Miracle-dz/UniFAR-FTUs huggingface dataset:
Immune Regulation of Intestinal Stem Cell Proliferation and Differentiation in Drosophila

Intestinal progenitor cells integrate signals from their niche, and from the gut lumen, to divide and differentiate at a rate that maintains an epithelial barrier to microbial invasion of the host interior. Despite the importance of evolutionarily-conserved innate immune defenses for the maintenance of stable host-microbiota relationships, we know little about specific contributions of stem cell immunity to gut homeostasis. We used the Drosophila model to determine the consequences of compromised intestinal stem cell immune activity for epithelial homeostasis. We showed that loss of stem cell immunity greatly impacts growth and renewal in the adult gut. In particular, we noticed that inhibition of stem cell immunity impeded key growth and differentiation events in the progenitor cell compartment leading to a gradual loss of stem cell numbers with age, and impaired differentiation of mature enteroendocrine cells. Our results emphasize the importance of immune signaling in the stem cell population for epithelial function in the adult gut.
Interferon alpha-based combinations suppress SARS-CoV-2 infection in vitro and in vivo

There is an urgent need for new antivirals with powerful therapeutic potential and tolerable side effects. In the present study, we found that recombinant human interferon-alpha (IFNa) triggers intrinsic and extrinsic cellular antiviral responses, as well as reduces replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in vitro. Although IFNa alone was insufficient to completely abolish SARS-CoV-2 replication, combinations of IFNa with remdesivir or other antiviral agents (EIDD-2801, camostat, cycloheximide, or convalescent serum) showed strong synergy and effectively inhibited SARS-CoV-2 infection in human lung epithelial Calu-3 cells. Furthermore, we showed that the IFNa-remdesivir combination suppressed virus replication in human lung organoids, and that its single prophylactic dose attenuated SARS-CoV-2 infection in lungs of Syrian hamsters. Transcriptome and metabolomic analyses showed that the combination of IFNa-remdesivir suppressed virus-mediated changes in infected cells, although it affected the homeostasis of uninfected cells. We also demonstrated synergistic antiviral activity of IFNa2a-based combinations against other virus infections in vitro. Altogether, our results indicate that IFNa2a-based combination therapies can achieve higher efficacy while requiring lower dosage compared to monotherapies, making them attractive targets for further pre-clinical and clinical development.
Post-interval EEG activity is related to task-goals in temporal discrimination

Studies investigating the neural mechanisms of time perception often measure brain activity while participants perform a temporal task. However, several of these studies are based exclusively on tasks in which time is relevant, making it hard to dissociate activity related to decisions about time from other task-related patterns. In the present study, human participants performed a temporal or color discrimination task of visual stimuli. Participants were informed which magnitude they would have to judge before or after presenting the two stimuli (S1 and S2) in different blocks. Our behavioral results showed, as expected, that performance was better when participants knew beforehand which magnitude they would judge. Electrophysiological data (EEG) was analysed using Linear Discriminant Contrasts (LDC) and a Representational Similarity Analysis (RSA) approach to investigate whether and when information about time and color was encoded. During the presentation of S1, we did not find consistent differences in EEG activity as a function of the task. On the other hand, during S2, we found that temporal and color information was encoded in a task-relevant manner. Taken together, our results suggest that task goals strongly modulate decision-related information in EEG activity.
Effectiveness of portable air filtration on reducing indoor aerosol counts: preclinical observational trials

Objective: To assess the effectiveness of aerosol filtration by portable air cleaning devices with high efficiency particulate air (HEPA) filters used in addition to standard building heating ventilation and air-conditioning (HVAC). Methods: Test rooms, including a hospital single-patient room, were filled with test aerosol to simulate aerosol movement. Aerosol counts were measured over time with various portable air cleaning devices and room ventilation systems to quantify the aerosol concentration reduction rate and overall clearance rate. Results: Portable air cleaners were very effective in removing aerosols, especially for the devices with high flow rate. In a small control room, the aerosols were cleared 4 to 5 times faster with portable air cleaners than the room with HVAC alone. A single bed hospital room equipped with an excellent ventilation rate (~ 14 air changes per hour) can clear the aerosols in 20 minutes. However, with the addition of two air cleaners, the clearance time became 3 times faster (in 6 minutes and 30 seconds). Conclusions: Portable air cleaning devices with HEPA filtration were highly effective at removing aerosols. To clear aerosols (above 90% clearance) in under 10 minutes requires around 25 air changes per hour; readily feasible with air cleaners. Inexpensive portable air cleaning devices should be considered for small and enclosed spaces in health care settings such as inpatient rooms, personal protective equipment donning/doffing stations, and staff tea rooms. Portable air cleaners are particularly important where there is limited ability to reduce aerosol transmission with building HVAC ventilation.
A Unified Model of Solar Prominence Formation

Several mechanisms have been proposed to account for the formation of solar prominences or filaments, among which direct injection and evaporation–condensation models are the two most popular ones. In the direct injection model, cold plasma is ejected from the chromosphere into the corona along magnetic field lines; in the evaporation–condensation model, the cold chromospheric plasma is heated to over a million degrees and is evaporated into the corona, where the accumulated plasma finally reaches thermal instability or nonequilibrium so as to condensate to cold prominences. In this paper, we try to unify the two mechanisms: The essence of filament formation is the localized heating in the chromosphere. If the heating happens in the lower chromosphere, the enhanced gas pressure pushes the cold plasma in the upper chromosphere to move up to the corona, such a process is manifested as the direct injection model. If the heating happens in the upper chromosphere, the local plasma is heated to 1–2 million degrees, and is evaporated into the corona. Later, the plasma condensates to form a prominence. Such a process is manifested as the evaporation–condensation model. With radiative hydrodynamic simulations we confirmed that the two widely accepted formation mechanisms of solar prominences can really be unified in such a single framework. A particular case is also found where both injection and evaporation–condensation processes occur together.
SpikE: spike-based embeddings for multi-relational graph data

Despite the recent success of reconciling spike-based coding with the error backpropagation algorithm, spiking neural networks are still mostly applied to tasks stemming from sensory processing, operating on traditional data structures like visual or auditory data. A rich data representation that finds wide application in industry and research is the so-called knowledge graph - a graph-based structure where entities are depicted as nodes and relations between them as edges. Complex systems like molecules, social networks and industrial factory systems can be described using the common language of knowledge graphs, allowing the usage of graph embedding algorithms to make context-aware predictions in these information-packed environments. We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations. Learning such spike-based embeddings only requires knowledge about spike times and spike time differences, compatible with recently proposed frameworks for training spiking neural networks. The presented model is easily mapped to current neuromorphic hardware systems and thereby moves inference on knowledge graphs into a domain where these architectures thrive, unlocking a promising industrial application area for this technology.
Pseudo-Nambu-Goldstone dark matter model inspired by grand unification

A pseudo-Nambu-Goldstone boson (pNGB) is an attractive candidate for dark matter (DM) due to the simple evasion of the current severe limits of DM direct detection experiments. One of the pNGB DM models has been proposed based on a {\it gauged} $U(1)_{B-L}$ symmetry. The pNGB has long enough lifetime to be a DM and thermal relic abundance of pNGB DM can be fit with the observed value against the constraints on the DM decays from the cosmic-ray observations. The pNGB DM model can be embedded into an $SO(10)$ pNGB DM model in the framework of an $SO(10)$ grand unified theory, whose $SO(10)$ is broken to the Pati-Salam gauge group at the unified scale, and further to the Standard Model gauge group at the intermediate scale. Unlike the previous pNGB DM model, the parameters such as the gauge coupling constants of $U(1)_{B-L}$, the kinetic mixing parameter of between $U(1)_Y$ and $U(1)_{B-L}$ are determined by solving the renormalization group equations for gauge coupling constants with appropriate matching conditions. From the constraints of the DM lifetime and gamma-ray observations, the pNGB DM mass must be less than $\mathcal{O}(100)$$\,$GeV. We find that the thermal relic abundance can be consistent with all the constraints when the DM mass is close to half of the CP even Higg masses.
AstroSat observation of the HBL 1ES 1959+650 during its October 2017 flaring

We present the results of the X-ray flaring activity of 1ES 1959+650 during October 25-26, 2017 using AstroSat observations. The source was variable in the X-ray. We investigated the evolution of the X-ray spectral properties of the source by dividing the total observation period (∼ 130 ksecs) into time segments of 5 ksecs, and fitting the SXT and LAXPC spectra for each segment. Synchrotron emission of a broken power-law particle density model provided a better fit than the log-parabola one. The X-ray flux and the normalised particle density at an energy less than the break one, were found to anti-correlate with the index before the break. However, a stronger correlation between the density and index was obtained when a delay of ∼ 60 ksec was introduced. The amplitude of the normalised particle density variation |∆nγ/nγ | ∼ 0.1 was found to be less than that of the index ∆Γ ∼ 0.5. We model the amplitudes and the time delay in a scenario where the particle acceleration time-scale varies on a timescale comparable to itself. In this framework, the rest frame acceleration time-scale is estimated to be ∼ 1.97 × 10 secs and the emission region size to be ∼ 6.73 × 10 cms.
An Overview on Irreversible Port-Hamiltonian Systems

A comprehensive overview of the irreversible port-Hamiltonian system’s formulation for finite and infinite dimensional systems defined on 1D spatial domains is provided in a unified manner. The irreversible port-Hamiltonian system formulation shows the extension of classical port-Hamiltonian system formulations to cope with irreversible thermodynamic systems for finite and infinite dimensional systems. This is achieved by including, in an explicit manner, the coupling between irreversible mechanical and thermal phenomena with the thermal domain as an energy-preserving and entropy-increasing operator. Similarly to Hamiltonian systems, this operator is skew-symmetric, guaranteeing energy conservation. To distinguish from Hamiltonian systems, the operator depends on co-state variables and is, hence, a nonlinear-function in the gradient of the total energy. This is what allows encoding the second law as a structural property of irreversible port-Hamiltonian systems. The formalism encompasses coupled thermo-mechanical systems and purely reversible or conservative systems as a particular case. This appears clearly when splitting the state space such that the entropy coordinate is separated from other state variables. Several examples have been used to illustrate the formalism, both for finite and infinite dimensional systems, and a discussion on ongoing and future studies is provided.
Active learning of tree tensor networks using optimal least-squares

In this paper, we propose new learning algorithms for approximating high-dimensional functions using tree tensor networks in a least-squares setting. Given a dimension tree or architecture of the tensor network, we provide an algorithm that generates a sequence of nested tensor subspaces based on a generalization of principal component analysis for multivariate functions. An optimal least-squares method is used for computing projections onto the generated tensor subspaces, using samples generated from a distribution depending on the previously generated subspaces. We provide an error bound in expectation for the obtained approximation. Practical strategies are proposed for adapting the feature spaces and ranks to achieve a prescribed error. Also, we propose an algorithm that progressively constructs the dimension tree by suitable pairings of variables, that allows to further reduce the number of samples necessary to reach that error. Numerical examples illustrate the performance of the proposed algorithms and show that stable approximations are obtained with a number of samples close to the number of free parameters of the estimated tensor networks.
Sequential Search Models: A Pairwise Maximum Rank Approach

This paper studies sequential search models that (1) incorporate unobserved product quality, which can be correlated with endogenous observable characteristics (such as price) and endogenous search cost variables (such as product rankings in online search intermediaries); and (2) do not require researchers to know the true distribution of the match value between consumers and products. A likelihood approach to estimate such models gives biased results. Therefore, I propose a new estimator — pairwise maximum rank (PMR) estimator — for both preference and search cost parameters. I show that the PMR estimator is consistent using only data on consumers’ search order among one pair of products rather than data on consumers’ full consideration set or final purchase. Additionally, we can use the PMR estimator to test for the true match value distribution in the data. In the empirical application, I apply the PMR estimator to quantify the effect of rankings in Expedia hotel search using two samples of the data set, to which consumers are randomly assigned. I find the position effect to be $0.11-$0.36, and the effect estimated using the sample with randomly generated rankings is close to the effect estimated using the sample with endogenous rankings. Moreover, I find that the true match value distribution in the data is unlikely to be N(0,1). Likelihood estimation ignoring endogeneity gives an upward bias of at least $1.17; misspecification of match value distribution as N(0,1) gives an upward bias of at least $2.99.
Communication Topology Co-Design in Graph Recurrent Neural Network based Distributed Control

When designing large-scale distributed controllers, the information sharing constraints between sub-controllers, as defined by a communication topology interconnecting them, are as important as the controller itself. Controllers implemented using dense topologies typically out-perform those implemented using sparse topologies, but it is also desirable to minimize the cost of controller deployment. Motivated by the above, we introduce a compact but expressive graph recurrent neural network (GRNN) parameterization of distributed controllers that is well suited for distributed controller and communication topology co-design. Our proposed parameterization enjoys a local and distributed architecture, similar to previous Graph Neural Network (GNN)-based parameterizations, while further naturally allowing for joint optimization of the distributed controller and communication topology needed to implement it. We show that the distributed controller/communication topology co-design task can be posed as an ℓ1-regularized empirical risk minimization problem that can be efficiently solved using stochastic gradient methods. We run extensive simulations to study the performance of GRNN-based distributed controllers and show that (a) they achieve performance comparable to GNN-based controllers while having fewer free parameters, and (b) our method allows for performance/communication density tradeoff curves to be efficiently approximated.
AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries

This paper proposes a neural network that performs audio transformations to user-specified sources (e.g., vocals) of a given audio track according to a given description while preserving other sources not mentioned in the description. Audio Manipulation on a Specific Source (AMSS) is challenging because a sound object (i.e., a waveform sample or frequency bin) is 'transparent'; it usually carries information from multiple sources, in contrast to a pixel in an image. To address this challenging problem, we propose AMSS-Net, which extracts latent sources and selectively manipulates them while preserving irrelevant sources. We also propose an evaluation benchmark for several AMSS tasks, and we show that AMSS-Net outperforms baselines on several AMSS tasks via objective metrics and empirical verification.
Constructing and calculating Adams operations on topological modular forms

We construct stable Adams operations ψn on the cohomology theory Tmf of topological modular forms. This is done by extending the sheaf defining Tmf to be functorial with respect to isogenies of generalised elliptic curves of invertible degree. These Adams operations are then calculated on the homotopy groups of Tmf using a combination of descent spectral sequences, the theory of synthetic spectra, and Anderson duality.
A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning

In this report, we relate the algorithmic design of Barlow Twins' method to the Hilbert-Schmidt Independence Criterion (HSIC), thus establishing it as a contrastive learning approach that is free of negative samples. Through this perspective, we argue that Barlow Twins (and thus the class of negative-sample-free contrastive learning methods) suggests a possibility to bridge the two major families of self-supervised learning philosophies: non-contrastive and contrastive approaches. In particular, Barlow twins exemplified how we could combine the best practices of both worlds: avoiding the need of large training batch size and negative sample pairing (like non-contrastive methods) and avoiding symmetry-breaking network designs (like contrastive methods).
Accountable Fine-Grained Blockchain Rewriting in the Permissionless Setting

Blockchain rewriting with fine-grained access control allows a user to create a transaction associated with a set of attributes, while a modifier who possesses sufficient rewriting privileges from a trusted authority satisfying the attribute set can anonymously rewrite the transaction. However, it lacks accountability and is not designed for open blockchains that require no centralized trust authority. In this work, we introduce accountable fine-grained blockchain rewriting in a permissionless setting. The property of accountability allows the modifier’s identity and their rewriting privileges to be held accountable for the modified transactions in case of malicious rewriting. Our contributions are three-fold. First, we present a generic framework for secure blockchain rewriting in the permissionless setting. Second, we present an instantiation of our framework and show its practicality through evaluation analysis. Last, we demonstrate that our proof-of-concept implementation can be effectively integrated into open blockchains.
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols. In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. We further use neural parameterization for the new form to improve unsupervised parsing performance. We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols.
Light vector dark matter with scalar mediator and muon 
g−2
 anomaly

We study a model with a vector dark matter (DM) candidate interacting with the SM charged leptons through a scalar portal. The dark matter candidate acquires mass when the complex scalar breaks an abelian gauge symmetry spontaneously. The scalar interacts with the SM charged leptons through a dimension-6 operator. The scalar mediator induces elastic scattering of dark matter with electrons at tree level and also DM-nucleon interaction when the effects from scalar-Higgs mixing are also taken into account. Given the recent results from Xenon1T upper bounds on DM-electron elastic scattering cross section where the strongest sensitivity lies in the range $\sim {\cal O}$(1) GeV, we find the viable space in the parameter space respecting constraints from the observed relic density, direct detection, muon $(g_\mu-2)$ anomaly, $e^+ e^-$ colliders, electron beam-dump experiments and astrophysical observables. It is shown that the current upper bounds of Xenon1T on DM-electron interaction is partially sensitive to the regions in the viable parameter space which is already excluded by the electron beam-dump experiment, Orsay. We also find that there are viable DM particles with masses $\sim {\cal O}(1)$ GeV evading the direct detection but stand well above the neutrino floor. Almost the same viable regions are found when we apply the direct detection upper limits on the DM-proton spin-independent cross section.