Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-shot self-supervised particle tracking

Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on either vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, the data produced by experiments are often challenging to label and cannot be easily reproduced numerically. Here, we propose a novel deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And Regression), that learns to tracks objects with sub-pixel accuracy from a single unlabeled experimental image. This is made possible by exploiting the inherent roto-translational symmetries of the data. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we analyze challenging experimental data containing densely packed cells or noisy backgrounds. We also exploit additional symmetries to extend the measurable particle properties to the particle's vertical position by propagating the signal in Fourier space and its polarizability by scaling the signal strength. Thanks to the ability to train deep-learning models with a single unlabeled image, LodeSTAR can accelerate the development of high-quality microscopic analysis pipelines for engineering, biology, and medicine.
Dropout can Simulate Exponential Number of Models for Sample Selection Techniques

Following Coteaching, generally in the literature, two models are used in sample selection based approaches for training with noisy labels. Meanwhile, it is also well known that Dropout when present in a network trains an ensemble of sub-networks. We show how to leverage this property of Dropout to train an exponential number of shared models, by training a single model with Dropout. We show how we can modify existing two model-based sample selection methodologies to use an exponential number of shared models. Not only is it more convenient to use a single model with Dropout, but this approach also combines the natural benefits of Dropout with that of training an exponential number of models, leading to improved results.
An explicit Watson-Ichino formula with CM newforms

In this paper, we extend the work of Humphries and Khan [HK20] to establish an explicit version of Watson--Ichino formula for $L(1/2,f\otimes\mathrm{ad} g)$, where $f$ is a Hecke--Maass form and $g$ is a CM newform.
Hydrogen Storage Performance Enhancement and Bandgap Opening of M-Decorated ($\mathrm{M}=\mathrm{Li}$, Na and K) III\textsubscript{4}-V\textsubscript{4} Monolayer by Fluorine Functionalization

The effects of fluorine functionalization on the hydrogen storage capability of alkaline decorated III4-V4 monolayers is studied. This structure can store up to two hydrogen molecules per alkaline atom. Here, we demonstrate that functionalizing alkaline decorated monolayer with a high electronegative element of fluorine can significantly enhance both binding energy and the maximum number of the stored hydrogen molecules. In this regard, the hydrogen molecule storage capability is notably improved from two to four and when absolute binding is considered. In addition, F-functionalization of M-decorated III4-V4 can demonstrate bandgap opening effect, introduce semiconducting characteristics and forming a new two-dimensional semiconductor structure. The bandgap for Li-Al4P4-F is 1.23 eV which is very close to the solar peak. The resulted bandgap in the M-III4-V4-F structure is even significantly larger than that of pristine III4-V4 monolayer. The binding of the hydrogen molecule to the alkaline atom is also improved from 0.114 to 0.272 eV by the fluorine functionalization.
Towards early dark energy and $n_s$=1 with Planck, ACT and SPT

We investigate the constraints on early dark energy (EDE) by combining the most recent CMB observations available, ACT DR4, SPT-3G, and Planck2018 ($\ell_\text{TT,max}=1000$) data. This combined CMB dataset favors non-zero EDE fractions and large Hubble constants. The inclusion of BAO+Pantheon data has little effect on the results, leads to $H_0=71.6( 72.9)_{-1.5}^{+2.0}$ and $73.17(72.74)^{+0.55}_{-0.77}$ km/s/Mpc for axion-like EDE and AdS-EDE, respectively. The axion-like EDE can fit the data significantly better ($\Delta \chi^2 \lesssim -10$) than $\Lambda$CDM, which is mainly driven by the ACT data. It is found again that if the current $H_0$ measured locally is correct, complete resolution of the Hubble tension seems to be pointing towards a scale invariant Harrison-Zeldovich spectrum of primordial scalar perturbation, i.e. $n_s=1$ for $H_0\sim 73$ km/s/Mpc.
Variable Rate Compression for Raw 3D Point Clouds

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.
Uniqueness in Inverse Diffraction Grating Problems with Infinitely Many Plane Waves at a Fixed Frequency

This paper is concerned with the inverse diffraction problems by a periodic curve with Dirichlet boundary condition in two dimensions. It is proved that the periodic curve can be uniquely determined by the near-field measurement data corresponding to infinitely many incident plane waves with distinct directions at a fixed frequency. Our proof is based on Schiffer's idea which consists of two ingredients: i) the total fields for incident plane waves with distinct directions are linearly independent, and ii) there exist only finitely many linearly independent Dirichlet eigenfunctions in a bounded domain or in a closed waveguide under additional assumptions on the waveguide boundary. Based on the Rayleigh expansion, we prove that the phased near-field data can be uniquely determined by the phaseless near-field data in a bounded domain, with the exception of a finite set of incident angles. Such a phase retrieval result leads to a new uniqueness result for the inverse grating diffraction problem with phaseless near-field data at a fixed frequency. Since the incident direction determines the quasi-periodicity of the boundary value problem, our inverse issues are different from the existing results of [Htttlich&Kirsch, Inverse Problems 13 (1997): 351-361] where fixed-direction plane waves at multiple frequencies were considered.
Research on Application of Artificial Neural Network in Predicting Mining Subsidence

A new method was put forward for the quantitative prediction of mining subsidence by means of ANN(Artificial Neural Network). Problems of selecting influential factors, establishing ANN prediction model and application of ANN model were discussed. BP algorithm was used for modeling and predicting the mining subsidence. The result shows that the ANN prediction model is theoretically feasible and significant in predicting complex exploitation sink system.
Fifty Years of Prolog and Beyond

Both logic programming in general and Prolog in particular have a long and fascinating history, intermingled with that of many disciplines they inherited from or catalyzed. A large body of research has been gathered over the last 50 years, supported by many Prolog implementations. Many implementations are still actively developed, while new ones keep appearing. Often, the features added by different systems were motivated by the interdisciplinary needs of programmers and implementors, yielding systems that, while sharing the “classic” core language, in particular, the main aspects of the ISO-Prolog standard, also depart from each other in other aspects. This obviously poses challenges for code portability. The field has also inspired many related, but quite different languages that have created their own communities. This article aims at integrating and applying the main lessons learned in the process of evolution of Prolog. It is structured into three major parts. First, we overview the evolution of Prolog systems and the community approximately up to the ISO standard, considering both the main historic developments and the motivations behind several Prolog implementations, as well as other logic programming languages influenced by Prolog. Then, we discuss the Prolog implementations that are most active after the appearance of the standard: their visions, goals, commonalities, and incompatibilities. Finally, we perform a SWOT analysis in order to better identify the potential of Prolog and propose future directions along with which Prolog might continue to add useful features, interfaces, libraries, and tools, while at the same time improving compatibility between implementations.
Interpretable machine-learning identification of the crossover from subradiance to superradiance in an atomic array

Light–matter interacting quantum systems manifest strong correlations that lead to distinct cooperative spontaneous emissions of subradiance or superradiance. To demonstrate the essence of finite-range correlations in such systems, we consider an atomic array under the resonant dipole–dipole interactions (RDDI) and apply an interpretable machine learning (ML) with the integrated gradients to identify the crossover between the subradiant and superradiant sectors. The machine shows that the next nearest-neighbor (NN) couplings in RDDI play as much as the roles of NN ones in determining the whole eigenspectrum within the training sets. Our results present the advantage of ML approach with explainable ability to reveal the underlying mechanism of correlations in quantum optical systems, which can be potentially applied to investigate many other strongly interacting quantum many-body systems.
Learning speed equalization network for few-shot learning

Abstract. The core challenge of few-shot learning is the serious overfitting problem on new tasks because of the scarcity of labeled data. Self-supervision learning can mine powerful supervision signals from the data itself to enhance the generalization performance of the model. Thus a rotation self-supervision module has been directly integrated to a few-shot learning network to alleviate the overfitting problem. However, due to the level difference or convergence speed difference in the loss function for each task, the overall model can be alternately dominated or biased by a certain task during the training stages, which has disadvantages for the main task performance. Therefore, we design a network architecture with auxiliary task learning speed equalization (LSENet) for few-shot learning. The overall model improves the generalization capability using an auxiliary task. In addition, we design a speed equalization module to constrain the decline rate of the two tasks to achieve balanced learning. Our method alleviates the overfitting problem of few-shot learning and greatly improves classification accuracy. Extensive experiments are conducted on benchmark datasets to demonstrate the effectiveness of our method.
Gynandromorphy and Other Morphological Aberrations in Hylaeus Fabricius (Hymenoptera: Colletidae: Hylaeinae) Specimens from Colorado, USA

ABSTRACT: The presence of morphological abnormalities from the described state (aberrations) can cause issues with identification of complex groups of bees. Three accounts of aberrant specimens of North American Hylaeus Fabricius from Rocky Mountain, Colorado are described. A specimen of Hylaeus rudbeckiae (Cockerell and Casad) exhibiting mosaic gynandromorphy, a specimen of Hylaeus personatellus (Cockerell) exhibiting aberrations on the face, and a specimen of Hylaeus coloradensis (Cockerell) exhibiting an aberration in the wing venation are described here with photo documentation.
SparseP

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they can yield significant performance and energy improvements in parallel applications by alleviating data access costs. Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the Sparse Matrix Vector Multiplication (SpMV) kernel. SpMV has been characterized as one of the most significant and thoroughly studied scientific computation kernels. It is primarily a memory-bound kernel with intensive memory accesses due its algorithmic nature, the compressed matrix format used, and the sparsity patterns of the input matrices given. This paper provides the first comprehensive analysis of SpMV on a real-world PIM architecture, and presents SparseP, the first SpMV library for real PIM architectures. We make three key contributions. First, we implement a wide variety of software strategies on SpMV for a multithreaded PIM core, including (1) various compressed matrix formats, (2) load balancing schemes across parallel threads and (3) synchronization approaches, and characterize the computational limits of a single multithreaded PIM core. Second, we design various load balancing schemes across multiple PIM cores, and two types of data partitioning techniques to execute SpMV on thousands of PIM cores: (1) 1D-partitioned kernels to perform the complete SpMV computation only using PIM cores, and (2) 2D-partitioned kernels to strive a balance between computation and data transfer costs to PIM-enabled memory. Third, we compare SpMV execution on a real-world PIM system with 2528 PIM cores to an Intel Xeon CPU and an NVIDIA Tesla V100 GPU to study the performance and energy efficiency of various devices, i.e., both memory-centric PIM systems and conventional processor-centric CPU/GPU systems, for the SpMV kernel. SparseP software package provides 25 SpMV kernels for real PIM systems supporting the four most widely used compressed matrix formats, i.e., CSR, COO, BCSR and BCOO, and a wide range of data types. SparseP is publicly and freely available at https://github.com/CMU-SAFARI/SparseP. Our extensive evaluation using 26 matrices with various sparsity patterns provides new insights and recommendations for software designers and hardware architects to efficiently accelerate the SpMV kernel on real PIM systems.
Fast terminal sliding mode control of nonlinear systems

The design method of terminal sliding mode control is considered and a new terminal sliding hypersurface and control of nonlinear systems is proposed.It is shown that,by this design the system state variables can be driven into each sliding hypersurface in finite time until it arrived at the equilibrium point.At the same time good dynamic stability properties could be obtained.Finally simulation results showed that this new method of terminal sliding mode control is useful.
Rif-Correct, a software tool for bacterial mRNA half-life estimation

Background Genome-wide measurement of bacterial mRNA lifetimes using the antibiotic rifampicin has provided new insights into the control of bacterial mRNA decay. However, for long polycistronic mRNAs, the estimation of mRNA half-life can be confounded with transcriptional runoff caused by rifampicin’s inhibition of initiating RNA polymerases, and not elongating RNA polymerases. Results We present the Rif-correct software package, a free open-source tool that uses transcriptome models of transcript architecture to allow for more accurate mRNA half-life estimates that account for the transcriptional runoff. Rif-correct is implemented as a customizable python script that allows for users to control all the analysis parameters to achieve improved mRNA half-life estimates. Conclusions Rif-correct is the first free open-source computational analysis pipeline for Rif-seq dataset mRNA half-life estimation. It is simple to run, fast, and easy to run with a detailed instruction manual and example datasets.
Event-related causality in stereo-EEG discriminates syntactic processing of noun phrases and verb phrases

Objective. Syntax involves complex neurobiological mechanisms, which are difficult to disentangle for multiple reasons. Using a protocol able to separate syntactic information from sound information we investigated the neural causal connections evoked by the processing of homophonous phrases, i.e. with the same acoustic information but with different syntactic content. These could be either verb phrases (VP) or noun phrases. Approach. We used event-related causality from stereo-electroencephalographic recordings in ten epileptic patients in multiple cortical and subcortical areas, including language areas and their homologous in the non-dominant hemisphere. The recordings were made while the subjects were listening to the homophonous phrases. Main results. We identified the different networks involved in the processing of these syntactic operations (faster in the dominant hemisphere) showing that VPs engage a wider cortical and subcortical network. We also present a proof-of-concept for the decoding of the syntactic category of a perceived phrase based on causality measures. Significance. Our findings help unravel the neural correlates of syntactic elaboration and show how a decoding based on multiple cortical and subcortical areas could contribute to the development of speech prostheses for speech impairment mitigation.
ABIOTIC STRESS GENE 1 mediates aroma volatiles accumulation by activating MdLOX1a in apple

Fruit aroma is an important organoleptic quality, which influences consumer preference and market competitiveness. Aroma compound synthesis pathways in plants have been widely identified of which the lipoxygenase pathway is crucial for fatty acid catabolism to form esters in apple. However, the regulatory mechanism of this pathway remains elusive. In this study, linear regression analysis and transgene verification revealed that the lipoxygenase MdLOX1a participates in ester biosynthesis. Yeast one-hybrid library screening indicated that a novel abiotic stress gene, MdASG1 (ABIOTIC STRESS GENE 1), was a positive regulator of the MdLOX1a promoter and ester production based on yeast one-hybrid and dual-luciferase assays, and correlation analysis among eight apple cultivars. Overexpression of MdASG1 in apple and tomato stimulated the lipoxygenase pathway and increased the fatty acid-derived volatile content, whereas the latter was decreased by MdASG1 silencing. Furthermore, MdASG1 overexpression enhanced the salt-stress tolerance of tomato and apple ‘Orin’ calli accompanied by a higher content of fatty acid-derived volatiles compared with that of non-stressed transgenic tomato fruit. Collectively, these findings indicate that MdASG1 activates MdLOX1a expression and participates in the lipoxygenase pathway, subsequently increasing the accumulation of aroma compounds especially under moderate salt stress treatment. The results also provide insight into the regulation of aroma production, and the potential strategy of prudent development and utilization of saline-alkali land to produce high-quality fruit, thereby reducing pressure on arable land and ensuring national food security. One-sentence Summary MdASG1 directly activates MdLOX1a expression to promote aroma volatiles accumulation especially under moderate salt stress.
Differential Recognition of Computationally Optimized H3 Hemagglutinin Influenza Vaccine Candidates by Human Antibodies

Vaccine development for circulating influenza viruses, particularly for the H3N2 subtype, remains challenging due to consistent antigenic drift. Computationally optimized broadly reactive antigen (COBRA) technology has proven effective for broadening influenza hemagglutinin (HA)-elicited antibody responses compared to wild-type immunogens. ABSTRACT Among circulating influenza viruses in humans, H3N2 viruses typically evolve faster than other subtypes and have caused disease in millions of people since emerging in 1968. Computationally optimized broadly reactive antigen (COBRA) technology is one strategy to broaden vaccine-elicited antibody responses among influenza subtypes. In this study, we determined the structural integrity of an H3N2 COBRA hemagglutinin (HA), TJ5, and we probed the antigenic profile of several H3N2 COBRA HAs by assessing recognition of these immunogens by human B cells from seasonally vaccinated human subjects. Of three recently described COBRA H3 HA antigens (TJ5, NG2, and J4), we determined that TJ5 and J4 HA proteins recognize pre-existing B cells more effectively than NG2 HA and a wild-type Hong Kong/4801/2014 protein. We also isolated a panel of 12 H3 HA-specific human monoclonal antibodies (MAbs) and identified that most MAbs recognize both wild-type and COBRA HA proteins and have functional activity against a broad panel of H3N2 viruses. Most MAbs target the receptor-binding site, and one MAb targets the HA stem. MAb TJ5-5 recognizes TJ5 and J4 COBRA HA proteins but has poor recognition of NG2 HA, similar to the global B-cell analysis. We determined a 3.4 Å structure via cryo-electron microscopy of Fab TJ5-5 complexed with the H3 COBRA TJ5, which revealed residues important to the differential binding. Overall, these studies determined that COBRA H3 HA proteins have correct antigenic and structural features, and the proteins are recognized by B cells and MAbs isolated from seasonally vaccinated humans. IMPORTANCE Vaccine development for circulating influenza viruses, particularly for the H3N2 subtype, remains challenging due to consistent antigenic drift. Computationally optimized broadly reactive antigen (COBRA) technology has proven effective for broadening influenza hemagglutinin (HA)-elicited antibody responses compared to wild-type immunogens. Here, we determined the structural features and antigenic profiles of H3 COBRA HA proteins. Two H3 COBRA HA proteins, TJ5 and J4, are better recognized by pre-existing B cells and monoclonal antibodies from the 2017 to 2018 vaccine season compared to COBRA NG2 and a wild-type A/Hong Kong/2014 HA protein. We determined a cryo-electron microscopy (cryo-EM) structure of one MAb that poorly recognizes NG2, MAb TJ5-5, in complex with the TJ5 COBRA HA protein and identified residues critical to MAb recognition. As NG2 is more effective than TJ5 for the recent Hong Kong/2019 virus, these data provide insights into the diminished effectiveness of influenza vaccines across vaccine seasons.