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Liu, Han

Publications and source records attributed to Liu, Han.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DNABERT-S: pioneering species differentiation with species-aware DNA embeddings

SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 28 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. AVAILABILITY AND IMPLEMENTATION: Model, codes, and data are publically available at https://github.com/MAGICS-LAB/DNABERT_S.

Zhou, Zhihan↗
DOE OSTI2024

Effect of Pressure on Crystal Structure and Phonon Density of States of FeSi

The strongly correlated material FeSi displays several unusual thermal, magnetic, and structural properties under varying pressure-temperature (P-T) conditions. It is a potential thermoelectric alloy and a material with several geochemical implications as a possible constituent at the Earth's core-mantle boundary (CMB). Previous theoretical studies predicted a pressure-induced B20-B2 transition at ambient temperature below 40 GPa; however, experimentally, the structural transition is observed only at high P-T conditions. In this study, we have performed high-pressure powder X-ray diffraction (XRD) up to 90 GPa and Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements up to 120 GPa to understand the phase stability and lattice dynamics. Our study provides evidence for a non-hydrostatic stress-induced B20-B2 transition in FeSi at around 36 GPa. We deduced the Fe partial phonon density of states (PDOS) and thermal parameters from NRIXS measurements up to 120 GPa and compared them with density functional theory (DFT) calculations. Furthermore, the computations show pressure-induced metallization and the band gap closing around 12 GPa.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National Accelerator Laboratory (Fermilab). Our primary objective is to regulate the spill process to ensure a consistent intensity profile, with the ultimate goal of creating an automated controller capable of providing real-time feedback and calibration of the Spill Regulation System (SRS) parameters on a millisecond timescale. We treat the Mu2e accelerator system as a Markov Decision Process suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and enhance training stability. A key innovation in our approach is the integration of a neuralized Proportional-Integral-Derivative (PID) controller into the policy function, resulting in a significant improvement in the Spill Duty Factor (SDF) by 13.6%, surpassing the performance of the current PID controller baseline by an additional 1.6%. This paper presents the preliminary offline results based on a differentiable simulator of the Mu2e accelerator. It paves the groundwork for real-time implementations and applications, representing a crucial step towards automated proton beam intensity control for the Mu2e experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Disentangling Beam Losses in The Fermilab Main Injector Enclosure Using Real-Time Edge AI

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler Ring. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Deciphering the origin of any of the 260 BLM readings is often difficult. The (Accelerator) Real-time Edge AI for Distributed Systems project, or READS, has developed an AI/ML model, and implemented it on fast FPGA hardware, that disentangles mixed beam losses and attributes probabilities to each BLM as to which machine(s) the loss originated from in real-time. The model inferences are then streamed to the Fermilab accelerator controls network (ACNET) where they are available for operators and experts alike to aid in tuning the machines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

This paper addresses the challenges in accurate and realtime traffic congestion prediction with uncertainty by proposing Ising-Traffic, a novel quantum-inspired dual-model Ising based traffic prediction framework which delivers higher accuracy and lower latency than SOTA solutions. While traditional and deep learning methods face the trade-off between algorithm complexity and computational efficiency, our Ising-based method leverages Ising’s inherent and unique capability of finding the state of a system with the lowest energy and applying it to traffic prediction. In this work, traffic prediction under uncertainty is formulated into two separate Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machine and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98× speedups and 5% accuracy improvement over SOTA.

traffic flow control, Ising↗
DOE OSTI2023

Effects of Cinnamomum camphora coppice planting on soil fertility, microbial community structure and enzyme activity in subtropical China

Cinnamomum camphora ( C. camphora ) is a broad-leaved evergreen tree cultivated in subtropical China. Currently, the use of C. camphora clonal cuttings for coppice management has become popular. However, the effects of C. camphora coppice planting on soil abiotic and biotic variances remained unclear. In this study, we collected soil from three points in the seven-year C. camphora coppice planting land: under the tree canopy (P15), between trees (P50), and abandoned land (Control) to investigate the effects of C. camphora coppice planting on soil fertility, microbial community structure and enzyme activity. The results revealed that C. camphora coppice planting significantly increased soil fertility in the point under the tree canopy (P15) and point between trees (P50), and P15 had more significant effects than P50. Meanwhile, in P15 and P50, soil bacterial, fungal alpha-diversity were improved and microbial community structures were also changed. And the changes of soil organic carbon and total nitrogen promote the transformation of soil bacterial, fungal community structures, respectively. In addition, C. camphora coppice planting significantly ( p < 0.05) increased soil urease (UE), polyphenol oxidase, and peroxidase activities, while significantly decreased soil ACP activity. This study demonstrated that the C. camphora coppice planting could improve soil fertility in subtropical China, which promoted the transformation of soil microbial community from oligotrophs ( K -strategist) to copiotrophs ( r -strategist). Thus, this work can provide a theoretical basis for soil nutrient variation and productive management of C. camphora coppice plantation in subtropical China.

Sun, Luyuan↗
DOE OSTI2022

Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler

Fermilab’s Main Injector enclosure houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure’s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. In this work, we introduce a novel neural approach for automatically disentangling each machine’s contributions to those measured losses. Using a continuous adaptation of the popular UNet architecture in conjunction with a novel data augmentation scheme, our model accurately infers the machine of origin on a per-BLM basis in periods of joint and independent operation. Crucially, by extracting beam loss information at varying receptive fields, the method is capable of learning both local and global machine signatures and producing high quality inferences using only raw BLM loss measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Optimizing Mu2e Spill Regulation System Algorithms

A slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During the extraction, the beam on target experiences small intensity variations owing to many factors. Various adaptive learning algorithms will be employed for beam regulation to achieve the required spill quality. We discuss here preliminary results of the slow and fast regulation algorithms validation through the computer simulations before their implementation in the FPGA. Particle tracking with sextupole resonance was used to determine the fine shape of the spill profile. Fast semi-analytical simulation schemes and Machine Learning models were used to optimize the fast regulation loop.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of traditional ML, we introduce a “topology-informed ML” paradigm—wherein some features of the network topology (rather than traditional descriptors) are used as fingerprint for ML models—and apply this method to predict the forward (stage I) dissolution rate of a series of silicate glasses. We demonstrate that relying on a topological description of the atomic network (i) increases the accuracy of the predictions, (ii) enhances the simplicity and interpretability of the predictive models, (iii) reduces the need for large training sets, and (iv) improves the ability of the models to extrapolate predictions far from their training sets. As such, topology-informed ML can overcome the limitations facing traditional ML (e.g., accuracy vs. simplicity tradeoff) and offers a promising route to predict the properties of materials in a robust fashion.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Defect Engineering of Chalcogen-Tailored Oxygen Electrocatalysts for Rechargeable Quasi-Solid-State Zinc-Air Batteries

A critical bottleneck limiting the performance of rechargeable zinc-air batteries lies in the inefficient bifunctional electrocatalysts for the oxygen reduction and evolution reactions at the air electrodes. Hybridizing transition-metal oxides with functional graphene materials has shown great advantages due to their catalytic synergism. However, both the mediocre catalytic activity of metal oxides and the restricted 2D mass/charge transfer of graphene render these hybrid catalysts inefficient. Here, an effective strategy combining anion substitution, defect engineering, and the dopant effect to address the above two critical issues is shown. This strategy is demonstrated on a hybrid catalyst consisting of sulfur-deficient cobalt oxysulfide single crystals and nitrogen-doped graphene nanomeshes (CoO 0.87 S 0.13 /GN). The defect chemistries of both oxygen-vacancy-rich, nonstoichiometric cobalt oxysulfides and edge-nitrogen-rich graphene nanomeshes lead to a remarkable improvement in electrocatalytic performance, where CoO 0.87 S 0.13 /GN exhibits strongly comparable catalytic activity to and much better stability than the best-known benchmark noble-metal catalysts. In application to quasi-solid-state zinc-air batteries, CoO 0.87 S 0.13 /GN as a freestanding catalyst assembly benefits from both structural integrity and enhanced charge transfer to achieve efficient and very stable cycling operation over 300 cycles with a low discharge-charge voltage gap of 0.77 V at 20 mA cm(-2) under ambient conditions.

bifunctional electrocatalysts↗
DOE OSTI2017

Analysis of the functional gene structure and metabolic potential of microbial community in high arsenic groundwater

Microbial functional potential in high arsenic (As) groundwater ecosystems remains largely unknown. In this study, the microbial community functional composition of nineteen groundwater samples was investigated using a functional gene array (GeoChip 5.0). Samples were divided into low and high As groups based on the clustering analysis of geochemical parameters and microbial functional structures. The results showed that As related genes (arsC, arrA), sulfate related genes (dsrA and dsrB), nitrogen cycling related genes (ureC, amoA, and hzo) and methanogen genes (mcrA, hdrB) in groundwater samples were correlated with As, SO 4 2- , NH 4 + or CH 4 concentrations, respectively. Canonical correspondence analysis (CCA) results indicated that some geochemical parameters including As, total organic content, SO 4 2- , NH 4 + , oxidation-reduction potential (ORP) and pH were important factors shaping the functional microbial community structures. Alkaline and reducing conditions with relatively low SO 4 2- , ORP, and high NH 4 + , as well as SO 4 2- and Fe reduction and ammonification involved in microbially-mediated geochemical processes could be associated with As enrichment in groundwater. This study provides an overall picture of functional microbial communities in high As groundwater aquifers, and also provides insights into the critical role of microorganisms in As biogeochemical cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

An Approach to Improve the Retention of the Fission Product Ag in TRISO Fuel Particles - 18596

The silicon carbide (SiC) coating included in TRISO particles acts as the main barrier against the radioactive fission products release into the coolant circuit. However, radioactive silver (Ag{sup 110m}) was identified outside the intact particles with a very high release fraction, which could be problematic in the licensing process for modern HTR fuel. Our previous work proposed that dissolution/recrystallization of SiC by Ag could help Ag migrate at 1800 deg. C. The aim of this work is to study whether dissolution of SiC by Ag could take place and contribute to the Ag migration at operation temperature (from 1000 to 1300 deg. C). Both, experiment and thermodynamic analysis, showed that SiC dissolution by Ag was possible above 890 deg. C. But the dissolution from 1000 to 1300 deg. C is far less effective in moving Ag due to the insufficient driving force for activating self-penetration into the grain boundaries, compared with the previous work conducted at 1800 deg. C. Compared with silver alone, the silver-palladium mixture was more permeable in SiC and showed a diffusive behavior. The diffusion front was Ag rich while most palladium remained in reaction zone rather than in the diffusion front. Furthermore, thermodynamic analysis suggested that non-stoichiometric SiC with additional free silicon could impede such a reaction. Hence it is conceivable that a sacrificial layer of SiC+Si on the inner side of the main SiC coating could improve fission product retention overall without a significant increase in cost. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

In vivo epidermal migration requires focal adhesion targeting of ACF7

Turnover of focal adhesions allows cell retraction, which is essential for cell migration. The mammalian spectraplakin protein, ACF7 (Actin-Crosslinking Factor 7), promotes focal adhesion dynamics by targeting of microtubule plus ends towards focal adhesions. However, it remains unclear how the activity of ACF7 is regulated spatiotemporally to achieve focal adhesion-specific guidance of microtubule. To explore the potential mechanisms, we resolve the crystal structure of ACF7's NT (amino-terminal) domain, which mediates F-actin interactions. Structural analysis leads to identification of a key tyrosine residue at the calponin homology (CH) domain of ACF7, whose phosphorylation by Src/FAK (focal adhesion kinase) complex is essential for F-actin binding of ACF7. Using skin epidermis as a model system, we further demonstrate that the phosphorylation of ACF7 plays an indispensable role in focal adhesion dynamics and epidermal migration in vitro and in vivo . Altogether, our findings provide critical insights into the molecular mechanisms underlying coordinated cytoskeletal dynamics during cell movement.

59 BASIC BIOLOGICAL SCIENCES↗