Metal-macrocyclic framework featuring adaptive cavity for precise palladium recognition
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Chemistry↗
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FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition
In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.
Distributed↗
SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination
Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.
Linne, Karl C. [U. Chicago (main)] (ORCID:00090009↗
Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions
In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.
LiDAR, machine learning, neural network, object re↗
Manipulating symmetry-breaking charge separation employing molecular recognition
The exploration of symmetry-breaking charge separation (SB-CS) is imperative when designing functional light-harvesting materials. Past explorations, however, have been confined to covalent systems, more often than not requiring complicated/demanding syntheses and facing inconvenient regulation of charge transfer processes. Here, in this work, we present a concept that regulates the efficiency of SB-CS through molecular recognition utilizing a pyridinium-based cyclophane as a host. This host undergoes photo-driven excited-state SB-CS. By employing different guests with distinct frontier molecular orbital energy levels, we have achieved comprehensive control of electron transfer pathways in the cyclophane, modulating between accelerated (>10-fold) intramolecular SB-CS involving superexchange and direct intermolecular electron transfer between the host and guest. The improvement in SB-CS efficiency results in catalytic activity for the photo-oxidation of a sulfur-mustard simulant. This research offers an opportunity for tuning SB-CS by utilizing molecular recognition, which holds the potential for achieving precise regulation without complicated organic syntheses.
charge transfer↗
Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation
Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector
Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.
Kitagawa, H. [Pisa U.]↗
Commissioning of the Mu2e tracker DAQ, planning for the Vertical Slice Test and pre-pattern recognition studies
The primary objective of the Mu2e experiment at Fermilab is to search for the neutrino-less coherent $\mu \rightarrow e$ conversion in the field of an aluminum nucleus ($\mu^- \text{Al} \rightarrow e^- \text{Al}$). The signature of this process is a monochromatic Conversion Electron (CE) with an energy of approximately 104.97 MeV \cite{bartoszek2015mu2e}. Within the Standard Model (SM), the branching ratio for this process, including neutrino masses and oscillation, is expected to be less than $\mathcal{O}(10^{-50})$. This value is far beyond current experimental capabilities. However, models of physics beyond the SM predict much higher relative rates, approaching an observable level. The SINDRUM II experiment set an upper limit on muon conversion at $7 \times 10^{-13}$ (90\% CL) on Au target \cite{SINDRUMII:2006dvw}, and the Mu2e collaboration aims to improve this limit by four orders of magnitude. Observing this process would provide a clear evidence of physics beyond the Standard Model. A brief discussion of the theoretical and experimental aspects is provided in Chapter \ref{intr}. Mu2e adopts a sophisticated experimental setup to achieve its goals, further described in Chapter \ref{mu2echapter}. The central part of the Mu2e detector is the tracker, that consists of 18 tracking stations. The tracker must provide excellent momentum resolution, approximately 1 MeV/c, to distinguish the monochromatic CE signal from the background. To minimize the energy losses, a straw tube tracker will be used \cite{bobbb}. Chapter \ref{chaptertrk} provides an overview of the straw tracker design and its working principles. This Thesis presents a comprehensive study of the Mu2e tracker, covering complementary aspects from initial commissioning to optimization and first steps of the calibration processes. My work at Fermilab has been focused on the complete Data Acquisition (DAQ) testing from both hardware and software perspectives. I was involved in the commissioning of the Mu2e DAQ system and the Vertical Slice Test (VST) of the tracker. The VST encompasses the entire testing chain, from the straws to the readout, and to processed data on disk. I was also focused on the offline analysis, especially on pre-pattern recognition studies, to explore the best methods for identifying $\delta$-electrons during the data taking. Chapter \ref{commissioning} details the commissioning of the tracker DAQ system, emphasizing the importance of understanding of the readout process before the data acquisition. This includes validating the readout logic and firmware through Monte Carlo simulations to confirm functionality and buffering, monitoring the quality of the data from the tracker preamplifiers and front-end electronics, and assessing overall DAQ performance to ensure reliability during future calibration and data-taking. Chapter \ref{planning} discusses the initial steps towards the tracker calibration. The ultimate goal is to perform a time calibration of the first assembled station of the tracker using cosmic muons, aiming for a longitudinal hit position resolution better than 4 cm. This involves determining the signal propagation times and channel-to-channel delays. I performed a Monte Carlo study to determine the impact of the station orientation on the quality of the calibration, in particular on the cosmic track reconstruction, focusing on potential biases that could arise. These studies provide essential insights into the operation, optimization, and calibration of the Mu2e tracker system. Given the high data volume expected during Mu2e operations, estimated at approximately 7 PBytes per year, optimizing memory usage and minimizing CPU consumption are critical. A significant challenge lies in effectively flagging $\delta$-electron hits, which are the primary source of hits in the tracker, without compromising the efficiency of CE hit detection and track reconstruction. A detailed study of pre-pattern recognition and a thorough comparison of two $\delta$-electron flagging algorithms is provided in Chapter \ref{delta}. In Chapter \ref{conclusions}, the findings are concisely summarized, offering a comprehensive synthesis of the research and emphasizing the key insights derived from this study.
43 PARTICLE ACCELERATORS↗
The BRIAR Dataset: A Comprehensive Whole-Body Biometric Recognition Benchmark at Extreme Distances and Altitudes (Collections 1-6)
The Biometric Recognition and Identification at Altitude and Range (BRIAR) program aims to extend biometric capabilities into severe operational environments characterized by long ranges, atmospheric turbulence, and elevated viewpoints. This paper introduces the program’s two final government collections expanding activities, environments, viewpoints, distances, and modalities, and adding appearance and environmental stressors. We refine curation/evaluation and include sequestered subsets to support controlled assessments. These updates strengthen BRIAR as a comprehensive resource for whole-body, face, and gait recognition at altitude and range, while maintaining privacy-first practices.
Yoon, Rocky [ORNL] (ORCID:0009000491499165)↗
Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition
We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.
99 - GENERAL AND MISCELLANEOUS↗
Open Set Recognition for Unknown Waveform Classification
This presentation applies open set recognition to classify unknown waveforms, enabling systems to not only identify known types but also reliably detect when waveforms fall outside the training distribution. This approach enhances robustness by avoiding forced misclassification of novel or anomalous signals.
99 - GENERAL AND MISCELLANEOUS↗
An evolutionarily conserved tryptophan cage promotes folding of the extended RNA recognition motif in the hnRNPR ‐like protein family
Abstract The heterogeneous nuclear ribonucleoprotein (hnRNP) R‐like family is a class of RNA binding proteins in the hnRNP superfamily with diverse functions in RNA processing. Here, we present the 1.90 Å X‐ray crystal structure and solution NMR studies of the first RNA recognition motif (RRM) of human hnRNPR. We find that this domain adopts an extended RRM (eRRM1) featuring a canonical RRM with a structured N‐terminal extension (N ext ) motif that docks against the RRM and extends the β‐sheet surface. The adjoining loop is structured and forms a tryptophan cage motif to position the N ext motif for docking to the RRM. Combining mutagenesis, solution NMR spectroscopy, and thermal denaturation studies, we evaluate the importance of residues in the N ext –RRM interface and adjoining loop on eRRM folding and conformational dynamics. We find that these sites are essential for protein solubility, conformational ordering, and thermal stability. Consistent with their importance, mutations in the N ext –RRM interface and loop are associated with several cancers in a survey of somatic mutations in cancer studies. Sequence and structure comparison of the human hnRNPR eRRM1 to experimentally verified and predicted hnRNPR‐like proteins reveals conserved features in the eRRM.
Biochemistry & Molecular Biology↗
Dielectric-Engineered Monolayer MoS 2 Memtransistors for Brain-Inspired Computing with High Recognition Accuracy
Two-dimensional transition metal dichalcogenides (2D-TMDs)-based memtransistors have emerged as promising candidates for neuromorphic hardware due to their exceptional ability to emulate synaptic behavior. However, many existing 2D-TMDs memtransistors rely on polycrystalline channels with grain boundaries or defects introduced through postgrowth treatments, raising concerns about material integrity and the preservation of intrinsic properties. Here, in this work, we demonstrate a monocrystalline monolayer MoS 2 memtransistor fabricated on a silicon nitride (SiN X ) substrate, achieving a large resistive switching ratio of 10 4 , a dynamic range exceeding 90, along with highly linear and symmetric weight updates, minimal cycle-to-cycle variability, and low device-to-device variability. These attributes are critical for enabling high-performance neuromorphic hardware. Based on experimental data, we further show that these artificial synapses enable a recognition accuracy of more than 97% on the MNIST handwritten digits data set. Our findings present a straightforward approach to realizing 2D-TMDs memtransistors through dielectric engineering, offering a promising platform for next-generation neuromorphic computing systems.
2D TMDs↗
Automatic speech recognition predicts contemporaneous earthquake fault displacement
Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.
58 GEOSCIENCES↗
From small changes to big gains: pyridinium-based tetralactam macrocycle for enhanced sugar recognition in water
The complex distribution of functional groups in carbohydrates, coupled with their strong solvation in water, makes them challenging targets for synthetic receptors. Despite extensive research into various molecular frameworks, most synthetic carbohydrate receptors have exhibited low affinities, and their interactions with sugars in aqueous environments remain poorly understood. In this work, we present a simple pyridinium-based hydrogen-bonding receptor derived from a subtle structural modification of a well-known tetralactam macrocycle. This small structural change resulted in a dramatic enhancement of glucose binding affinity, increasing from 56 M −1 to 3001 M −1 . Remarkably, the performance of our synthetic lectin surpasses that of the natural lectin, concanavalin A, by over fivefold. X-ray crystallography of the macrocycle–glucose complex reveals a distinctive hydrogen bonding pattern, which allows for a larger surface overlap between the receptor and glucose, contributing to the enhanced affinity. Furthermore, this receptor possesses allosteric binding sites, which involve chloride binding and trigger receptor aggregation. This unique allosteric process reveals the critical role of structural flexibility in this hydrogen-bonding receptor for the effective recognition of sugars. We also demonstrate the potential of this synthetic lectin as a highly sensitive glucose sensor in aqueous solutions.
Zhai, Canjia↗
An Application of Molecular Recognition for the Efficient Removal of Cesium from Hanford Nuclear Waste by Modular Solvent Extraction
In this work, experimental results leading to flowsheet design are presented showing how a calixarene-crown ether based solvent-extraction process can meet the challenge of cesium removal from nuclear tank wastes stored at the US Department of Energy Hanford site. Cleanup of legacy Cold War nuclear waste stored in underground tanks represents one of the greatest environmental challenges facing the US Department of Energy in terms of risk, cost, and effectiveness of applicable science and technology. Planning for the cleanup at the Hanford Site calls for the removal of the radioactive fission product 137Cs from its alkaline salt waste, including the use of modular processes that can be deployed near the tank farms. To meet the resulting need for extremely high selectivity, the Next-Generation Caustic-Side Solvent Extraction (NG-CSSX) process employing a calix[4]arene-crown ether in modified kerosene has been adapted to remove sub-millimolar cesium in competition with molar sodium and potassium in a high-nitrate alkaline matrix. Potassium loading in the solvent was determined in extraction, scrubbing, and stripping, leading to an empirical model closely approximating cesium distribution ratios for a variety of Hanford waste types. Process chemistry has been developed based on this molecular-recognition approach, focusing on the competitive effect of potassium loading and the mitigating process modifications needed, including extending the scrub section. The result is a modular flowsheet design that can achieve cesium decontamination factors well in excess of 15,000 even for the worst-case Hanford waste.
Williams, Neil [ORNL] (ORCID:000000023159226X)↗
Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation.
Cornett, David [ORNL] (ORCID:0000000222910860)↗
Structure-function studies of Vibrio cholerae quorum-sensing receptor CqsR signal recognition
Ethanolamine signaling through the transmembrane quorum-sensing receptor CqsR influencesVibrio choleraeniche recognition and host colonization. In this study, we present a comprehensive structure-function analysis of CqsR. Specifically, we have determined X-ray crystal structures of the CqsR periplasmic domain bound to the signaling agonist ethanolamine and its analogs, serinol and L-alaninol, as well as the ligand-free (apo) form of CqsR. The periplasmic ligand-binding domain of CqsR is a Cache domain, the most prevalent extracellular sensory module in prokaryotes. Our findings provide a rare structural comparison of ligand-bound and unbound states of a Cache domain receptor. Coupled with thermodynamic binding assays and genetic analyses, these structures elucidate the molecular basis of CqsR ligand specificity. This study not only advances the understanding of Cache domain function but also informs the identification of ligands for orphan Cache receptors and the rational design of signaling agonists and antagonists. Lastly, we discuss ligand-induced conformational changes in the CqsR Cache domains and explore the potential for the existence of additional regulatory ligands.
Microbiology↗