Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA-Seq for Enrichment and Analysis of IRF5 Transcript Expression in SLE

Polymorphisms in the interferon regulatory factor 5 (IRF5) gene have been consistently replicated and shown to confer risk for or protection from the development of systemic lupus erythematosus (SLE). IRF5 expression is significantly upregulated in SLE patients and upregulation associates with IRF5-SLE risk haplotypes. IRF5 alternative splicing has also been shown to be elevated in SLE patients. Given that human IRF5 exists as multiple alternatively spliced transcripts with distinct function(s), it is important to determine whether the IRF5 transcript profile expressed in healthy donor immune cells is different from that expressed in SLE patients. Moreover, it is not currently known whether an IRF5-SLE risk haplotype defines the profile of IRF5 transcripts expressed. Using standard molecular cloning techniques, we identified and isolated 14 new differentially spliced IRF5 transcript variants from purified monocytes of healthy donors and SLE patients to generate an IRF5 variant transcriptome. Next-generation sequencing was then used to perform in-depth and quantitative analysis of full-length IRF5 transcript expression in primary immune cells of SLE patients and healthy donors by next-generation sequencing. Evidence for additional alternatively spliced transcripts was obtained from de novo junction discovery. Data from these studies support the overall complexity of IRF5 alternative splicing in SLE. Results from next-generation sequencing correlated with cloning and gave similar abundance rankings in SLE patients thus supporting the use of this new technology for in-depth single gene transcript profiling. Results from this study provide the first proof that 1) SLE patients express an IRF5 transcript signature that is distinct from healthy donors, 2) an IRF5-SLE risk haplotype defines the top four most abundant IRF5 transcripts expressed in SLE patients, and 3) an IRF5 transcript signature enables clustering of SLE patients with the H2 risk haplotype.
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving

The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies. However, crafting traffic scenarios with multiple, heterogeneous agents is typically considered as a tedious and time-consuming task, especially in more complex simulation environments. In our work, we introduce MATS-Gym, a Multi-Agent Traffic Scenario framework to train agents in CARLA, a high-fidelity driving simulator. MATS-Gym is a multi-agent training framework for autonomous driving that uses partial scenario specifications to generate traffic scenarios with variable numbers of agents. This paper unifies various existing approaches to traffic scenario description into a single training framework and demonstrates how it can be integrated with techniques from unsupervised environment design to automate the generation of adaptive auto-curricula. The code is available at https://github.com/AutonomousDrivingExaminer/mats-gym.
Non-stationary Bandits with Habituation and Recover Dynamics and Knapsack Constraints

Multi-armed bandit models have proven to be useful in modeling many real world problems in the areas of control and sequential decision making with partial information. However, in many scenarios, such as those prevalent in healthcare and operations management, the decision maker's expected reward will decrease if an action is selected too frequently while it may recover if they abstain from selecting this action. This scenario is further complicated when choosing a particular action also expends a random amount of a limited resource where the distribution is also initially unknown to the decision maker. In this paper we study a class of models that address this setting that we call reducing or gaining unknown efficacy bandits with stochastic knapsack constraints (ROGUEwK). We propose a combination upper confidence bound (UCB) and lower confidence bound (LCB) approximation algorithm for optimizing this model. Our algorithm chooses which action to play at each time point by solving a linear program (LP) with the UCB for the average rewards and LCB for the average costs as inputs. We show that the regret of our algorithm is sub-linear as a function of time and total constraint budget when compared to a dynamic oracle. We validate the performance of our algorithm against existing state of the art non-stationary and knapsack bandit approaches in a simulation study and show that our methods are able to on average achieve a 13% improvement in terms of total reward.
Immunization of Volunteers with Salmonella enterica Serovar Typhi Strain Ty21a Elicits the Oligoclonal Expansion of CD8+ T Cells with Predominant Vβ Repertoires

ABSTRACT CD8+ T cells are likely to play an important role in host defense against Salmonella enterica serovar Typhi by several effector mechanisms, including lysis of infected cells (cytotoxicity) and gamma interferon (IFN-γ) secretion. In an effort to better understand these responses, we studied the T-cell receptor (TCR) repertoire of serovar Typhi-specific CD8+ T cells in humans. To this end, we determined the TCR beta chain (Vβ) usage of CD8+ T cells from three volunteers orally immunized with Ty21a typhoid vaccine by flow cytometry using a panel of monoclonal antibodies. Although TCR Vβ usage varied among volunteers, we identified oligoclonal Vβ subset expansions in individual volunteers (Vβ 2, 5.1, 8, 17, and 22 in volunteer 1; Vβ 1, 2, 5.1, 14, 17, and 22 in volunteer 2; and Vβ 3, 8, 14, and 16 in volunteer 3). These subsets were antigen specific, as shown by cytotoxicity and IFN-γ secretion assays on Vβ sorted cells and on T-cell clones derived from these volunteers. Moreover, eight-color flow cytometric analysis showed that these clones exhibited a T effector memory phenotype (i.e., CCR7− CD27− CD45RO+ CD62L−) and coexpressed gut homing molecules (e.g., high levels of integrin α4β7, intermediate levels of CCR9, and low levels of CD103). In conclusion, our results show that long-term T-cell responses to serovar Typhi in Ty21a vaccinees are oligoclonal, involving multiple TCR Vβ families. Moreover, these serovar Typhi-specific CD8+ T cells bearing defined Vβ specificities are phenotypically and functionally consistent with T effector memory cells with preferential gut homing potential.
Layer-selective spin-orbit coupling and strong correlation in bilayer graphene

Spin-orbit coupling (SOC) and electron-electron interaction can mutually influence each other and give rise to a plethora of intriguing phenomena in condensed matter systems. In pristine bilayer graphene, which has weak SOC, intrinsic Lifshitz transitions and concomitant van-Hove singularities lead to the emergence of many-body correlated phases. Layer-selective SOC can be proximity induced by adding a layer of tungsten diselenide (WSe2) on its one side. By applying an electric displacement field, the system can be tuned across a spectrum wherein electronic correlation, SOC, or a combination of both dominates. Our investigations reveal an intricate phase diagram of proximity-induced SOC-selective bilayer graphene. Not only does this phase diagram include those correlated phases reminiscent of SOC-free doped bilayer graphene, but it also hosts unique SOC-induced states allowing a compelling measurement of valley g-factor and a seemingly impossible correlated insulator at charge neutrality, thereby showcasing the remarkable tunability of the interplay between interaction and SOC in WSe2 enriched bilayer graphene.
Photonic nanojets

We report what we believe to be the first evidence of localized photonic nanojets generated at the shadow-side surfaces of micron-scale, circular dielectric cylinders illuminated by a plane wave. Using high-resolution finite-difference time-domain (FDTD) numerical modeling, we have found that photonic nanojets have waists smaller than the diffraction limit and propagate over several optical wavelengths without significant diffraction. We have further found that such nanojets can enhance, by several orders of magnitude, the backscattering of visible light by nanometer-scale dielectric particles located within the nanojets. Not involving evanescent fields and not requiring mechanical scanning, photonic nanojets may provide a new means to detect and image nanoparticles of size well below the diffraction limit. This may provide a new ultramicroscopy technique for using visible light to detect and image nanoparticles, such as proteins, viral particles, and even single molecules; and to monitor molecular synthesis and aggregation processes of importance in many areas of biology, chemistry, material sciences, and tissue engineering.
Effect of sodium bicarbonate on prolonged running performance: A randomized, double-blind, cross-over study

Background The ability to sustain intense exercise seems to be partially limited by the body’s capability to counteract decreases in both intra- and extracellular pH. While the influence of an enhanced buffering capacity via sodium bicarbonate (BICA) on short-term, high-intensity exercise performance has been repeatedly investigated, studies on prolonged endurance performances are comparatively rare, especially for running. The aim of the following study was to assess the ergogenic effects of an oral BICA substitution upon exhaustive intensive endurance running performance. Method In a double-blind randomized cross-over study, 18 trained runners (VO2peak: 61.2 ± 6.4 ml•min-1•kg-1) performed two exhaustive graded exercise tests and two constant load tests (30 main at 95% individual anaerobic threshold (IAT) followed by 110% IAT until exhaustion) after ingestion of either sodium bicarbonate (BICA) (0.3 g/kg) or placebo (4 g NaCl) diluted in 700 ml of water. Time to exhaustion (TTE) in the constant load test was defined as the main outcome measure. Throughout each test respiratory gas exchange measurements were conducted as well as determinations of heart rate, blood gases and blood lactate concentration. Results TTE in the constant load test did not differ significantly between BICA and placebo conditions (BICA: 39.6 ± 5.6 min, placebo: 39.3 ± 5.6 min; p = 0.78). While pH in the placebo test dropped to a slightly acidotic value two minutes after cessation of exercise (7.34 ± 0.05) the value in the BICA trial remained within the normal range (7.41 ± 0.06) (p < 0.001). In contrast, maximum running speed (Vmax) in the exhaustive graded exercise test was significantly higher with BICA (17.4 ± 1.0 km/h) compared to placebo (17.1 ± 1.0 km/h) (p = 0.009). The numerical difference in maximum oxygen consumption (VO2peak) failed to reach statistical significance (BICA: 61.2 ± 6.4 ml•min-1•kg-1, placebo: 59.8 ± 6.4 ml•min-1•kg-1; p = 0.31). Maximum blood lactate was significantly higher with BICA compared to the corresponding placebo test (BICA: 11.1 ± 2.3 mmol/l, placebo: 8.9 ± 3.0 mmol/l; p < 0.001). At the end of exercise, an acidotic pH value was found in both exhaustive graded exercise tests (p = 0.002). BICA caused gastrointestinal side effects in 15 patients. Conclusion Maximal performance was enhanced significantly after BICA administration. The ergogenic effect of BICA in the exhaustive graded exercise test can most likely be attributed to an increased anaerobic glycolysis that is reflected by an accumulation of lactate. However, TTE in prolonged high-intensity running was not improved. Even at the end of exercise no severe metabolic acidosis was found. Metabolic acidification as one of the dominant factors causing muscular fatigue should therefore be reconsidered. Trial registration German Clinical Trials Register (DRKS) DRKS00011284.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

We present PlainMamba: a simple non-hierarchical state space model (SSM) designed for general visual recognition. The recent Mamba model has shown how SSMs can be highly competitive with other architectures on sequential data and initial attempts have been made to apply it to images. In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information. Our architecture is designed to be easy to use and easy to scale, formed by stacking identical PlainMamba blocks, resulting in a model with constant width throughout all layers. The architecture is further simplified by removing the need for special tokens. We evaluate PlainMamba on a variety of visual recognition tasks including image classification, semantic segmentation, object detection, and instance segmentation. Our method achieves performance gains over previous non-hierarchical models and is competitive with hierarchical alternatives. For tasks requiring high-resolution inputs, in particular, PlainMamba requires much less computing while maintaining high performance. Code and models are available at https://github.com/ChenhongyiYang/PlainMamba
Equality of magnetization and edge current for interacting lattice fermions at positive temperature

We prove that the bulk magnetization is equal to the edge current in the thermodynamic limit for a large class of models of lattice fermions with finite-range interactions satisfying local indistinguishability of the Gibbs state, a condition known to hold for sufficiently high temperatures. Our result implies that edge currents in such systems are determined by bulk properties and are therefore stable against large perturbations near the boundaries. Moreover, the equality persists also after taking the derivative with respect to the chemical potential. We show that this form of bulk-edge correspondence is essentially a consequence of homogeneity in the bulk and locality of the Gibbs state. An important intermediate result is a new version of Bloch's theorem for two-dimensional systems, stating that persistent currents vanish in the bulk.
Fast ${\cal H}$ -Matrix-Based Direct Integral Equation Solver With Reduced Computational Cost for Large-Scale Interconnect Extraction

In this paper, we propose a fast <i>H</i>-matrix-based direct solution with a significantly reduced computational cost for an integral-equation-based capacitance extraction of large-scale 3-D interconnects in multiple dielectrics. We reduce the computational cost of an <i>H</i>-matrix-based computation by simultaneously optimizing the <i>H</i>-matrix partition to minimize the number of matrix blocks and minimizing the rank of each matrix block based on a prescribed accuracy. With the proposed cost-reduction method, we develop a fast LU-based direct solver. This solver possesses a complexity of <i>kC</i><sub>sp</sub><i>O</i> (<i>N</i><b>log</b><i>N</i>) in storage, a complexity of <i>k</i><sup>2</sup><i>C</i><sub>sp</sub><sup>2</sup><i>O</i>(<i>N</i>log<sup>2</sup>N) in LU factorization, and a complexity of <i>kC</i><sub>sp</sub><i>O</i>(<i>N</i>logN) in LU solution, where <i>k</i> is the maximal rank, <i>C</i><sub>sp</sub> is a constant dependent on matrix partition, and the constant <i>kC</i><sub>sp</sub> is minimized based on accuracy by the proposed cost-reduction method. The proposed solver successfully factorizes dense matrices that involve millions of unknowns in fast CPU time and modest memory consumption, and with the prescribed accuracy satisfied. As an algebraic method, the underlying fast technique is kernel independent.
Optogenetic control of freely behaving adult Drosophila using a red-shifted channelrhodopsin

Optogenetics allows the manipulation of neural activity in freely moving animals with millisecond precision, but its application in Drosophila has been limited. Here we show that a recently described Red activatable Channelrhodopsin (ReaChR) permits control of complex behavior in freely moving adult flies, at wavelengths that are not thought to interfere with normal visual function. This tool affords the opportunity to control neural activity over a broad dynamic range of stimulation intensities. Using time-resolved activation, we show that the neural control of male courtship song can be separated into probabilistic, persistent and deterministic, command-like components. The former, but not the latter, neurons are subject to functional modulation by social experience, supporting the idea that they constitute a locus of state-dependent influence. This separation is not evident using thermogenetic tools, underscoring the importance of temporally precise control of neuronal activation in the functional dissection of neural circuits in Drosophila .
Chain rule symmetry for singular SPDEs

We characterise the chain rule symmetry for the geometric stochastic heat equations in the full subcritical regime for Gaussian and non-Gaussian noises. We show that the renormalised counter-terms that give a solution invariant under changes of coordinates are generated by iterations of covariant derivatives. The result was known only for space-time white noises, with a very specific proof that so far could not be extended to the general case. The key idea of the present paper is to change the perspective on several levels and to use ideas coming from operad theory and homological algebra. Concretely, we introduce the operad of Christoffel trees that captures the counter-terms of the renormalised equation; our main new insight is to describe the space of invariant terms homologically, using a suitable perturbation of the differential of the operadic twisting of that operad. As a consequence, we obtain the correct renormalisation for the quasi-linear KPZ equation in the subcritical regime completing the programme started by Hairer and Gerencser. Previously, the main algebraic tool used in the study of singular SPDEs were Hopf algebras of decorated trees; our work shows that operad theory and homological algebra add new powerful tools with immediate applications to open problems that were out of reach by other methods.
A TDD Distributed MIMO Testbed Using a 1-Bit Radio-Over-Fiber Fronthaul Architecture

We present the uplink and downlink of a time-division duplex distributed multiple-input multiple-output (D-MIMO) testbed, based on a 1-bit radio-over-fiber architecture, which is low-cost and scalable. The proposed architecture involves a central unit (CU) that is equipped with 1-bit digital-to-analog and analog-to-digital converters, operating at 10 GS/s. The CU is connected to multiple single-antenna remote radio heads (RRHs) via optical fibers, over which a binary RF waveform is transmitted. In the uplink, a binary RF waveform is generated at the RRHs by a comparator, whose inputs are the received RF signal and a suitably designed dither signal. In the downlink, a binary RF waveform is generated at the CU via bandpass sigma-delta modulation. Our measurement results show that low error-vector magnitude (EVM) can be achieved in both the uplink and the downlink, despite 1-bit sampling at the CU. Specifically, for point-to-point over-cable transmission between a single user equipment (UE) and a CU equipped with a single RRH, we report, for a 10 MBd signal using single-carrier 16QAM modulation, an EVM of 3.3% in the downlink, and of 4.5% in the uplink. We then consider a CU connected to 3 RRHs serving over the air 2 UEs, and show that, after over-the-air reciprocity calibration, a downlink zero-forcing precoder designed on the basis of uplink channel estimates at the CU, achieves an EVM of 6.4% and 10.9% at UE 1 and UE 2, respectively. Finally, we investigate the ability of the proposed architecture to support orthogonal frequency-division multiplexing (OFDM) waveforms, and its robustness against both in-band and out-of-band interference.
Constant-selection evolutionary dynamics on weighted networks

The population structure often impacts evolutionary dynamics. In constant-selection evolutionary dynamics between two types, amplifiers of selection are networks that promote the fitter mutant to take over the entire population, and suppressors of selection do the opposite. It has been shown that most undirected and unweighted networks are amplifiers of selection under a common updating rule and initial condition. Here, we extensively investigate how edge weights influence selection on undirected networks. We show that random edge weights make small networks less amplifying than the corresponding unweighted networks in a majority of cases and also make them suppressors of selection (i.e., less amplifying than the complete graph, or equivalently, the Moran process) in many cases. Qualitatively, the same result holds true for larger empirical networks. These results suggest that amplifiers of selection are not as common for weighted networks as for unweighted counterparts.
An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and images, has inspired researchers to delve into constructing SR from multi-modal information without using IDs. However, the complexity of multi-modal learning manifests in diverse feature extractors, fusion methods, and pre-trained models. Consequently, designing a simple and universal \textbf{M}ulti-\textbf{M}odal \textbf{S}equential \textbf{R}ecommendation (\textbf{MMSR}) framework remains a formidable challenge. We systematically summarize the existing multi-modal related SR methods and distill the essence into four core components: visual encoder, text encoder, multimodal fusion module, and sequential architecture. Along these dimensions, we dissect the model designs, and answer the following sub-questions: First, we explore how to construct MMSR from scratch, ensuring its performance either on par with or exceeds existing SR methods without complex techniques. Second, we examine if MMSR can benefit from existing multi-modal pre-training paradigms. Third, we assess MMSR's capability in tackling common challenges like cold start and domain transferring. Our experiment results across four real-world recommendation scenarios demonstrate the great potential ID-agnostic multi-modal sequential recommendation. Our framework can be found at: https://github.com/MMSR23/MMSR.
Four-gluon vertex from the Curci-Ferrari model at one-loop order

We compute the four-gluon vertex from the Curci-Ferrari model at one-loop order for a collinear configuration. Our results display a good agreement with the first lattice data for this vertex, released very recently (arXiv:2401.12008). A noteworthy novelty of our work is that we can provide analytical expressions for the four-gluon vertex in collinear configurations, together with a renormalization scheme that allows us to perform reliable perturbative computations even in the infrared regime. We observe an infrared suppression in the form factor associated with the tree-level four-gluon tensor with a possible zero-crossing in the deep infrared which demands new lattice investigations to be confirmed. Moreover, we report an infrared divergence in the completely symmetric tensor form factor due to the ghost-loop contributions. These results come as predictions since previous two-point correlations fix all the available parameters of the model, up to an overall constant factor.