Spin-ss Rational QQ-system

Bethe ansatz equations for spin-$s$ Heisenberg spin chain with $s\ge1$ are significantly more difficult to analyze than the spin-$\tfrac{1}{2}$ case, due to the presence of repeated roots. As a result, it is challenging to derive extra conditions for the Bethe roots to be physical and study the related completeness problem. In this paper, we propose the rational $Q$-system for the XXX$_s$ spin chain. Solutions of the proposed $Q$-system give all and only physical solutions of the Bethe ansatz equations required by completeness. The rational $Q$-system is equivalent to the requirement that the solution and the corresponding dual solution of the $TQ$-relation are both polynomials, which we prove rigorously. Based on this analysis, we propose the extra conditions for solutions of the XXX$_s$ Bethe ansatz equations to be physical.Comment: 37 page

Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry

Motivation: We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations. Results: Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a dataset onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service

Message Passing-Based Joint User Activity Detection and Channel Estimation for Temporally-Correlated Massive Access

This paper studies the user activity detection and channel estimation problem in a temporally-correlated massive access system where a very large number of users communicate with a base station sporadically and each user once activated can transmit with a large probability over multiple consecutive frames. We formulate the problem as a dynamic compressed sensing (DCS) problem to exploit both the sparsity and the temporal correlation of user activity. By leveraging the hybrid generalized approximate message passing (HyGAMP) framework, we design a computationally efficient algorithm, HyGAMP-DCS, to solve this problem. In contrast to only exploit the historical estimations, the proposed algorithm performs bidirectional message passing between the neighboring frames for activity likelihood update to fully exploit the temporally-correlated user activities. Furthermore, we develop an expectation maximization HyGAMP-DCS (EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the estimation procedure when the system statistics are unknown. In particular, we propose to utilize the analysis tool of state evolution to find the appropriate hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate that our proposed algorithms can significantly improve the user activity detection accuracy and reduce the channel estimation error.Comment: 31 pages, 14 figures, minor revisio

Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference

<p>Abstract</p> <p>Background</p> <p>Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality.</p> <p>Results</p> <p>Microarray experiments were performed in a γ-proteobacterium <it>Shewanella oneidensis</it>. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing <it>Shewanella </it>genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses.</p> <p>Conclusion</p> <p>These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.</p

Spontaneous excitation of an accelerated multilevel atom in dipole coupling to the derivative of a scalar field

We study the spontaneous excitation of an accelerated multilevel atom in dipole coupling to the derivative of a massless quantum scalar field and separately calculate the contributions of the vacuum fluctuation and radiation reaction to the rate of change of the mean atomic energy of the atom. It is found that, in contrast to the case where a monopole like interaction between the atom and the field is assumed, there appear extra corrections proportional to the acceleration squared, in addition to corrections which can be viewed as a result of an ambient thermal bath at the Unruh temperature, as compared with the inertial case, and the acceleration induced correction terms show anisotropy with the contribution from longitudinal polarization being four times that from the transverse polarization for isotropically polarized accelerated atoms. Our results suggest that the effect of acceleration on the rate of change of the mean atomic energy is dependent not only on the quantum field to which the atom is coupled, but also on the type of the interaction even if the same quantum scalar field is considered.Comment: 11 pages, no figure

Biostatistical Considerations of the Use of Genomic DNA Reference in Microarrays

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories (2, 3, 5, 7, 9, 20, 24-26). While some reported that experimental results of microarrays using genomic DNA reference conformed nicely to those obtained by cDNA: cDNA co-hybridization method, others acquired poor results. We hypothesized that these conflicting reports could be resolved by biostatistical analyses. To test it, microarray experiments were performed in a 4 proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either by direct cDNA: cDNA co-hybridization, or by indirect calculation through a Shewanella genomic DNA reference. Several major biostatistical techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses significantly improved the data quality. These findings could potentially serve as guidelines for microarray data analysis using genomic DNA as reference

L1-ORF1p and Ago2 are involved in a siRNA-mediated regulation for promoter activity of L1-5’UTR

Introduction. Long interspersed nuclear elements-1 (L1), as the only one self-active retrotransposon of the mobile element, was found to be generally activated in tumor cells. The 5‘UTR of L1 (L1-5’UTR) contains both sense and antisense bidirectional promoters, transcription products of which can generate double-strand RNA (dsRNA). In addition, L1-ORF1p, a dsRNA binding protein encoded by L1, is considered to engage in some RNA-protein (RNP) formation. Ago2, one of the RISC components, can bind to dsRNA to form RISC complex, but its role in L1 regulation still remains unclear. Due that the 5‘UTR of L1 (L1-5’UTR) contains both sense and antisense bidirectional promoters, so the activities in both string were identified. A dsRNA-mediated regulation of L1-5’UTR, with the feedback regulation of L1-ORF1p as well as other key molecules engaged (Ago1–4) in this process, was also investigated. Material and methods. Genomic DNA was extracted from HEK293 cells and subjected to L1-5’UTR prepa­ration by PCR. Report gene system pIRESneo with SV40 promoter was employed. The promoter activities of different regions in L1-5’UTR were identified by constructing these regions into pIRESneo, which SV40 region was removed prior, to generate different recombinant plasmids. The promoter activities in recombinant plasmids were detected by the luciferase expression assay. Western blot and co-immunoprecipitation were employed to identify proteins expression and protein-protein interaction respectively. Results. Ago2 is a member of Agos family, which usually forms a RISC complex with si/miRNA and is involved in post- transcriptional regulation of many genes. Here L1-ORF1p and Ago2 conducts a regulation as a negative feedback for L1-5'UTR sense promoter. L1-ORF1p could form the immune complexes with Ago1, Ago2 and Ago4, respectively. Conclusions. L1-5’UTR harbors both sense and antisense promoter activity and a dsRNA-mediated regulation is responsible for L1-5’UTR regulation. Agos proteins and L1-ORF1p were engaged in this process

Cooperative Multi-Cell Massive Access with Temporally Correlated Activity

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.Comment: 16 pages, 17 figures, minor revisio

GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.Comment: 10 pages, 7 figures and 3 table