Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases

Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic forgetting issue, an Adapter-based Continual Learning framework called ACL is proposed to help effectively learn a set of new diseases at each round (or task) of continual learning, without changing the shared feature extractor. The learnable lightweight task-specific adapter(s) can be flexibly designed (e.g., two convolutional layers) and then added to the pretrained and fixed feature extractor. Together with a specially designed task-specific head which absorbs all previously learned old diseases as a single"out-of-distribution"category, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are comparable and consequently the appropriate classifier head can be more accurately selected during model inference. Extensive empirical evaluations on three image datasets demonstrate the superior performance of ACL in continual learning of new diseases. The source code is available at https://github.com/GiantJun/CL_Pytorch.
Astrophysical neutrino point sources as a probe of new physics

Recently, the IceCube collaboration observed a neutrino excess in the direction of NGC 1068 with high statistical significance. This constitutes the second detection of an astrophysical neutrino point source after the discovery of a variable emission originating from the blazar TXS~0506+056. Neutrinos emitted by these sources traverse huge, well-determined distances on their way to Earth. This makes them a promising tool to test new physics in the neutrino sector. We consider secret interactions with the cosmic neutrino background and discuss their impact on the flux of neutrino point sources. The observation of emission from NGC 1068 and TXS 0506+056 can then be used to put limits on the strength of the interaction. We find that our ignorance of the absolute neutrino masses has a strong impact and, therefore, we present limits in two benchmark scenarios with the sum of the neutrino masses around their lower and upper limits.
An application of Sparre Andersen's fluctuation theorem for exchangeable and sign-invariant random variables

We revisit here a famous result by Sparre Andersen on persistence probabilities $\mathbf{P}(S_k>0 \;\forall\, 0\leq k\leq n)$ for symmetric random walks $(S_n)_{n\geq 0}$. We give a short proof of this result when considering sums of random variables that are only assumed exchangeable and sign-invariant. We then apply this result to the study of persistence probabilities of (symmetric) additive functionals of Markov chains, which can be seen as a natural generalization of integrated random walks.
Gene Activity as the Predictive Indicator for Transcriptional Bursting Dynamics

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. In this study, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict the average ON and OFF periods of individual alleles. Lowly transcribing alleles predominantly modulate OFF durations (burst frequency), while highly transcribing alleles primarily tune ON durations (burst size). Importantly, these relationships persist even under perturbation of cis-regulatory elements or trans-factors. This suggests a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes. Our study provides a foundation for future investigations into the molecular mechanisms underpinning spatiotemporal transcriptional control.
How human-derived brain organoids are built differently from brain organoids derived of genetically-close relatives: A multi-scale hypothesis

How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts are underway to solve this puzzle via quantification of gene expression, chromatin organization, cellular structure, and tissue structure, computational modeling efforts remain far behind. To help accelerate the computational modeling efforts, we review two recent publications, the first on a cellular-based model for tissues and the second on a model of a cell nucleus that includes a lamina shell and chromatin. We then address how the two models can be combined to ultimately test multiscale hypotheses linking the chromatin scale and the tissue scale. To be concrete, we turn to an in vitro system for the brain known as a brain organoid. We provide a multiscale hypothesis to distinguish structural differences between brain organoids built from induced-pluripotent human stem cells and from induced-pluripotent gorilla and chimpanzee stem cells. Recent experiments discover that a cell fate transition from neuroepithelial cells to radial glial cells includes a new intermediate state that is delayed in human-derived brain organoids as compared to their genetically-close relatives, which significantly narrows and lengthens the cells on the apical side [1]. Additional experiments revealed that the protein ZEB2 plays a major role in the emergence of this new intermediate state with ZEB2 mRNA levels peaking at the onset of the emergence [1]. We postulate that the enhancement of ZEB2 expression driving this intermediate state is potentially due to chromatin reorganization. More precisely, there exists critical strain triggering the reorganization that is higher for human-derived stem cells, thereby resulting in a delay. Such a hypothesis can readily be tested experimentally within individual cells and within brain organoids as well as computationally to help work towards solving the gene-to-tissue organization puzzle.
Fate of dissipative hierarchy of timescales in the presence of unitary dynamics

The generic behavior of purely dissipative open quantum many-body systems with local dissipation processes can be investigated using random matrix theory, revealing a hierarchy of decay timescales of observables organized by their complexity as shown in [Wang et al., Phys. Rev. Lett. 124, 100604 (2020)]. This hierarchy is reflected in distinct eigenvalue clusters of the Lindbladian. Here, we analyze how this spectrum evolves when unitary dynamics is present, both for the case of strongly and weakly dissipative dynamics. In the strongly dissipative case, the unitary dynamics can be treated perturbatively and it turns out that the locality of the Hamiltonian determines how susceptible the spectrum is to such a perturbation. For the physically most relevant case of (dissipative) two-body interactions, we find that the correction in the first order of the perturbation vanishes, leading to the relative robustness of the spectral features. For weak dissipation, the spectrum flows into clusters with well-separated eigenmodes, which we identify to be the local symmetries of the Hamiltonian.
Location and thermal evolution of the pseudogap due to spin fluctuations

We study pseudogap behavior in a metal near a spin density wave (SDW) instability due to thermal magnetic fluctuations. We consider the $t-t'$ Hubbard model on a square lattice at a finite doping, at intermediate coupling strength, and analyze the thermal evolution of the electron spectral function between a SDW ordered state at low temperatures and a normal Fermi liquid at high temperatures. We argue that for proper description of the pseudogap one needs to sum up infinite series of diagrams for both the fermionic self-energy and the SDW order parameter in the SDW state or the magnetic correlation length in the paramagnetic state. We use the eikonal approach to sum up an infinite series of diagrammatic contributions from thermal fluctuations. Earlier studies found that in the SDW state, the spectral function $A_{\bf k}(\omega)$ of a hot fermion at a finite $T$ is exponentially small below the energy scale $\Delta (T)$, which scales with SDW order and vanishes at the ordering temperature $T_N$, and has a hump at a larger frequency $\Delta_{\rm pg}$, comparable to the zero-temperature SDW gap $\Delta (T=0)$. We argue that the hump, which we associate with the pseudogap, survives in some $T$ range above $T_N$. We show that this range is split by regions of strong and weak pseudogap behavior. In the first region, $\Delta_{\rm pg}$ is weakly temperature dependent, despite that it comes from thermal fluctuations. Such a behavior has been seen in numerical studies of the Hubbard model. We show that to obtain it, one needs to go beyond the one-loop approximation and sum up the infinite series of diagrams. In the second regime, $\Delta_{\rm pg}$ decreases with increasing $T$ and eventually vanishes. We further argue that a magnetic pseudogap at a finite $T$ emerges only if the ground state is magnetically ordered. We present the phase diagram and apply the results to high-$T_c$ cuprates.
A conjectural chain model for positive $S^1$-equivariant symplectic homology of star-shaped toric domains in $\mathbb{C}^2$

For any star-shaped toric domain in $\mathbb{C}^2$, we define a filtered chain complex which conjecturally computes positive $S^1$-equivariant symplectic homology of the domain. Assuming this conjecture, we show that the limit $\lim_{k \to \infty} c^{\mathrm{GH}}_k(X)/k$ exists for any star-shaped toric domain $X \subset \mathbb{C}^2$, where $c^{\mathrm{GH}}_k$ denotes the $k$-th Gutt-Hutchings capacity.
GrOVe: Ownership Verification of Graph Neural Networks using Embeddings

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the node. Embeddings generated by a GNN for a graph node are unique to that GNN. Prior work has shown that GNNs are prone to model extraction attacks. Model extraction attacks and defenses have been explored extensively in other non-graph settings. While detecting or preventing model extraction appears to be difficult, deterring them via effective ownership verification techniques offer a potential defense. In non-graph settings, fingerprinting models, or the data used to build them, have shown to be a promising approach toward ownership verification. We present GrOVe, a state-of-the-art GNN model fingerprinting scheme that, given a target model and a suspect model, can reliably determine if the suspect model was trained independently of the target model or if it is a surrogate of the target model obtained via model extraction. We show that GrOVe can distinguish between surrogate and independent models even when the independent model uses the same training dataset and architecture as the original target model. Using six benchmark datasets and three model architectures, we show that consistently achieves low false-positive and false-negative rates. We demonstrate that is robust against known fingerprint evasion techniques while remaining computationally efficient.
Identification of Latent Classes in Mixture Models: A Monte Carlo Simulation Study

Author(s): McDonald, Stefanie R. | Advisor(s): Marcoulides, George A. | Abstract: Factor mixture modeling is an increasingly popular method used in applied research settings that combines latent class models and common factor models. The method is often used to detect the presence of heterogeneous populations (i.e., latent classes) that differ on one or more population parameters of a factor model in a sample that lacks a priori information regarding population membership. Researchers have faced many challenges when using the factor mixture modeling method. The present study employs a Monte Carlo approach to compare the performance of factor mixture modeling with three other tenable methods: Q-Factor Analysis, Marcoulides and Drezner Model, and McDonald Eigen Analysis, in detecting latent classes in a sample. Data were simulated by manipulating six design factors: sample size, number of classes, mixing proportion, class separation, distributional characteristics, and complexity of within-class factor structures. The performance of each method was assessed by determining the proportion of replications for which each method detected the correct number of latent classes (i.e., populations) and Cohen's h effect size for each manipulated design factor. Overall, the factor mixture model performed best. In particular, the sample size, number of classes, and distributional characteristic of the latent variable significantly impacted the ability of factor mixture model to detect the correct number of classes. The McDonald Eigen Analysis performed second best, overall. The method was only significantly influenced by the number of classes. The other design conditions had a small or negligent influence on the performance. Q-Factor Analysis was influenced most by the complexity of the within-class factor structure and the number of classes. Finally, Marcoulides and Drezner Model was most influenced by the sample size and the number of classes.From the results, it can be concluded that under certain design conditions, less parameterized and less complex methods perform just as well as factor mixture modeling in detecting latent classes. Researchers can be guided in determining the best method to use based on the characteristics of their data.
2023 More Robust Estimators for Panel Bartik Designs, With An Application to the Eﬀect of Imports from China on US Employment

We show that panel Bartik regressions identify non-convex combinations of location-and-period-speciﬁc treatment eﬀects. Thus, those regressions could be biased if eﬀects are heterogeneous. We propose an alternative instrumental-variable correlated-random-coeﬃcient (IV-CRC) estimator, that is more robust to heterogeneous eﬀects. We revisit Autor et al. (2013), who use a panel Bartik regression to estimate the eﬀect of imports from China on US manufacturing employment. Their regression estimates a highly non-convex combination of eﬀects, and our IV-CRC estimator is small and insigniﬁcant: without assuming constant treatment eﬀects, one cannot conclude from their data that imports from China negatively aﬀected US employment.
Evolution of matter and galaxy clustering in cosmological hydrodynamical simulations

We quantify the evolution of matter and galaxy clustering in cosmological hydrodynamical simulations via correlation and bias functions of matter and galaxies. We use simulations TNG100 and TNG300 with epochs from z = 5 to z = 0. We calculate spatial correlation functions (CFs) of galaxies, ξ(r), for simulated galaxies and dark matter (DM) particles to characterize the evolving cosmic web. We find that bias parameters decrease during the evolution, confirming earlier results. Bias parameters of the lowest luminosity galaxies, b0, estimated from CFs are lower relative to CFs of particle density-limited clustered samples of DM. At low and medium luminosities, bias parameters of galaxies are equal, suggesting that dwarf galaxies reside in the same filamentary web as brighter galaxies. We find that bias parameters b0, estimated from CFs of clustered DM, agree with the expected values from the fraction of particles in the clustered population, b = 1/Fc. The cosmic web contains filamentary structures of various densities, and fractions of matter in the clustered and the unclustered populations are both less than unity. Thus, the CF amplitude of the clustered matter is always higher than that for all matter, i.e. bias parameter must be b > 1. Differences between CFs of galaxies and clustered DM suggest that these functions describe different properties of the cosmic web.
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization

Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propose a bilevel extension of the celebrated SARAH algorithm. We demonstrate that the algorithm requires $\mathcal{O}((n+m)^{\frac12}\varepsilon^{-1})$ oracle calls to achieve $\varepsilon$-stationarity with $n+m$ the total number of samples, which improves over all previous bilevel algorithms. Moreover, we provide a lower bound on the number of oracle calls required to get an approximate stationary point of the objective function of the bilevel problem. This lower bound is attained by our algorithm, making it optimal in terms of sample complexity.
From fractal R-L ladder networks to the diffusion equation

We give a self-contained presentation of fractal R-L ladder networks as well as a detailed computation of the admittance of these systems. We also discuss the conditions under which such systems display a fractional behavior. Finally, we give a full discussion of the connection existing between fractal R-L network and the diffusion equation.
The half-filled extended Hubbard model on a square lattice: Phase boundaries from determinant quantum Monte Carlo simulations

The extended Hubbard model (EHM) describes fermions on a lattice coupled through on-site, $U$, and first-neighbor, $V$, interactions. In the context of high-$T_c$ cuprates, antiferromagnetic fluctuations may lead to an attractive channel, hence to superconductivity. Despite interest in the two-dimensional version of the model, the current knowledge about the phase diagram is still far from complete. Here, we report on the results of extensive determinant quantum Monte Carlo simulations for this model at half filling, in which we have used the average sign of the product of fermionic determinants as an additional observable to locate critical points. We arrive at a ground state phase diagram in the $U$-$V$ plane in which the boundaries involving antiferromagnetic, charge-ordered, $s$- and $d$-wave superconductivity, and phase-separated phases are quantitatively set with good accuracy. We have also proposed a partial phase diagram, $T_c(U,V)$, featuring critical temperatures for the CDW and $s$-wave superconducting phases.
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion

Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r. t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it possible to calculate scale once during inference as a post-processing step and use it over-time. With this as a goal, a set of temporal consistency losses that minimize pose inconsistencies over time are introduced. Evaluations show that introducing these constraints not only reduces depth inconsistencies but also improves the baseline performance of depth and ego-motion prediction.
The role of plasma–atom and molecule interactions on power & particle balance during detachment on the MAST Upgrade Super-X divertor

This paper shows first quantitative analysis of the detachment processes in the MAST Upgrade Super-X divertor (SXD). We identify an unprecedented impact of plasma-molecular interactions involving molecular ions (likely D2+ ), resulting in strong ion sinks (Molecular Activated Recombination—MAR), leading to a reduction of ion target flux. The MAR ion sinks exceed the divertor ion sources before electron-ion recombination (EIR) starts to occur, suggesting that significant ionisation occurs outside of the divertor chamber. In the EIR region, Te≪0.2 eV is observed and MAR remains significant in these deep detached phases. The total ion sink strength demonstrates the capability for particle (ion) exhaust in the Super-X Configuration. Molecular Activated Dissociation is the dominant volumetric neutral atom creation process can lead to an electron cooling of 20% of PSOL . The measured total radiative power losses in the divertor chamber are consistent with inferred hydrogenic radiative power losses. This suggests that intrinsic divertor impurity radiation, despite the carbon walls, is minor in the divertor chamber. This contrasts previous TCV results, which may be associated with enhanced plasma-neutral interactions and reduced chemical erosion in the detached, tightly baffled SXD. The above observations have also been observed in higher heat flux (narrower SOL width) type I ELMy H-mode discharges. This provides evidence that the characterisation in this paper may be general.
Characterization of a superconducting microstrip single-photon detector shunted with an external resistor

A superconducting microstrip single-photon detector (SMSPD) generally requires a shunt resistor to avoid latching, caused by its high current-carrying capacity and low kinetic inductance. Here, the effect of the shunt resistor on the behaviors of microbridge SMSPDs was investigated. We analyzed the change in equivalent switching current at different shunt resistances in two ways and determined the operating current range using intrinsic dark count rate (iDCR) curves. We observed that the reduction in shunt resistance can increase the operating current range, which helps to improve the internal detection efficiency (IDE) and reduce the iDCR. However, the reduction in the shunt resistance can reduce the pulse amplitude and increase the pulse decay time, which can degrade the timing jitter and count rate performance of the SMSPD. The trends of the experimental results can be qualitatively reproduced using a circuit model for an SMSPD with a shunt resistor, which provides useful information for the selection of shunt resistors. Furthermore, we report the improved detection performance of a helium-ion-irradiated SMSPD shunted with a small resistance of 5.2 Ω. We observed a weak IDE saturation with a bias current at a wavelength up to 2000 nm and a nonlinear relation between detection current and photon energy.
Hyperbolic Image-Text Representations

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept"dog"entails all images that contain dogs. Despite being intuitive, current large-scale vision and language models such as CLIP do not explicitly capture such hierarchy. We propose MERU, a contrastive model that yields hyperbolic representations of images and text. Hyperbolic spaces have suitable geometric properties to embed tree-like data, so MERU can better capture the underlying hierarchy in image-text datasets. Our results show that MERU learns a highly interpretable and structured representation space while being competitive with CLIP's performance on standard multi-modal tasks like image classification and image-text retrieval. Our code and models are available at https://www.github. com/facebookresearch/meru