Search

Numerical simulations suggest that dark matter halos surrounding galaxies host numerous small subhalos, which might be detectable by the Fermi-LAT. In this work, we revisit the search for gamma-ray subhalo candidates using the latest Fermi-LAT 4FGL-DR4 catalog. The search is performed by fitting the spectral data of unassociated point sources in the catalog through an unbinned maximum likelihood method. We consider two models in the fitting. One is an empirical function provided by the catalog, and another is a DM model in which DM particles within nearby subhalos annihilate into gamma rays and other Standard Model particles. Based on the fitting results, we identify 32 candidates for which the maximum likelihood value of the DM model fit exceeds that of the empirical function fit. The estimated J-factors of these candidates range from $0.2$ to $5.8 \times 10^{20}\,{\rm GeV^{2}\,cm^{-5}}$, the DM particle masses vary from $30$ to $500\,{\rm GeV}$ and 12 of them are within the range of $[30, 80]\,{\rm GeV}$. Candidate 4FGL J2124.2+1531 is an exception with a J-factor of $4.52 \times 10^{21}\,{\rm GeV^{2}\,cm^{-5}}$ and a particle mass of $3108.44\,{\rm GeV}$. Interestingly, the identified candidates do not overlap with those reported in previous works, and we discuss the possible reasons for the discrepancy. At the current stage, we cannot rule out the possibility that these candidates are gamma-ray pulsars, and further confirmation through multi-band observations is required., Comment: 9 pages, 3 figures, 1 table, accepted for publication in PRD

We show how to understand CRT symmetry as gauge symmetry in holographic de Sitter universe involving a pair of mirror universes, in which frame there are two times going in two opposite temporal directions each. The CRT symmetry is global with respect to the big bang for these two-sheet universes. In this construction the presence of an observer is equivalent to taking a CRT gauge, thus breaks the global CRT symmetry. In de Sitter space the observer need not to carry a clock, as the clock is equivalent to a way telling the temporal order of events. The role of a clock can be replaced by redshift, like in FLRW cosmology, points to the adaptability of measurement in cosmological contexts. This concept drives us to write gauge invariant formulas, i.e. correlation function [O(t-w),O(-t-w)]=0 identically for w the time from the observer to big bang. In this spirit, the gauge invariant formula overlaps with the fact that the Hilbert space is real in holographic de Sitter universe. The observer is highly entangled with the rest of the universe in a thermofield double state, thus contributes to no extra degrees of freedom. We show that the time $w$ to the big bang is not real singularity, but an unavoidable focus point along the timelike geodesic congruence of the observer. Our result highlights the role of observer in understanding quantum gravity., Comment: 5 pages

Photothermal microscopy is an emerging tool for measuring light-matter interactions with single-molecule sensitivity. It is generally believed that the spectral acquisition speed in photothermal microscopy is limited by the slow thermal diffusion process. Here, we demonstrate mid-infrared energy deposition (MIRED) spectroscopy, which offers both microsecond-scale temporal resolution and sub-micron spatial resolution. In this approach, the photothermal process is optically probed while the infrared pulses from a quantum cascade laser array are rapidly tuned. Based on Newton's law, the energy deposition corresponds to the first derivative of local temperature rise over time and provides the instantaneous infrared absorption. By employing time-resolved measurement of transient energy deposition, the upper limit for spectrum encoding shifts to the vibrational relaxation level, which occurs on the picosecond scale. This method significantly increases the detection bandwidth while maintaining the sensitivity and resolution advantages of photothermal detection.

Hyperspectral imaging has been widely used for spectral and spatial identification of target molecules, yet often contaminated by sophisticated noise. Current denoising methods generally rely on independent and identically distributed noise statistics, showing corrupted performance for non-independent noise removal. Here, we demonstrate Self-supervised PErmutation Noise2noise Denoising (SPEND), a deep learning denoising architecture tailor-made for removing non-independent noise from a single hyperspectral image stack. We utilize hyperspectral stimulated Raman scattering and mid-infrared photothermal microscopy as the testbeds, where the noise is spatially correlated and spectrally varied. Based on single hyperspectral images, SPEND permutates odd and even spectral frames to generate two stacks with identical noise properties, and uses the pairs for efficient self-supervised noise-to-noise training. SPEND achieved an 8-fold signal-to-noise improvement without having access to the ground truth data. SPEND enabled accurate mapping of low concentration biomolecules in both fingerprint and silent regions, demonstrating its robustness in sophisticated cellular environments.

Magnetar is proposed as one of the possible central engines for a gamma-ray burst (GRB). Recent studies show that if a magnetar has a rotational axis misaligned from the magnetic one, a periodic lightcurve pattern is expected with a period of seconds to minutes. Inspired by this unique feature, in this paper, we search for the quasi-periodic oscillation (QPO) signals in the {\it Swift} observations of GRBs. Using the Lomb-Scargle periodogram and the weighted wavelet Z-transform algorithms, we find that the {\it Swift}-BAT data of GRB 210514A has a QPO signal with a period $\sim 11\,{\rm s}$. The estimated confidence level of the signal is over $3 \sigma$. The global lightcurve of this GRB exhibits a double-plateau structure with a sharp decay segment between plateaus. The lightcurve feature resembles those of GRBs that were reported to have internal plateaus. We explain the observations of GRB 210514A with a supra-massive magnetar (SMM) model, where the QPO signal in the first plateau is produced via the dipole radiation of the SMM experiencing a precession motion, the sharp decay is due to the collapse of the SMM into a black hole (BH), and the second plateau could be produced via the fall-back accretion of the newborn BH. We fit the precession model to the observations using the Bayesian statistic and the best-fit magnetar parameters are discussed. Alternative models concerning a BH central engine may also provide reasonable explanations for this burst, only in this case the QPO signal could merely be a coincidence., Comment: 13 pages, 4 figures, accepted for publication in ApJ

Multi-fidelity machine learning methods address the accuracy-efficiency trade-off by integrating scarce, resource-intensive high-fidelity data with abundant but less accurate low-fidelity data. We propose a practical multi-fidelity strategy for problems spanning low- and high-dimensional domains, integrating a non-probabilistic regression model for the low-fidelity with a Bayesian model for the high-fidelity. The models are trained in a staggered scheme, where the low-fidelity model is transfer-learned to the high-fidelity data and a Bayesian model is trained for the residual. This three-model strategy -- deterministic low-fidelity, transfer learning, and Bayesian residual -- leads to a prediction that includes uncertainty quantification both for noisy and noiseless multi-fidelity data. The strategy is general and unifies the topic, highlighting the expressivity trade-off between the transfer-learning and Bayesian models (a complex transfer-learning model leads to a simpler Bayesian model, and vice versa). We propose modeling choices for two scenarios, and argue in favor of using a linear transfer-learning model that fuses 1) kernel ridge regression for low-fidelity with Gaussian processes for high-fidelity; or 2) deep neural network for low-fidelity with a Bayesian neural network for high-fidelity. We demonstrate the effectiveness and efficiency of the proposed strategies and contrast them with the state-of-the-art based on various numerical examples. The simplicity of these formulations makes them practical for a broad scope of future engineering applications., Comment: 33 Pages, 21 Figures

Neural Network-based active learning (NAL) is a cost-effective data selection technique that utilizes neural networks to select and train on a small subset of samples. While existing work successfully develops various effective or theory-justified NAL algorithms, the understanding of the two commonly used query criteria of NAL: uncertainty-based and diversity-based, remains in its infancy. In this work, we try to move one step forward by offering a unified explanation for the success of both query criteria-based NAL from a feature learning view. Specifically, we consider a feature-noise data model comprising easy-to-learn or hard-to-learn features disrupted by noise, and conduct analysis over 2-layer NN-based NALs in the pool-based scenario. We provably show that both uncertainty-based and diversity-based NAL are inherently amenable to one and the same principle, i.e., striving to prioritize samples that contain yet-to-be-learned features. We further prove that this shared principle is the key to their success-achieve small test error within a small labeled set. Contrastingly, the strategy-free passive learning exhibits a large test error due to the inadequate learning of yet-to-be-learned features, necessitating resort to a significantly larger label complexity for a sufficient test error reduction. Experimental results validate our findings., Comment: Accepted by the 41th Intemational Conference on Machine Learning (lCML 2024)

Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods often neglect the rationality of the augmented samples. Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences. Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. The core idea is to leverage context information to generate more reasonable augmented views. Specifically, CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. These generated items are aligned with their respective context information and can effectively replace the corresponding original items, thereby generating a positive view of the original sequence. By considering two different augmentations of the same user sequence, we can construct a pair of positive samples for contrastive learning. To ensure representation cohesion, we train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model. Extensive experiments on five benchmark datasets demonstrate the advantages of our proposed method over existing baselines.

Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in addressing these challenges. To approach the global optimum within a limited budget in a cost-efficient manner, the design of cost-aware acquisition functions (AFs) becomes a crucial step. However, traditional manual design paradigm typically requires extensive domain knowledge and involves a labor-intensive trial-and-error process. This paper introduces EvolCAF, a novel framework that integrates large language models (LLMs) with evolutionary computation (EC) to automatically design cost-aware AFs. Leveraging the crossover and mutation in the algorithmic space, EvolCAF offers a novel design paradigm, significantly reduces the reliance on domain expertise and model training. The designed cost-aware AF maximizes the utilization of available information from historical data, surrogate models and budget details. It introduces novel ideas not previously explored in the existing literature on acquisition function design, allowing for clear interpretations to provide insights into its behavior and decision-making process. In comparison to the well-known EIpu and EI-cool methods designed by human experts, our approach showcases remarkable efficiency and generalization across various tasks, including 12 synthetic problems and 3 real-world hyperparameter tuning test sets.

Finding cohesive subgraphs in a large graph has many important applications, such as community detection and biological network analysis. Clique is often a too strict cohesive structure since communities or biological modules rarely form as cliques for various reasons such as data noise. Therefore, $k$-plex is introduced as a popular clique relaxation, which is a graph where every vertex is adjacent to all but at most $k$ vertices. In this paper, we propose a fast branch-and-bound algorithm as well as its task-based parallel version to enumerate all maximal $k$-plexes with at least $q$ vertices. Our algorithm adopts an effective search space partitioning approach that provides a lower time complexity, a new pivot vertex selection method that reduces candidate vertex size, an effective upper-bounding technique to prune useless branches, and three novel pruning techniques by vertex pairs. Our parallel algorithm uses a timeout mechanism to eliminate straggler tasks, and maximizes cache locality while ensuring load balancing. Extensive experiments show that compared with the state-of-the-art algorithms, our sequential and parallel algorithms enumerate large maximal $k$-plexes with up to $5 \times$ and $18.9 \times$ speedup, respectively. Ablation results also demonstrate that our pruning techniques bring up to $7 \times$ speedup compared with our basic algorithm., Comment: Accepted by EDBT2025. Camera-ready version

Deep-tissue chemical imaging plays a vital role in biological and medical applications. Here, we present a shortwave infrared photothermal (SWIP) microscope for millimeter-deep vibrational imaging with sub-micron lateral resolution and nanoparticle detection sensitivity. By pumping the overtone transition of carbon-hydrogen bonds and probing the subsequent photothermal lens with shortwave infrared light, SWIP can obtain chemical contrast from microparticles located millimeter-deep in a highly scattering phantom. By fast digitization on the optically probed signal, the amplitude of photothermal signal is shown to be 63 times larger than that of photoacoustic signal, thus enabling highly sensitive detection of nanoscale objects. SWIP can resolve the intracellular lipids across an intact tumor spheroid and the layered structure in millimeter-thick liver, skin, brain, and breast tissues. Together, SWIP microscopy fills a gap in vibrational imaging with sub-cellular resolution and millimeter-level penetration, which heralds broad potential for life science and clinical applications., Comment: 26 pages, 10 figures

Histological examination is crucial for cancer diagnosis, including hematoxylin and eosin (H&E) staining for mapping morphology and immunohistochemistry (IHC) staining for revealing chemical information. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to mimic H&E-like morphology. Yet, the underlying chemical features are not revealed, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Through spectral unmixing in the C-H vibration window, HC-SRH can map unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. In this way, HC-SRH provides excellent contrast for various tissue components. Considering rapidness is important in clinical trials, we implemented spectral selective sampling to boost the speed of HC-SRH by one order. We also successfully demonstrated the HC-SRH in a clinical-compatible fiber laser-based SRS microscopy. With the widely rapid tuning capability of the advanced fiber laser, a clear chemical contrast of nucleic acid and solid-state ester is shown in the fingerprint result., Comment: 6 figures

Weakly interacting massive particles, as a major candidate of dark matter (DM), may directly annihilate or decay into high-energy photons, producing monochromatic spectral lines in the gamma-ray band. These spectral lines, if detected, are smoking-gun signatures for the existence of new physics. Using the 5 years of DAMPE and 13 years of Fermi-LAT data, we search for line-like signals in the energy range of 3 GeV to 1 TeV from the Galactic halo. Different regions of interest are considered to accommodate different DM density profiles. We do not find any significant line structure, and the previously reported line-like feature at $\sim$133 GeV is also not detected in our analysis. Adopting a local DM density of $\rho_{\rm local}=0.4\,{\rm GeV\,cm^{-3}}$, we derive 95% confidence level constraints on the velocity-averaged cross-section of $\langle{\sigma v}\rangle_{\gamma\gamma} \lesssim 4 \times 10^{-28}\,{\rm cm^{3}\,s^{-1}}$ and the decay lifetime of $\tau_{\gamma\nu} \gtrsim 5 \times 10^{29}\,{\rm s}$ at 100 GeV, achieving the strongest constraints to date for the line energies of 6-660 GeV. The improvement stems from the longer Fermi-LAT data set used and the inclusion of DAMPE data in the analysis. The simultaneous use of two independent data sets could also reduce the systematic uncertainty of the search., Comment: 14 pages, 10 figures. Accepted for publication in PRD

Far-field chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological, material, and chemical systems. Chemical microscopy provides a nondestructive way of chemical identification without exterior labels. However, the diffraction limit of optics hindered it from discovering more details under the resolution limit. Recent development of super-resolution techniques gives enlightenment to open this door behind far-field chemical microscopy. Here, we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial resolution. We further highlight applications in biomedical research, material characterization, environmental study, cultural heritage conservation, and integrated chip inspection., Comment: 34 pages, 8 figures,1 table

The observed spectral shapes variation and tentative bimodal burst energy distribution (E-distribution) of fast radio burst (FRB) 20121102A with the FAST telescope are great puzzles. Adopting the published multifrequency data observed with the FAST and Arecibo telescopes at $L$ band and the GBT telescope at $C$ band, we investigate these puzzles through Monte Carlo simulations. The intrinsic energy function (E-function) is modeled as $dp/dE\propto E^{-\alpha_{\rm E}}$, and the spectral profile is described as a Gaussian function. A fringe pattern of its spectral peak frequency ($\nu_{\rm p}$) in 0.5-8 GHz is inferred from the $\nu_{\rm p}$ distribution of the GBT sample. We estimate the likelihood of $\alpha_{\rm E}$ and the standard deviation of the spectral profile ($\sigma_{\rm s}$) by utilizing the Kolmogorov--Smirnov (K-S) test probability for the observed and simulated specific E-distributions. Our simulations yields $\alpha_{\rm E}=1.82^{+0.10}_{-0.30}$ and $\sigma_{\rm s}=0.18^{+0.28}_{-0.06}$ ($3\sigma$ confidence level) with the FAST sample. These results suggest that a single power-law function is adequate to model the E-function of FRB 20121102A. The variations of its observed spectral indices and E-distributions with telescopes in different frequency ranges are due to both physical and observational reasons, i.e. narrow spectral width for a single burst and discrete $\nu_{p}$ fringe pattern in a broad frequency range among bursts, and the selection effects of the telescope bandpass and sensitivity. The putative $\nu_{p}$ fringe pattern cannot be explained with the current radiation physics models of FRBs. Some caveats of possible artificial effects that may introduce such a feature are discussed., Comment: Published in ApJ,13 pages, 5 figures

The observed hard TeV gamma-ray spectrum of the nearby gamma-ray burst (GRB) 190829A may challenge the conventional leptonic GRB afterglow model. It has been proposed that an ultra-high-energy (UHE; $\varepsilon^{'}_{\rm p}\sim 10^{20}$ eV) proton population can be pre-accelerated by internal shocks in GRB jets. We study possible signatures of the UHE protons embedded in the TeV afterglows when they escape the afterglow fireball. We show that the leptonic model can represent the observed multiwavelength lightcurves and spectral energy distributions of GRB 190829A by considering the uncertainties of the model parameters. Attributing the TeV gamma-ray afterglows to the emission of both the electron self-Compton scattering process and the UHE proton synchrotron radiations in the afterglow fireball, we obtain tentative upper limits of $\log_{10} \varepsilon_{\rm p}^{\prime}/{\rm eV}\sim 20.46$ and $\log_{10}E_{\rm p, total}/{\rm erg}\leq 50.75$, where $E_{\rm p, total}$ is the total energy of the proton population. The synchrotron radiations of the UHE protons should dominate the early TeV gamma-ray afterglows, implying that early observations are critical for revealing the UHE proton population., Comment: 9 pages, 4 figures, Accepted for Publication in ApJ

Amyloid proteins are associated with a broad spectrum of neurodegenerative diseases. However, it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular environment. To address this challenge, we developed a computational chemical microscope integrating 3D mid-infrared photothermal imaging with fluorescence imaging, termed Fluorescence-guided Bond-Selective Intensity Diffraction Tomography (FBS-IDT). Based on a low-cost and simple optical design, FBS-IDT enables chemical-specific volumetric imaging and 3D site-specific mid-IR fingerprint spectroscopic analysis of tau fibrils, an important type of amyloid protein aggregates, in their intracellular environment. Label-free volumetric chemical imaging of human cells with/without seeded tau fibrils is demonstrated to show the potential correlation between lipid accumulation and tau aggregate formation. Depth-resolved mid-infrared fingerprint spectroscopy is performed to reveal the protein secondary structure of the intracellular tau fibrils. 3D visualization of the \b{eta}-sheet for tau fibril structure is achieved.

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Current OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bondselective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 {\mu}m PMMA beads embedded in agarose gel. Next, we then show 3D hyperspectral imaging of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FFOCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on a bulky and highly scattering 150 {\mu}m thick mouse brain slice.

A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Here we report a miniaturized multifunctional fiber based optoacoustic emitter (mFOE) that first integrates simultaneous non-genetic optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". The non-genetic feature addresses the challenges of the viral transfection required by optogenetics in primates and human. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. We first validated the non-genetic stimulation function of the mFOE in rat cultured neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities was confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minimal brain tissue damage has been confirmed after these applications. The capability of non-genetic neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.

Clinical identification and fundamental study of viruses rely on the detection of viral proteins or viral nucleic acids. Yet, amplification-based and antigen-based methods are not able to provide precise compositional information of individual virions due to small particle size and low-abundance chemical contents (e.g., ~ 5000 proteins in a vesicular stomatitis virus). Here, we report a widefield interferometric defocus-enhanced mid-infrared photothermal (WIDE-MIP) microscope for high-throughput fingerprinting of single viruses. With the identification of feature absorption peaks, WIDE-MIP reveals the contents of viral proteins and nucleic acids in single DNA vaccinia viruses and RNA vesicular stomatitis viruses. Different nucleic acids signatures of thymine and uracil residue vibrations are obtained to differentiate DNA and RNA viruses. WIDE-MIP imaging further reveals an enriched \b{eta} sheet components in DNA varicella-zoster virus proteins. Together, these advances open a new avenue for compositional analysis of viral vectors and elucidating protein function in an assembled virion.

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput is necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically-labelled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labelling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of $^{13}$C atoms originating from $^{13}$C-labelled-glucose can be exploited by MIP-FISH to discriminate and identify $^{13}$C-labelled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.