an optimization-based veriﬁcation technique for stability analysis of the closed-loop system. Accordingly, given a time-discretization of the learned PWA system, we iteratively search for a common piecewise Lyapunov function in a set of positive deﬁnite functions, where a non-monotonic convergence is allowed. This Lyapunov candidate is veriﬁed on the uncertain system to either provide a certiﬁcate for stability or ﬁnd a counter-example when it fails. This counter-example is added to a set of samples to facilitate the further learning of a Lyapunov function. We demonstrate the results on two examples and show that the proposed approach yields a less conservative region of attraction (ROA) compared with alternative state-of-the-art approaches. Moreover, we provide the runtime results to demonstrate potentials of the proposed framework in real-world implementations.
Capture of electroweak multiplet dark matter in neutron stars

If dark matter has a sizable scattering cross section with nucleons, it can efficiently be captured by a neutron star. Its energy is then transferred to the neutron star as heat through the scattering and annihilation inside the star. This heating effect may be detectable via dedicated temperature observations of nearby old pulsars, providing an alternative method for dark matter searches. In this paper, we show that for electroweak multiplet dark matter this search strategy can probe the parameter region which is out of reach of future dark matter direct detection experiments. To see this systematically, we classify such dark matter candidates in terms of their electroweak charges and investigate the effect of ultraviolet physics by means of higher-dimensional effective operators. We then show that if the effect of ultraviolet physics is sizable, the dark matter-nucleon elastic scattering cross section becomes sufficiently large, whilst if it is suppressed, then the mass splittings among the components of the DM multiplet get small enough so that the inelastic scattering processes are operative. In any case, the electroweak multiplet dark matter particles are efficiently captured in neutron stars, making the search strategy with the temperature observation of old neutron stars promising.
Using random graphs to sample repulsive Gibbs point processes with arbitrary-range potentials

We study computational aspects of repulsive Gibbs point processes, which are probabilistic models of interacting particles in a finite-volume region of space. We introduce an approach for reducing a Gibbs point process to the hard-core model, a well-studied discrete spin system. Given an instance of such a point process, our reduction generates a random graph drawn from a natural geometric model. We show that the partition function of a hard-core model on graphs generated by the geometric model concentrates around the partition function of the Gibbs point process. Our reduction allows us to use a broad range of algorithms developed for the hard-core model to sample from the Gibbs point process and approximate its partition function. This is, to the extend of our knowledge, the first approach that deals with pair potentials of unbounded range. We compare the resulting algorithms with recently established results and study further properties of the random geometric graphs with respect to the hard-core model.
Role of nucleon eﬀective mass and symmetry energy on the neutrino mean free path in neutron star

The Korea-IBS-Daegu-SKKU energy density functional (KIDS-EDF) models, derived from the universal Skyrme functional, have been successfully and widely applied in describing the properties of ﬁnite nuclei and inﬁnite nuclear matter. In the present work, we extend the applications of the KIDS-EDF models to investigate the implications of the nucleon eﬀective mass and nuclear symmetry energy obtained from the KIDS-EDF models on the properties of neutron star (NS) and neutrino interaction with the NS constituents matter in the linear response approximation (LRA). We then analyze the total diﬀerential cross-section of neutrino, neutrino mean free path (NMFP), and the NS mass-radius (M-R) relations. We ﬁnd that the NS M-R relations predictions for all KIDS-EDF models are in excellent agreement with the recent observations as well as the NICER result. Remarkable prediction results on the NMFPs are given by the KIDS0-m*77 and KIDS0-m*99 models with 𝑀 ∗ 𝑛 / 𝑀 (cid:46) 1 which are quite higher in comparison with those obtained for the KIDS0, KIDS-A, and KIDS-B models with 𝑀 ∗ 𝑛 / 𝑀 (cid:38) 1. For the KIDS0, KIDS-A, and KIDS-B models, we obtain the 𝜆 (cid:46) 𝑅 NS , indicating that these models support the slow NS cooling and neutrino trapping in NS. On the contrary, both KIDS0-m*77 and KIDS0-m*99 models support faster NS cooling and a small possibility of neutrino trapping within NS, predicting 𝜆 (cid:38)
Surrogate model for gravitational wave signals from nonspinning, comparable-to large-mass-ratio black hole binaries built on black hole perturbation theory waveforms calibrated to numerical relativity

We present a reduced-order surrogate model of gravitational waveforms from non-spinning binary black hole systems with comparable to large mass-ratio configurations. This surrogate model, \texttt{BHPTNRSur1dq1e4}, is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with mass ratios varying from 2.5 to 10,000. \texttt{BHPTNRSur1dq1e4} extends an earlier waveform model, \texttt{EMRISur1dq1e4}, by using an updated transition-to-plunge model, covering longer durations up to 30,500 $m_1$ (where $m_1$ is the mass of the primary black hole), includes several more spherical harmonic modes up to $\ell=10$, and calibrates subdominant modes to numerical relativity (NR) data. In the comparable mass-ratio regime, including mass ratios as low as $2.5$, the gravitational waveforms generated through ppBHPT agree surprisingly well with those from NR after this simple calibration step. We also compare our model to recent SXS and RIT NR simulations at mass ratios ranging from $15$ to $32$, and find the dominant quadrupolar modes agree to better than $\approx 10^{-3}$. We expect our model to be useful to study intermediate-mass-ratio binary systems in current and future gravitational-wave detectors.
Lip to Speech Synthesis with Visual Context Attentional GAN

In this paper, we propose a novel lip-to-speech generative adversarial network, Visual Context Attentional GAN (VCA-GAN), which can jointly model local and global lip movements during speech synthesis. Specifically, the proposed VCA-GAN synthesizes the speech from local lip visual features by finding a mapping function of viseme-to-phoneme, while global visual context is embedded into the intermediate layers of the generator to clarify the ambiguity in the mapping induced by homophene. To achieve this, a visual context attention module is proposed where it encodes global representations from the local visual features, and provides the desired global visual context corresponding to the given coarse speech representation to the generator through audio-visual attention. In addition to the explicit modelling of local and global visual representations, synchronization learning is introduced as a form of contrastive learning that guides the generator to synthesize a speech in sync with the given input lip movements. Extensive experiments demonstrate that the proposed VCA-GAN outperforms existing state-of-the-art and is able to effectively synthesize the speech from multi-speaker that has been barely handled in the previous works.
Local and global polarization of 
Λ
 hyperons across RHIC-BES energies: The roles of spin hall effect, initial condition, and baryon diffusion

We perform a systematic study on the local and global spin polarization of Λ and Λ hyperons in relativistic heavy-ion collisions at beam energy scan energies via the (3+1)-dimensional CLVisc hydrodynamics model with AMPT and SMASH initial conditions. Following the quantum kinetic theory, we decompose the polarization vector as the parts induced by thermal vorticity, shear tensor and the spin Hall eﬀect (SHE). We ﬁnd that the polarization induced by SHE and the total polarization strongly depends on the initial conditions. At 7 . 7GeV, SHE gives a sizeable contribution and even ﬂips the sign of the local polarization along the beam direction for AMPT initial condition, which is not observed for SMASH initial condition. Meanwhile, the local polarization along the out-of-plane direction induced by SHE with AMPT initial condition does not always increase with decreasing collision energies. Next, we ﬁnd that the polarization along the beam direction is sensitive to the baryon diﬀusion coeﬃcient, but the local polarization along the out-of-plane direction is not. Our results for the global polarization of Λ and Λ agree well with the STAR data. Interestingly, the global polarization of Λ is not always larger than that of Λ due to various competing eﬀects. Our ﬁndings are helpful for understanding the polarization phenomenon and the detailed structure of quark-gluon plasma in relativistic heavy-ion collisions.
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators

We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power scenarios by sampling from a conditional distribution that uses wind speed forecasts to tailor the scenarios to a specific day. We apply the generated scenarios in a stochastic day-ahead bidding problem of a wind electricity producer and analyze whether the scenarios yield profitable decisions. Compared to Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow successfully narrows the range of scenarios around the daily trends while maintaining a diverse variety of possible realizations. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more stable profitable results compared to an unconditional selection of historical scenarios. The normalizing flow consistently obtains the highest profits, even for small sets scenarios.
Can language models learn from explanations in context?

Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help LMs. We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations. We evaluate how different types of explanations, instructions, and controls affect zero- and few-shot performance. We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models. We find that explanations can improve performance -- even without tuning. Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone. Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features. However, only large models benefit. In summary, explanations can support the in-context learning of large LMs on challenging tasks.
Model Based Meta Learning of Critics for Policy Gradients

Being able to seamlessly generalize across different tasks is fundamental for robots to act in our world. However, learning representations that generalize quickly to new scenarios is still an open research problem in reinforcement learning. In this paper we present a framework to meta-learn the critic for gradient-based policy learning. Concretely, we propose a model-based bi-level optimization algorithm that updates the critics parameters such that the policy that is learned with the updated critic gets closer to solving the meta-training tasks. We illustrate that our algorithm leads to learned critics that resemble the ground truth Q function for a given task. Finally, after meta-training, the learned critic can be used to learn new policies for new unseen task and environment settings via model-free policy gradient optimization, without requiring a model. We present results that show the generalization capabilities of our learned critic to new tasks and dynamics when used to learn a new policy in a new scenario.
Global Mapping of Surface Composition on an Exo-Earth Using Sparse Modeling

The time series of light reflected from exoplanets by future direct imaging can provide spatial information with respect to the planetary surface. We apply sparse modeling to the retrieval method that disentangles the spatial and spectral information from multiband reflected light curves termed as spin–orbit unmixing. We use the ℓ 1-norm and the total squared variation norm as regularization terms for the surface distribution. Applying our technique to a toy model of cloudless Earth, we show that our method can infer sparse and continuous surface distributions and also unmixed spectra without prior knowledge of the planet surface. We also apply the technique to the real Earth data as observed by DSCOVR/EPIC. We determined the representative components that can be interpreted as cloud and ocean. Additionally, we found two components that resembled the distribution of land. One of the components captures the Sahara Desert, and the other roughly corresponds to vegetation, although their spectra are still contaminated by clouds. Sparse modeling significantly improves the geographic retrieval, in particular, of clouds and leads to higher resolutions for other components when compared with spin–orbit unmixing using Tikhonov regularization.
VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices

In this paper, we address the problem of lip-voice synchronisation in videos containing human face and voice. Our approach is based on determining if the lips motion and the voice in a video are synchronised or not, depending on their audio-visual correspondence score. We propose an audio-visual cross-modal transformer-based model that outperforms several baseline models in the audio-visual synchronisation task on the standard lip-reading speech benchmark dataset LRS2. While the existing methods focus mainly on lip synchronisation in speech videos, we also consider the special case of the singing voice. The singing voice is a more challenging use case for synchronisation due to sustained vowel sounds. We also investigate the relevance of lip synchronisation models trained on speech datasets in the context of singing voice. Finally, we use the frozen visual features learned by our lip synchronisation model in the singing voice separation task to outperform a baseline audio-visual model which was trained end-to-end. The demos, source code, and the pre-trained models are available on https://ipcv.github. io/VocaLiST/
Creativity in crisis : creative climate of failing startups

Entrepreneurship is a bumpy road and 9 out of 10 new ventures fail. At the same time, it has long been understood that creativity is an important determinant of a startup’s performance and success. Previous work has looked at the role of the work climate on the creativity of organization but it has remained static and failed to address the specific case of startups. This study focuses on understanding the processes of failure by looking at how crisis influences the creative climate of new venture teams. Using semi-structured interviews, members of a failing startup were asked about their perceptions of their work environment before and during the crisis. From this we could derive the creative climate profile of each phase. In the success phase, the startup showed high intensity of climate factors favorable to creativity and low levels of factors detrimental to creativity. In the failure phase it showed opposite values. In particular, control and time pressure were the most recurring issues expressed by the participants. The results of this research clearly show that climates are dynamic and that crisis induces behaviors detrimental to a climate for creativity. These insights can help founders get awareness of their response to crisis and regain some control over the failure process.
Accurate benchmark results of Blasius boundary layer problem using a leaping Taylors series that converges for all real values

Blasius boundary layer solution is a Maclaurin series expansion of the function f ( η ), which has convergence problems when evaluating for higher values of η due to a singularity present at η ≈ − 5 . 69. In this paper we are in-troducing an accurate solution to f ( η ) using Taylor’s series expansions with progressively shifted centers of expansion(Leaping centers). Each series is solved as an IVP with the three initial values computed from solution of the previous series, so the gap between the centers of two consecutive expansions is selected from within the convergence disc of the ﬁrst series. The last series is formed such that it is convergent for a reasonable high η value needed for implementing the boundary condition at inﬁnity. The present methodology uses Newton-Raphson method to compute the value of the unknown initial condition viz. f ′′ (0) in an iterative manner. Benchmark accurate results of diﬀerent parameters of ﬂat plate boundary layer with no slip and slip boundary conditions have been reported in this paper.