Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarized neutron scattering study of the multiple order parameter system NdB 4

Neutron polarization analysis has been carried out in order to clarify the magnetic structures of multiple order parameter f -electron system NdB4. We confirmed the non-collinear ‘all-in all-out’ structure (Γ4) of the in-plane moment, which is in good agreement with our previous neutron powder diffraction study. We found that the magnetic moment along the c-axis mc showed diagonally antiferromagnetic structure (Γ10), inconsistent with previously reported ’vortex’ structure (Γ2). The microscopic mixture of these two structures with ~q0=(0, 0, 0) appears in phase II and remains stable in phases III and IV, where an incommensurate modulation coexists. The unusual magnetic ordering is phenomenologically understood via Landau theory with the primary order parameter Γ4 coupled with higher-order secondary order parameter Γ10. The magnetic moments were estimated to be 1.8 ± 0.2 and 0.2 ± 0. 05μB at T=7.5 K for Γ4 and Γ10, respectively. We also found that a long-period incommensurate modulation of the ~q1=(0, 0, 1/2) antiferromagnetic structure of mc with the propagation ~qs1=(0.14, 0.14, 0.1) and ~qs2=(0.2, 0, 0.1) in phase III and IV, respectively. The amplitude of sinusoidal modulation was about mc = 1.0 ± 0.2μB at T=1.5 K. The local (0, 0, 1/2) structure consists of in-plane ferromagnetic and out-of-plane antiferromagnetic coupling of mc, opposite to the coexisting Γ10. The mc of Γ10 is significantly enhanced up to 0. 6μB at T=1.5 K, which is accompanied by the incommensurate modulations. The Landau phenomenological approach indicates the higer-order magnetic and/or multipole interactions based on pseudo-quartet f -electron state play important role. ∗ metoki.naoto@jaea.go. jp
One-sided portrayal: Singapore media discourse of social scientists’ roles

ABSTRACT This article analyses the gap between the news media’s and scholars’ portrayals of social scientists’ roles in Singapore. It adopts a media representation theoretical framework, undertaking a media discourse analysis of the Singapore Select Committee on Deliberate Online Falsehood between January 2018 and October 2019. Findings show the media’s consistent depiction of the instrumental roles of social scientists via avoidance of academic language and social-political critiques. The article argues that Singapore’s news media portrayal emphasises the instrumental orientation of social scientists’ roles. The study contributes a social-cultural account in the Singapore context, reframing the media representation framework that has hitherto been understood from a Western perspective.
Biocompatible scaffolds based on collagen and oxidized dextran for endothelial cell survival and function in tissue engineering

Angiogenesis is a vital step in tissue regeneration. Hence, the current study aimed to prepare oxidized dextran (Odex)/collagen (Col)‐hydrogels with laminin (LMN), as an angiogenic extracellular matrix (ECM) component, for promoting human umbilical vein endothelial cell (HUVEC) proliferation and function. Odex/Col scaffolds were constructed at various concentrations and temperatures. Using oscillatory rheometry, scanning electron microscopy (SEM), and cell viability testing, the scaffolds were characterized, and then HUVEC proliferation and function was compared with or without LMN. The gelation time could be modified by altering the Odex/Col mass ratio as well as the temperature. SEM showed that Odex/Col hydrogels had a more regular three‐dimensional (3D) porous structure than the Col hydrogels. Moreover, HUVECs grew faster in the Col scaffold (12 mg/mL), whereas the Odex (30 mg/mL)/Col (6 mg/mL) scaffold exhibited the lowest apoptosis index. Furthermore, the expression level of vascular endothelial growth factor (VEGF) mRNA in the group without LMN was higher than that with LMN, and the Odex (30 mg/mL)/Col (6 mg/mL) scaffold without LMN had the highest VEGF protein secretion, allowing the cells to survive and function effectively. Odex/Col scaffolds, with or without LMN, are proposed as a tissue engineering construct to improve HUVEC survival and function for angiogenesis.
Investigation of optical turbulence from an unmanned aerial system

Wave propagation of electro-optical systems, lasers or imaging depends on the state of the atmosphere their beams are passing through. Fluctuations in the refractive index of air are responsible for signal attenuation and image degradation. This atmospheric effect is called optical turbulence and its strength is quantified by the structure function parameter of the refractive index 𝐶2𝑛 . In the atmospheric surface layer it is highly variable. Usually 𝐶2𝑛 decreases with height. In non-uniform terrain, big horizontal variations can arise. We developed a mobile airborne system for monitoring 𝐶2𝑛 to investigate the three-dimensional character of optical turbulence. Therefore the dodecacopter system HORUS (Hovering Remote controlled Ultra-light Sensor-platform (AIRCLIP /Dresden/Germany)) was chosen as mobile platform. An ultrasonic anemometer was mounted on a boom for high-resolution measurements of temperature and wind speed. Analyzing the time series of temperature, 𝐶2𝑛 values were derived from time averages of several minutes. The measurements took place in the surface layer over land in the vicinity of an 80 m high tower, equipped with ultrasonic anemometers at four discrete heights. Comparison measurements were performed. The minimum length of the boom outside the turbulent influence of the rotors was investigated. The comparison of the 𝐶2𝑛 values shows a good agreement. A second, smaller quadrocopter system in combination with a new very small and light-weight ultrasonic anemometer was also tested for turbulence measurements. The system is introduced and the applicability shown. Results from first field trials are presented and discussed.
Sublinear-Time Learning and Inference for High-Dimensional Models

Across domains, the scale of data and complexity of models have both been increasing greatly in the recent years. For many models of interest, tractable learning and inference without access to expensive computational resources have become challenging. In this thesis, we approach efficient learning and inference through the leverage of sparse structures inherent in the learning objective, which allows us to develop algorithms sublinear in the size of parameters without compromising the accuracy of models. In particular, we address the following three questions for each problem of interest: (a) how to formulate model estimation as an optimization problem with tractable sparse structure, (b) how to efficiently, i.e. in sublinear time, search, maintain, and utilize the sparse structures during training and inference, (c) how to guarantee fast convergence of our optimization algorithm despite its greedy nature? By answering these questions, we develop state-of-the-art algorithms in varied domains. Specifically, in the extreme classification domain, we utilizes primal and dual sparse structures to develop greedy algorithms of complexity sublinear in the number of classes, which obtain state-of-the-art accuracies on several benchmark data sets with one or two orders of magnitude speedup over existing algorithms. We also apply the primal-dual-sparse theory to develop a state-of-the-art trimming algorithm for Deep Neural Networks, which sparsifies neuron connections of a DNN with a task-dependent theoretical guarantee, which results in models of smaller storage cost and faster inference speed. When it comes to structured prediction problems (i.e. graphical models) with inter-dependent outputs, we propose decomposition methods that exploit sparse messages to decompose a structured learning problem of large output domains into factorwise learning modules amenable to sublineartime optimization methods, leading to practically much faster alternatives to existing learning algorithms. The decomposition technique is especially effective when combined with search data structures, such as those for Maximum Inner-Product Search (MIPS), to improve the learning efficiency jointly. Last but not the least, we design novel convex estimators for a latent-variable model by reparameterizing it as a solution of sparse support in an exponentially high-dimensional space, and approximate it with a greedy algorithm, which yields the first polynomial-time approximation method for the Latent-Feature Models and Generalized Mixed Regression without restrictive data assumptions.
Two-phase outcome-dependent sampling designs for sequential survival time analysis

In some observational studies, the covariates of interest might be expensive to measure although the outcome variable could easily be obtained. In this situation, a costefficient two-phase outcome-dependent sampling design could be employed to measure the expensive covariate for more informative subjects. In phase one, all members of a random sample from a population or a cohort are measured for the outcome variable and inexpensive covariates. In phase two, a subset of the cohort is selected based on the outcome variable, and the expensive covariate is measured only for the selected individuals. Case-cohort design is a commonly used outcome-dependent sampling design in time-to-event analyses. In generalized case-cohort design, in which the selection probability depends only on the event indicator, a random subsample of individuals who experienced the event are selected, along with a random subsample of those with censored event times. It was previously shown that when the selection probability at phase two depends on observed event time and censoring time in addition to the event indicator, the efficiency of the design might increase. Efficient design has a lower variance of the coefficient estimate of the expensive covariate in the regression model. In this study, we consider bivariate sequential time-to-event data, which consists of gap times between two events observed in sequence, as the outcome variables. The objective of this study is to investigate efficient two-phase sampling designs for a predetermined phase two sample size. We consider sampling designs depending on the event indicators and gap times. A likelihood-based method is used to estimate the associations between the expensive covariate and the two gap times. We show that when the selection probability at phase two depends on the two observed gap times and censoring times in addition to their event indicators, the efficiency of the design might improve.
Isospeed: Improving (min, +) Convolution by Exploiting (min, +)/(max, +) Isomorphism (Artifact)

(min,+) convolution is the key operation in (min,+) algebra, a theory often used to compute performance bounds in real-time systems. As already observed in many works, its algorithm can be computationally expensive, due to the fact that: i) its complexity is superquadratic with respect to the size of the operands; ii) operands must be extended before starting its computation, and iii) said extension is tied to the least common multiple of the operand periods. In this paper, we leverage the isomorphism between (min,+) and (max,+) algebras to devise a new algorithm for (min,+) convolution, in which the need for operand extension is minimized. This al-gorithm is considerably faster than the ones known so far, and it allows us to abate the computation times of (min,+) convolution by orders of magnitude.
CoPheScan: phenome-wide association studies accounting for linkage disequilibrium

Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.
Inadequate structural constraint on Fab approach rather than paratope elicitation limits HIV-1 MPER vaccine utility

Broadly neutralizing antibodies (bnAbs) against HIV-1 target conserved epitopes, thereby inhibiting viral entry. Yet surprisingly, those recognizing linear epitopes in the HIV-1 gp41 membrane proximal external region (MPER) are elicited neither by peptide nor protein scaffold vaccines. Here, we observe that while Abs generated by MPER/liposome vaccines may exhibit human bnAb-like paratopes, B-cell programming without constraints imposed by the gp160 ectodomain selects Abs unable to access the MPER within its native “crawlspace”. During natural infection, the flexible hinge of IgG3 partially mitigates steric occlusion of less pliable IgG1 subclass Abs with identical MPER specificity, until affinity maturation refines entry mechanisms. The IgG3 subclass maintains B-cell competitiveness, exploiting bivalent ligation resulting from greater intramolecular Fab arm length, offsetting weak antibody affinity. These findings suggest future immunization strategies.
Combination of NY-ESO-1-TCR-T-cells coengineered to secrete SiRPα decoys with anti-tumor antibodies to augment macrophage phagocytosis

The adoptive transfer of T cell receptor (TCR)-engineered T cells (ACT) targeting the HLA-A2 restricted cancer-testis epitope NY-ESO-1157-165 (A2/NY) has yielded favorable clinical responses against a variety of cancers. Two promising approaches to improve ACT efficacy are TCR affinity-optimization and combinatorial treatment strategies to reprogram the tumor microenvironment (TME). By computational design, we previously developed a panel of affinity-enhanced A2/NY-TCRs. Here, we have demonstrated improved tumor control and engraftment by T cells gene-modified to express one such TCR comprising a single amino acid replacement in CDR3β (A97L). To harness macrophages in the TME, we coengineered TCR-T cells to constitutively or inducibly secrete a high-affinity signal regulatory protein alpha (SiRPα) decoy (CV1) to block the CD47 ‘don’t eat me’ signal. We demonstrated better control of tumor outgrowth by CV1-Fc coengineered TCR-T cells but in subcutaneous xenograft tumor models we observed depletion of both CV1-Fc and CV1 coengineered T cells. Importantly, CV1 coengineered T cells were not depleted by human macrophages in vitro. Moreover, Avelumab and Cetuximab enhanced macrophage-mediated phagocytosis in vitro in the presence of CV1, and augmented tumor control upon ACT. Taken together, our study indicates important clinical promise for harnessing macrophages by combining CV1 coengineered TCR-T cells with tumor-targeting monoclonal antibodies.
Implementing a framework of carbon and nitrogen feedback responses into a plant resource allocation model

The allocation of resources to roots and shoots can greatly alter total plant mass. Allocation is thought to be the consequence of growth processes (i.e uptake rates, transport rates, growth rates) and the communication between them via signalling mechanisms. Feedbacks that alter growth processes are induced in nature by changes in the internal pools of carbon and nitrogen, but how these function together to define allocation remains unclear. We introduce a framework model of internal feedback responses to changes in plant carbon and nitrogen concentrations. We evaluate how well the model responds to changes in carbon and nitrogen availability by simulating external environmental perturbations that influence the uptake of resources. The model reflects experimental results when looking at the effect of atmospheric CO2 and soil nitrogen concentrations on total plant mass and replicates observed responses to leaf defoliation events. Overall this shows that a combination of known signalling mechanisms are sufficient to reproduce experimentally observed responses to external resource availability. Model simulations highlight key areas of uncertainty where more empirical data is needed. In particular, quantitative data is needed to establish the strengths and rates at which feedback responses to carbon and nitrogen substrate concentrations alter growth and uptake rates.
Zona incerta dopamine neurons encode motivational vigor in food seeking

Energy deprivation triggers food seeking to ensure homeostatic consumption, but the neural coding of motivational vigor in food seeking during physical hunger remains unknown. Here, we report that ablation of dopamine (DA) neurons in zona incerta (ZI) but not ventral tegmental area potently impaired food seeking after fasting. ZI DA neurons were quickly activated for food approach but inhibited during food consumption. Chemogenetic manipulation of ZI DA neurons bidirectionally regulated feeding motivation to control meal frequency but not meal size for food intake. In addition, activation of ZI DA neurons and their projections to paraventricular thalamus transited positive-valence signals to promote acquisition and expression of contextual food memory. Together, these findings reveal that ZI DA neurons encode motivational vigor in food seeking for homeostatic eating. One Sentence Summary Activation of ZI DA neurons vigorously drives and maintains food-seeking behaviors to ensure food consumption triggered by energy deprivation through inhibitory DAZI-PVT transmissions that transit positive-valence signals associated with contextual food memory.
Sequence basis of transcription initiation in human genome

Transcription initiation is an essential process for ensuring proper function of any gene, however, a unified understanding of sequence patterns and rules that determine transcription initiation sites in human genome remains elusive. By explaining transcription initiation at basepair resolution from sequence with a deep learning-inspired explainable modeling approach, here we show that simple rules can explain the vast majority of human promoters. We identified key sequence patterns that contribute to human promoter function, each activating transcription with a distinct position-specific effect curve that likely reflects its mechanism of promoting transcription initiation. Most of these position-specific effects have not been previously characterized, and we verified them using experimental perturbations of transcription factors and sequences. We revealed the sequence basis of bidirectional transcription at promoters and links between promoter selectivity and gene expression variation across cell types. Additionally, by analyzing 241 mammalian genomes and mouse transcription initiation site data, we showed that the sequence determinants are conserved across mammalian species. Taken together, we provide a unified model of the sequence basis of transcription initiation at the basepair level that is broadly applicable across mammalian species, and shed new light on basic questions related to promoter sequence and function.
Medium-term planning of vehicle-to-grid aggregators for providing frequency regulation service

Utilization of electric vehicles’ battery to provide frequency regulation service in electricity markets is a technically feasible and economically attractive idea. The role of aggregators as a middleman between electric vehicle owners and the frequency regulation market has been discussed in literature. However, the economic interaction between the aggregator and the vehicle owners on division of interests is still a missing point. In this paper, a new pricing model for aggregators of electric vehicles is proposed in a way, that not only maximizes its profit, but also the vehicle owners have sufficient incentives to take part in the offered Vehicle-to-Grid program. In our proposed model, the aggregator takes into account the depreciation cost of electric vehicle batteries and the cost of net energy transaction between the electric vehicles and the grid, and considers these items in settling accounts with vehicle owners. The proposed model has been implemented on PJM frequency regulation market and the results are discussed in the paper.
TopoPyScale: A Python Package for Hillslope Climate Downscaling

Global climate reanalyses and projections are available worldwide for the past and coming century. However, model grids remain too coarse to be directly relevant at the hillslope-scale (Fan et al., 2019), requiring adapted downscaling tools to account for the effects of local topography. Mountain regions are experiencing accelerated warming with the cryosphere rapidly responding to climate change. To understand and study geomorphological, hydrological, and glaciological changes, we need tools to downscale meteorological timeseries for the basic atmospheric variables used to solve surface energy and mass balance in process-based models. Advanced dynamical downscaling methods exist, though they come with a high computational cost and complex technical setup (Kruyt et al., 2022). TopoPyScale uses a pragmatic approach to downscaling by minimizing complexity, reducing computational cost, simplifying interoperability with land surface models, while retaining physical coherence and allowing the primary drivers of land surface-atmosphere interaction to be considered. The toolbox is designed to be flexible in its usage and development.
On efficient computation in active inference

Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its computational cost and the difficulty of specifying an appropriate target distribution for the agent. This paper introduces two solutions that work in concert to address these limitations. First, we present a novel planning algorithm for finite temporal horizons with drastically lower computational complexity. Second, inspired by Z-learning from control theory literature, we simplify the process of setting an appropriate target distribution for new and existing active inference planning schemes. Our first approach leverages the dynamic programming algorithm, known for its computational efficiency, to minimize the cost function used in planning through the Bellman-optimality principle. Accordingly, our algorithm recursively assesses the expected free energy of actions in the reverse temporal order. This improves computational efficiency by orders of magnitude and allows precise model learning and planning, even under uncertain conditions. Our method simplifies the planning process and shows meaningful behaviour even when specifying only the agent's final goal state. The proposed solutions make defining a target distribution from a goal state straightforward compared to the more complicated task of defining a temporally informed target distribution. The effectiveness of these methods is tested and demonstrated through simulations in standard grid-world tasks. These advances create new opportunities for various applications.