SkelFMM: A Simplified Fast Multipole Method Based on Recursive Skeletonization

This work introduces the kernel-independent multi-level algorithm "skelFMM" for evaluating all pairwise interactions between $N$ points connected through a kernel such as the fundamental solution of the Laplace or the Helmholtz equations. The method is based on linear algebraic tools such as randomized low rank approximation and "skeleton representations" of far-field interactions. The work is related to previously proposed linear algebraic reformulations of the fast multipole method (FMM), but is distinguished by relying on simpler data structures. In particular, skelFMM does not require an "interaction list", as it relies instead on algebraically-modified kernel interactions between near-neighbors at every level. Like other kernel independent algorithms, it only requires evaluation of the kernel function, allowing the methodology to easily be extended to a range of different kernels in 2D and 3D. The simplicity of the algorithm makes it particularly amenable to parallel implementation on heterogeneous hardware architectures. The performance of the algorithm is demonstrated through numerical experiments conducted on uniform and non-uniform point distributions in 2D and 3D, involving Laplace and (low frequency) Helmholtz kernels. The algorithm relies on a precomputation stage that constructs a tailored representation for a given geometry of points. Once the precomputation has completed, the matrix-vector multiplication attains high speed through GPU acceleration that leverages batched linear algebra

Combined Transcriptomic and Proteomic Profiling of E. coli under Microaerobic versus Aerobic Conditions: The Multifaceted Roles of Noncoding Small RNAs and Oxygen-Dependent Sensing in Global Gene Expression Control

Adaptive mechanisms that facilitate intestinal colonization by the human microbiota, including Escherichia coli, may be better understood by analyzing the physiology and gene expression of bacteria in low-oxygen environments. We used high-throughput transcriptomics and proteomics to compare the expression profiles of E. coli grown under aerobic versus microaerobic conditions. Clustering of high-abundance transcripts under microaerobiosis highlighted genes controlling acid-stress adaptation (gadAXW, gadAB, hdeAB-yhiD and hdeD operons), cell adhesion/biofilm formation (pgaABCD and csgDEFG operons), electron transport (cydAB), oligopeptide transport (oppABCDF), and anaerobic respiration/fermentation (hyaABCDEF and hycABCDEFGHI operons). In contrast, downregulated genes were involved in iron transport (fhuABCD, feoABC and fepA-entD operons), iron-sulfur cluster assembly (iscRSUA and sufABCDSE operons), aerobic respiration (sdhDAB and sucABCDSE operons), and de novo nucleotide synthesis (nrdHIEF). Additionally, quantitative proteomics showed that the products (proteins) of these high- or low-abundance transcripts were expressed consistently. Our findings highlight interrelationships among energy production, carbon metabolism, and iron homeostasis. Moreover, we have identified and validated a subset of differentially expressed noncoding small RNAs (i.e., CsrC, RyhB, RprA and GcvB), and we discuss their regulatory functions during microaerobic growth. Collectively, we reveal key changes in gene expression at the transcriptional and post-transcriptional levels that sustain E. coli growth when oxygen levels are low.Ministry of Science and Technology, Taiwan: 104-2311-B-001-011-MY3, and 107-2311-B-001-029-MY3; Academia Sinica: AS 2323, and AS-IA-110-L0

Urinary DNA Lesions as a Biomarker for Assessing Male Reproductive Health

The study aimed to examine whether urinary 8-hydroxy-2’-deoxyguanosine (8-OHdG) could serve as a biomarker for assessing sperm DNA integrity. Urine and semen samples were collected from 124 coke-oven workers, who had chronically been exposed to polycyclic aromatic hydrocarbons (PAHs), from a steel plant in Kaohsiung, Taiwan. The coke-oven workers were divided into two subgroups: topside-oven workers and side-oven workers. Sperm DNA integrity was assessed in terms of DNA fragmentation and 8-oxo-7,8-dihydro-2’-deoxyguanosine (8-oxodGuo). Urine samples were used to detect 1-hydroxypyrene (1-OHP) and urinary 8-OHdG, which served to assess exposure to PAHs and the whole body oxidative stress, respectively. Urinary 1-OHP concentrations were significantly higher in the topside-oven workers than the side-oven workers (p = 0.02). Sperm 8-oxodGuo concentrations were correlated with percentages of sperm fragmentation (p = 0.044), but urinary 8-OHdG levels failed to correlate with sperm 8-oxodGuo levels and with percentages of fragmentation. In conclusion, exposure to PAHs was linked to increased oxidative stress on sperm. However, urinary 8-OHdG may not be a suitable biomarker for examining sperm DNA damage associated with oxidative stress

Auditory cueing strategy for stride length and cadence modification: a feasibility study with healthy adults

People with Parkinson's Disease experience gait impairments that significantly impact their quality of life. Visual, auditory, and tactile cues can alleviate gait impairments, but they can become less effective due to the progressive nature of the disease and changes in people's motor capability. In this study, we develop a human-in-the-loop (HIL) framework that monitors two key gait parameters, stride length and cadence, and continuously learns a person-specific model of how the parameters change in response to the feedback. The model is then used in an optimization algorithm to improve the gait parameters. This feasibility study examines whether auditory cues can be used to influence stride length in people without gait impairments. The results demonstrate the benefits of the HIL framework in maintaining people's stride length in the presence of a secondary task.Comment: 6 pages, 4 figures; the paper is accepted and presented at EMBC 202

Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants

In this era of large-scale stellar spectroscopic surveys, measurements of stellar attributes ("labels," i.e. parameters and abundances) must be made precise and consistent across surveys. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling. With The Cannon, we transfer information from the APOGEE survey to determine precise Teff, log g, [Fe/H], and [$\alpha$/M] from the spectra of 450,000 LAMOST giants. The Cannon fits a predictive model for LAMOST spectra using 9952 stars observed in common between the two surveys, taking five labels from APOGEE DR12 as ground truth: Teff, log g, [Fe/H], [\alpha/M], and K-band extinction $A_k$. The model is then used to infer Teff, log g, [Fe/H], and [$\alpha$/M] for 454,180 giants, 20% of the LAMOST DR2 stellar sample. These are the first [$\alpha$/M] values for the full set of LAMOST giants, and the largest catalog of [$\alpha$/M] for giant stars to date. Furthermore, these labels are by construction on the APOGEE label scale; for spectra with S/N > 50, cross-validation of the model yields typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in [$\alpha$/M], values comparable to the broadly stated, conservative APOGEE DR12 uncertainties. Thus, by using "label transfer" to tie low-resolution (LAMOST R $\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R $\sim$ 22,500) survey, we substantially reduce the inconsistencies between labels measured by the individual survey pipelines. This demonstrates that label transfer with The Cannon can successfully bring different surveys onto the same physical scale.Comment: 27 pages, 14 figures. Accepted by ApJ on 16 Dec 2016, implementing suggestions from the referee reports. Associated code available at https://github.com/annayqho/TheCanno

Communicating Robot's Intentions while Assisting Users via Augmented Reality

This paper explores the challenges faced by assistive robots in effectively cooperating with humans, requiring them to anticipate human behavior, predict their actions' impact, and generate understandable robot actions. The study focuses on a use-case involving a user with limited mobility needing assistance with pouring a beverage, where tasks like unscrewing a cap or reaching for objects demand coordinated support from the robot. Yet, anticipating the robot's intentions can be challenging for the user, which can hinder effective collaboration. To address this issue, we propose an innovative solution that utilizes Augmented Reality (AR) to communicate the robot's intentions and expected movements to the user, fostering a seamless and intuitive interaction

Criticality and entanglement in non-unitary quantum circuits and tensor networks of non-interacting fermions

Models for non-unitary quantum dynamics, such as quantum circuits that include projective measurements, have been shown to exhibit rich quantum critical behavior. There are many complementary perspectives on this behavior. For example, there is a known correspondence between d-dimensional local non-unitary quantum circuits and tensor networks on a D=(d+1)-dimensional lattice. Here, we show that in the case of systems of non-interacting fermions, there is furthermore a full correspondence between non-unitary circuits in d spatial dimensions and unitary non-interacting fermion problems with static Hermitian Hamiltonians in D=(d+1) spatial dimensions. This provides a powerful new perspective for understanding entanglement phases and critical behavior exhibited by non-interacting circuits. Classifying the symmetries of the corresponding non-interacting Hamiltonian, we show that a large class of random circuits, including the most generic circuits with randomness in space and time, are in correspondence with Hamiltonians with static spatial disorder in the ten Altland-Zirnbauer symmetry classes. We find the criticality that is known to occur in all of these classes to be the origin of the critical entanglement properties of the corresponding random non-unitary circuit. To exemplify this, we numerically study the quantum states at the boundary of Haar-random Gaussian fermionic tensor networks of dimension D=2 and D=3. We show that the most general such tensor network ensemble corresponds to a unitary problem of non-interacting fermions with static disorder in Altland-Zirnbauer symmetry class DIII, which for both D=2 and D=3 is known to exhibit a stable critical metallic phase. Tensor networks and corresponding random non-unitary circuits in the other nine Altland-Zirnbauer symmetry classes can be obtained from the DIII case by implementing Clifford algebra extensions for classifying spaces.Comment: (25+14) pages, 19 figure