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    Fabrication of a multifaceted mapping mirror using two-photon polymerization for a snapshot image mapping spectrometer
    (Optica Publishing Group, 2023) Lu, Jiawei; Ng, Xue Wen; Piston, David; Tkaczyk, Tomasz S.
    A design and fabrication technique for making high-precision and large-format multifaceted mapping mirrors is presented. The method is based on two-photon polymerization, which allows more flexibility in the mapping mirror design. The mirror fabricated in this paper consists of 36 2D tilted square pixels, instead of the continuous facet design used in diamond cutting. The paper presents a detailed discussion of the fabrication parameters and optimization process, with particular emphasis on the optimization of stitching defects by compensating for the overall tilt angle and reducing the printing field of view. The fabricated mirrors were coated with a thin layer of aluminum (93 nm) using sputter coating to enhance the reflection rate over the target wave range. The mapping mirror was characterized using a white light interferometer and a scanning electron microscope, which demonstrates its optical quality surface (with a surface roughness of 12 nm) and high-precision tilt angles (with an average of 2.03% deviation). Finally, the incorporation of one of the 3D printed mapping mirrors into an image mapping spectrometer prototype allowed for the acquisition of high-quality images of the USAF resolution target and bovine pulmonary artery endothelial cells stained with three fluorescent dyes, demonstrating the potential of this technology for practical applications.
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    Kramers nodal lines and Weyl fermions in SmAlSi
    (Springer Nature, 2023) Zhang, Yichen; Gao, Yuxiang; Gao, Xue-Jian; Lei, Shiming; Ni, Zhuoliang; Oh, Ji Seop; Huang, Jianwei; Yue, Ziqin; Zonno, Marta; Gorovikov, Sergey; Hashimoto, Makoto; Lu, Donghui; Denlinger, Jonathan D.; Birgeneau, Robert J.; Kono, Junichiro; Wu, Liang; Law, Kam Tuen; Morosan, Emilia; Yi, Ming
    Kramers nodal lines (KNLs) have recently been proposed theoretically as a special type of Weyl line degeneracy connecting time-reversal invariant momenta. KNLs are robust to spin orbit coupling and are inherent to all non-centrosymmetric achiral crystal structures, leading to unusual spin, magneto-electric, and optical properties. However, their existence in in real quantum materials has not been experimentally established. Here we gather the experimental evidence pointing at the presence of KNLs in SmAlSi, a non-centrosymmetric metal that develops incommensurate spin density wave order at low temperature. Using angle-resolved photoemission spectroscopy, density functional theory calculations, and magneto-transport methods, we provide evidence suggesting the presence of KNLs, together with observing Weyl fermions under the broken inversion symmetry in the paramagnetic phase of SmAlSi. We discuss the nesting possibilities regarding the emergent magnetic orders in SmAlSi. Our results provide a solid basis of experimental observations for exploring correlated topology in SmAlSi
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    Functional screening of lysosomal storage disorder genes identifies modifiers of alpha-synuclein neurotoxicity
    (Public Library of Science, 2023) Yu, Meigen; Ye, Hui; De-Paula, Ruth B.; Mangleburg, Carl Grant; Wu, Timothy; Lee, Tom V.; Li, Yarong; Duong, Duc; Phillips, Bridget; Cruchaga, Carlos; Allen, Genevera I.; Seyfried, Nicholas T.; Al-Ramahi, Ismael; Botas, Juan; Shulman, Joshua M.
    Heterozygous variants in the glucocerebrosidase (GBA) gene are common and potent risk factors for Parkinson’s disease (PD). GBA also causes the autosomal recessive lysosomal storage disorder (LSD), Gaucher disease, and emerging evidence from human genetics implicates many other LSD genes in PD susceptibility. We have systemically tested 86 conserved fly homologs of 37 human LSD genes for requirements in the aging adult Drosophila brain and for potential genetic interactions with neurodegeneration caused by α-synuclein (αSyn), which forms Lewy body pathology in PD. Our screen identifies 15 genetic enhancers of αSyn-induced progressive locomotor dysfunction, including knockdown of fly homologs of GBA and other LSD genes with independent support as PD susceptibility factors from human genetics (SCARB2, SMPD1, CTSD, GNPTAB, SLC17A5). For several genes, results from multiple alleles suggest dose-sensitivity and context-dependent pleiotropy in the presence or absence of αSyn. Homologs of two genes causing cholesterol storage disorders, Npc1a / NPC1 and Lip4 / LIPA, were independently confirmed as loss-of-function enhancers of αSyn-induced retinal degeneration. The enzymes encoded by several modifier genes are upregulated in αSyn transgenic flies, based on unbiased proteomics, revealing a possible, albeit ineffective, compensatory response. Overall, our results reinforce the important role of lysosomal genes in brain health and PD pathogenesis, and implicate several metabolic pathways, including cholesterol homeostasis, in αSyn-mediated neurotoxicity.
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    SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data
    (Frontiers Media S.A., 2023) Osher, Nathaniel; Kang, Jian; Krishnan, Santhoshi; Rao, Arvind; Baladandayuthapani, Veerabhadran
    Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions.Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features.Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes.Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.
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    Impact of a natural disturbance on the performance and microbial communities in a full-scale constructed wetland for industrial wastewater treatment
    (Frontiers Media S.A., 2023) Hollstein, Marielle; Comerford, Mattheau; Uhl, Michael; Abel, Michael; Egan, Scott P.; Stadler, Lauren B.
    Constructed Wetlands (CWs) are a cost-effective, versatile and sustainable choice for wastewater treatment. In these environments, microbial communities play a significant role in pollutant removal. However, little is known about how microbial communities in full-scale CWs contribute to maintaining water quality or how their dynamics change in response to pulse disturbances such as fire or freezes. Furthermore, few studies have examined the relationship between CW microbial community structure and performance in full-scale industrial operations. We characterized the water-column and leaf-litter layer microbial communities in a 110-acre free water surface CW that provides tertiary wastewater treatment to a plastics manufacturing plant. The CW’s sampling campaign was conducted over a 12-month period that included Winter Storm Uri, a 100-year freeze event. Analysis of 16S rRNA gene amplicon sequences revealed that the bacterial communities experienced a temporal shift. There was also a shift in microbial community structure between the influent and the first segment of the CW. However, no differences in microbial community structure were observed in the second segment of the CW. There was a negative association between microbial community diversity and chlorophyll a, as well as microbial community diversity and total suspended solids (TSS); demonstrating an increase in microbial biodiversity as water quality improved throughout the CW. Six months after the freeze, CW performance in terms of removal of water quality constituents began to return to former removal trends. Yet, there was still a significant difference in microbial community structure within the CW relative to the previous year. This suggests CW functional resilience despite a shift in microbial community structure in the wetland.
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    Transition from Diffusive to Superdiffusive Transport in Carbon Nanotube Networks via Nematic Order Control
    (American Chemical Society, 2023) Wais, Michael; Bagsican, Filchito Renee G.; Komatsu, Natsumi; Gao, Weilu; Serita, Kazunori; Murakami, Hironaru; Held, Karsten; Kawayama, Iwao; Kono, Junichiro; Battiato, Marco; Tonouchi, Masayoshi
    The one-dimensional confinement of quasiparticles in individual carbon nanotubes (CNTs) leads to extremely anisotropic electronic and optical properties. In a macroscopic ensemble of randomly oriented CNTs, this anisotropy disappears together with other properties that make them attractive for certain device applications. The question however remains if not only anisotropy but also other types of behaviors are suppressed by disorder. Here, we compare the dynamics of quasiparticles under strong electric fields in aligned and random CNT networks using a combination of terahertz emission and photocurrent experiments and out-of-equilibrium numerical simulations. We find that the degree of alignment strongly influences the excited quasiparticles’ dynamics, rerouting the thermalization pathways. This is, in particular, evidenced in the high-energy, high-momentum electronic population (probed through the formation of low energy excitons via exciton impact ionization) and the transport regime evolving from diffusive to superdiffusive.
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    Synthesis of plasmonic gold nanoparticles on soft materials for biomedical applications
    (Elsevier, 2023) Granata, Federica; Pirillo, Noemi; Alabastri, Alessandro; Schirato, Andrea; Bruno, Luigi; Costa, Roberta; Malara, Natalia; Onesto, Valentina; Coluccio, Maria Laura; Iodice, Mario; Coppola, Giuseppe; Gentile, Francesco
    Plasmonic metal nanomaterials are usually supported by rigid substrates, typically made of silicon or glass. Recently, there has been growing interest in developing soft plasmonic devices. Such devices are low weight, low cost, exhibit elevated flexibility and improved mechanical properties. Moreover, they maintain the features of conventional nano-optic structures, such as the ability to enhance the local electromagnetic field. On account of these characteristics, they show promise as efficient biosensors in biological, medical, and bio-engineering applications. Here, we demonstrate the fabrication of soft polydimethylsiloxane (PDMS) plasmonic devices. Using a combination of techniques, including electroless deposition, we patterned thin membranes of PDMS with arrays of gold nanoparticle clusters. Resulting devices show regular patterns of gold nanoparticles extending over several hundreds of microns and are moderately hydrophilic, with a contact angle of about 80°. At the nanoscale, scanning electron and atomic force microscopy of samples reveal an average particle size of ∼50 nm. The nanoscopic size of the particles, along with their random distribution in a cluster, promotes the enhancement of electromagnetic fields, evidenced by numerical simulations and experiments. Mechanical characterization and the stress-strain relationship indicate that the device has a stiffness of 2.8 MPa. In biological immunoassay tests, the device correctly identified and detected anti-human immunoglobulins G (IgG) in solution with a concentration of 25 μg/ml.
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    Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes
    (Cell Press, 2023) Lycke, Roy; Kim, Robin; Zolotavin, Pavlo; Montes, Jon; Sun, Yingchu; Koszeghy, Aron; Altun, Esra; Noble, Brian; Yin, Rongkang; He, Fei; Totah, Nelson; Xie, Chong; Luan, Lan; Rice Neuroengineering Initiative
    Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation are often compromised by adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-nanoelectronic threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 μA. Importantly, StimNETs evoke longitudinally stable behavioral responses for over 8 months at a markedly low charge injection of 0.25 nC/phase. Quantified histological analyses show that chronic ICMS by StimNETs induces no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially selective neuromodulation at low currents, which lessens risk of tissue damage or exacerbation of off-target side effects.
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    An energy management system model with power quality constraints for unbalanced multi-microgrids interacting in a local energy market
    (Elsevier, 2023) Castellanos, Johanna; Correa-Flórez, Carlos Adrián; Garcés, Alejandro; Ordóñez-Plata, Gabriel; Uribe, César A.; Patino, Diego
    As multi-microgrids become readily available, some limited models have been proposed that study operational and power quality constraints with local energy markets independently. This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interactions in a Local Energy Market (LEM) for unbalanced microgrids (MGs). The LEM consists of a pre-dispatch step and an energy transactions step (ETS). The ETS combines the MGs’ objectives while considering two strategies: minimize the cost of buyers or maximize the revenue of sellers. Our proposed model considers harmonic distortion and voltage limit power quality constraints in both steps. Moreover, we model operational constraints such as power flow, power balance, and distributed energy resources behaviors and capacities. We numerically evaluate the proposed model using three unbalanced MGs with residential, industrial, and commercial load profiles, where each microgrid manages its resources locally. Furthermore, we create two groups of cases to analyze the interactions in the local energy market. In the first group, the price of the DSO energy and the surplus from MGs to DSO are the same. The numerical results show that using the increasing revenue strategy promotes MGs to interact more while encouraging them to have high energy prices. When the reducing cost strategy is used, fewer energy interactions occur, and the price of MGs energy is encouraged to be lower.
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    Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
    (IEEE, 2023) Shen, Guanxiong; Zhang, Junqing; Marshall, Alan; Valkama, Mikko; Cavallaro, Joseph R.
    Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.
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    Phonon-Assisted Intertube Electronic Transport in an Armchair Carbon Nanotube Film
    (American Physical Society, 2023) Adinehloo, Davoud; Gao, Weilu; Mojibpour, Ali; Kono, Junichiro; Perebeinos, Vasili; The Smalley-Curl Institute
    The electrical conductivity of a macroscopic assembly of nanomaterials is determined through a complex interplay of electronic transport within and between constituent nano-objects. Phonons play dual roles in this situation: their increased populations tend to reduce the conductivity via electron scattering, while they can boost the conductivity by assisting electrons to propagate through the potential-energy landscape. We identified a phonon-assisted coherent electron transport process between neighboring nanotubes in temperature-dependent conductivity measurements on a macroscopic film of armchair single-wall carbon nanotubes. Through atomistic modeling of electronic states and calculations of both electronic and phonon-assisted junction conductances, we conclude that phonon-assisted conductance is the dominant mechanism for observed high-temperature transport in armchair carbon nanotubes. The unambiguous manifestation of coherent intertube dynamics proves a single-chirality armchair nanotube film to be a unique macroscopic solid-state ensemble of nano-objects promising for the development of room-temperature coherent electronic devices.
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    Measuring complex refractive index through deep-learning-enabled optical reflectometry
    (IOP Publishing, 2023) Wang, Ziyang; Lin, Yuxuan Cosmi; Zhang, Kunyan; Wu, Wenjing; Huang, Shengxi
    Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet. Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.
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    Exploiting social graph networks for emotion prediction
    (Springer Nature, 2023) Khalid, Maryam; Sano, Akane; Computational Wellbeing Group
    Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person’s physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person’s physiology, we also incorporate the environment’s impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user’s social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model’s performance.
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    Dynamical latent state computation in the male macaque posterior parietal cortex
    (Springer Nature, 2023) Lakshminarasimhan, Kaushik J.; Avila, Eric; Pitkow, Xaq; Angelaki, Dora E.
    Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
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    Analysis of scalable channel estimation in FDD massive MIMO
    (Springer Nature, 2023) Zhang, Xing; Sabharwal, Ashutosh
    One of the key ideas for reducing downlink channel acquisition overhead for FDD massive MIMO systems is to exploit a combination of two assumptions: (i) the dimension of channel models in propagation domain may be much smaller than the next-generation base-station array sizes (e.g., 64 or more antennas), and (ii) uplink and downlink channels may share the same low-dimensional propagation domain. Our channel measurements demonstrate that the two assumptions may not always hold, thereby impacting the predicted performance of methods that rely on the above assumptions. In this paper, we analyze the error in modeling the downlink channel using uplink measurements, caused by the mismatch from the above two assumptions. We investigate how modeling error varies with base-station array size and provide both numerical and experimental results. We observe that modeling error increases with the number of base-station antennas, and channels with larger angular spreads have larger modeling error. Utilizing our modeling error analysis, we then investigate the resulting beamforming performance rate loss. Accordingly, we observe that the rate loss increases with the number of base-station antennas, and channels with larger angular spreads suffer from higher rate loss.
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    Robust deep learning object recognition models rely on low frequency information in natural images
    (PLOS, 2023) Li, Zhe; Caro, Josue Ortega; Rusak, Evgenia; Brendel, Wieland; Bethge, Matthias; Anselmi, Fabio; Patel, Ankit B.; Tolias, Andreas S.; Pitkow, Xaq
    Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.
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    Exploring Topological Semi-Metals for Interconnects
    (MDPI, 2023) Kundu, Satwik; Roy, Rupshali; Rahman, M. Saifur; Upadhyay, Suryansh; Topaloglu, Rasit Onur; Mohney, Suzanne E.; Huang, Shengxi; Ghosh, Swaroop
    The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level interconnect design using NbAs, a topological semi-metal with high electron mobility at the nanoscale, and demonstrated its advantages over Cu in terms of resistivity, propagation delay, and slew rate. Our simulations revealed that NbAs could reduce the propagation delay by up to 35.88%. We further evaluated the potential system-level performance gain for NbAs-based interconnects in cache memories and observed an instructions per cycle (IPC) improvement of up to 23.8%.
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    Technology Implementation for Mental Health End Users: A Model to Guide Digital Transformation for Inpatient Mental Health Professionals
    (JMIR, 2023) Westheimer, Jessa Lin; Moukaddam, Nidal; Lindsay, Jan A.; Sabharwal, Ashutosh; Najafi, Bijan; Iacobelli, Peter A.; Boland, Robert J.; Patriquin, Michelle A.
    Digital transformation is the adoption of digital technologies by an entity in an effort to increase operational efficiency. In mental health care, digital transformation entails technology implementation to improve the quality of care and mental health outcomes. Most psychiatric hospitals rely heavily on “high-touch” interventions or those that require in-person, face-to-face interaction with the patient. Those that are exploring digital mental health care interventions, particularly for outpatient care, often copiously commit to the “high-tech” model, losing the crucial human element. The process of digital transformation, especially within acute psychiatric treatment settings, is in its infancy. Existing implementation models outline the development of patient-facing treatment interventions within the primary care system; however, to our knowledge, there is no proposed or established model for implementing a new provider-facing ministration tool within an acute inpatient psychiatric setting. Solving the complex challenges within mental health care demands that new mental health technology is developed in concert with a use protocol by and for the inpatient mental health professional (IMHP; the end user), allowing the “high-touch” to inform the “high-tech” and vice versa. Therefore, in this viewpoint article, we propose the Technology Implementation for Mental-Health End-Users framework, which outlines the process for developing a prototype of an IMHP-facing digital intervention tool in parallel with a protocol for the IMHP end user to deliver the intervention. By balancing the design of the digital mental health care intervention tool with IMHP end user resource development, we can significantly improve mental health outcomes and pioneer digital transformation nationwide.
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    Magnetically tuned continuous transition from weak to strong coupling in terahertz magnon polaritons
    (American Physical Society, 2023) Baydin, Andrey; Hayashida, Kenji; Makihara, Takuma; Tay, Fuyang; Ma, Xiaoxuan; Ren, Wei; Ma, Guohong; Noe, G. Timothy; Katayama, Ikufumi; Takeda, Jun; Nojiri, Hiroyuki; Cao, Shixun; Bamba, Motoaki; Kono, Junichiro; Smalley-Curl Institute
    Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ∼14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems.
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    The 2023 terahertz science and technology roadmap
    (IOP Publishing, 2023) Leitenstorfer, Alfred; Moskalenko, Andrey S.; Kampfrath, Tobias; Kono, Junichiro; Castro-Camus, Enrique; Peng, Kun; Qureshi, Naser; Turchinovich, Dmitry; Tanaka, Koichiro; Markelz, Andrea G.; Havenith, Martina; Hough, Cameron; Joyce, Hannah J.; Padilla, Willie J.; Zhou, Binbin; Kim, Ki-Yong; Zhang, Xi-Cheng; Jepsen, Peter Uhd; Dhillon, Sukhdeep; Vitiello, Miriam; Linfield, Edmund; Davies, A. Giles; Hoffmann, Matthias C.; Lewis, Roger; Tonouchi, Masayoshi; Klarskov, Pernille; Seifert, Tom S.; Gerasimenko, Yaroslav A.; Mihailovic, Dragan; Huber, Rupert; Boland, Jessica L.; Mitrofanov, Oleg; Dean, Paul; Ellison, Brian N.; Huggard, Peter G.; Rea, Simon P.; Walker, Christopher; Leisawitz, David T.; Gao, Jian Rong; Li, Chong; Chen, Qin; Valušis, Gintaras; Wallace, Vincent P.; Pickwell-MacPherson, Emma; Shang, Xiaobang; Hesler, Jeffrey; Ridler, Nick; Renaud, Cyril C.; Kallfass, Ingmar; Nagatsuma, Tadao; Zeitler, J. Axel; Arnone, Don; Johnston, Michael B.; Cunningham, John
    Terahertz (THz) radiation encompasses a wide spectral range within the electromagnetic spectrum that extends from microwaves to the far infrared (100 GHz–∼30 THz). Within its frequency boundaries exist a broad variety of scientific disciplines that have presented, and continue to present, technical challenges to researchers. During the past 50 years, for instance, the demands of the scientific community have substantially evolved and with a need for advanced instrumentation to support radio astronomy, Earth observation, weather forecasting, security imaging, telecommunications, non-destructive device testing and much more. Furthermore, applications have required an emergence of technology from the laboratory environment to production-scale supply and in-the-field deployments ranging from harsh ground-based locations to deep space. In addressing these requirements, the research and development community has advanced related technology and bridged the transition between electronics and photonics that high frequency operation demands. The multidisciplinary nature of THz work was our stimulus for creating the 2017 THz Science and Technology Roadmap (Dhillon et al 2017 J. Phys. D: Appl. Phys. 50 043001). As one might envisage, though, there remains much to explore both scientifically and technically and the field has continued to develop and expand rapidly. It is timely, therefore, to revise our previous roadmap and in this 2023 version we both provide an update on key developments in established technical areas that have important scientific and public benefit, and highlight new and emerging areas that show particular promise. The developments that we describe thus span from fundamental scientific research, such as THz astronomy and the emergent area of THz quantum optics, to highly applied and commercially and societally impactful subjects that include 6G THz communications, medical imaging, and climate monitoring and prediction. Our Roadmap vision draws upon the expertise and perspective of multiple international specialists that together provide an overview of past developments and the likely challenges facing the field of THz science and technology in future decades. The document is written in a form that is accessible to policy makers who wish to gain an overview of the current state of the THz art, and for the non-specialist and curious who wish to understand available technology and challenges. A such, our experts deliver a ‘snapshot’ introduction to the current status of the field and provide suggestions for exciting future technical development directions. Ultimately, we intend the Roadmap to portray the advantages and benefits of the THz domain and to stimulate further exploration of the field in support of scientific research and commercial realisation.