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Microbial biophotovoltaic cells exploit the ability of cyanobacteria and microalgae to convert light energy into electrical current using water as the source of electrons. Such bioelectrochemical systems have a clear advantage over more conventional microbial fuel cells which require the input of organic carbon for microbial growth. However, innovative approaches are needed to address scale-up issues associated with the fabrication of the inorganic (electrodes) and biological (microbe) parts of the biophotovoltaic device. Here we demonstrate the feasibility of using a simple commercial inkjet printer to fabricate a thin-film paper-based biophotovoltaic cell consisting of a layer of cyanobacterial cells on top of a carbon nanotube conducting surface. We show that these printed cyanobacteria are capable of generating a sustained electrical current both in the dark (as a ‘solar bio-battery’) and in response to light (as a ‘bio-solar-panel’) with potential applications in low-power devices. [ABSTRACT FROM AUTHOR]

Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits of unopened letters secured shut with "letterlocking," a practice—systematized in this paper—which underpinned global communications security for centuries before modern envelopes. We present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of a locked letter with complex internal folding, producing legible images of the letter's contents and crease pattern while preserving letterlocking evidence. We demonstrate our method on four letterpackets from Renaissance Europe, reading the contents of one unopened letter for the first time. Using the results of virtual unfolding, we situate our findings within a novel letterlocking categorization chart based on our study of 250,000 historical letters. Here, the authors present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of locked letters with complex internal folding, producing legible images of the letter's contents and crease pattern while preserving letterlocking evidence. [ABSTRACT FROM AUTHOR]

Over the past decade, an unexpected cooling trend has been observed in East Asia and North America during winter. Climate model simulations suggest that this pattern of stalled warming, besides accelerated warming, will repeat throughout the course of global warming, influenced by the natural decade-long variations in the climate system. However, understanding the exact factors affecting the pace of warming remains a challenge. Here we show that a pause in warming over continental areas—namely, local warming hiatus—can be accompanied by excessive heat accumulation north of the ocean fronts. This oceanic condition, often manifesting in the form of marine heatwaves, constrains the subseasonal growth of atmospheric planetary waves, significantly increasing the likelihood of cold extremes in downstream continents. Our results underscore the importance of closely monitoring changing ocean fronts in response to human-induced warming, which can potentially reshape the inherent decade-long fluctuations within regional climates over the long term.This paper shows the inherent coupling of winter cold extremes over land and marine heatwaves in the past decade. These events are projected to recur with increased frequency, especially when ocean fronts undergo anomalous decadal warming. [ABSTRACT FROM AUTHOR]

Metal nanoparticle-organic interfaces are common but remain elusive for controlling reactions due to the complex interactions of randomly formed ligand-layers. This paper presents an approach for enhancing the selectivity of catalytic reactions by constructing a skin-like few-nanometre ultrathin crystalline porous covalent organic overlayer on a plasmonic nanoparticle surface. This organic overlayer features a highly ordered layout of pore openings that facilitates molecule entry without any surface poisoning effects and simultaneously endows favourable electronic effects to control molecular adsorption–desorption. Conformal organic overlayers are synthesised through the plasmonic oxidative activation and intermolecular covalent crosslinking of molecular units. We develop a light-operated multicomponent interfaced plasmonic catalytic platform comprising Pd-modified gold nanoparticles inside hollow silica to achieve the highly efficient and selective semihydrogenation of alkynes. This approach demonstrates a way to control molecular adsorption behaviours on metal surfaces, breaking the linear scaling relationship and simultaneously enhancing activity and selectivity. Controlling molecular interactions on metal surfaces for selective chemical synthesis remains difficult to achieve. Here, we report thin, porous covalent organic overlayers on metal nanocrystals to improve selectivity through plasmonic activation. [ABSTRACT FROM AUTHOR]

Gallium nitride-based light-emitting diodes have revolutionized the lighting market by becoming the most energy-efficient light sources. However, the power grid, in example electricity delivery system, is built based on alternating current, which raises problems for directly driving light emitting diodes that require direct current to operate effectively. In this paper, we demonstrate a proof-of-concept device that addresses this fundamental issue – a gallium nitride-based bidirectional light-emitting diode. Its structure is symmetrical with respect to the active region, which, depending on the positive or negative bias, allows for the injection of either electrons or holes from each side. It is composed of two tunnel junctions that surround the active region. In this work, the optical and electrical properties of bidirectional light emitting diodes are investigated under direct and alternating current conditions. We find that the light is emitted in both directions of the supplied current, contrary to conventional light emitting diodes; hence, bidirectional light-emitting diodes can be considered a semiconductor light source powered directly with alternating current. In addition, we show that bidirectional light-emitting diodes can be stacked vertically to multiply the optical power achieved from a single device. The bidirectional light-emitting diode emits visible light regardless of the direction of current flow, leading to AC applications. This is made possible by the use of two oppositely oriented tunnel junctions that surround the active region. [ABSTRACT FROM AUTHOR]

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect. Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data. [ABSTRACT FROM AUTHOR]

In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate. This paper shows that weather and fuel precursors show complementary predictability of wildfires extending across different timescales, which may be leveraged for seasonal or interannual wildfire prediction. [ABSTRACT FROM AUTHOR]

Non-volatile memories (NVMs) have the potential to reshape next-generation memory systems because of their promising properties of near-zero leakage power consumption, high density and non-volatility. However, NVMs also face critical security threats that exploit the non-volatile property. Compared to volatile memory, the capability of retaining data even after power down makes NVM more vulnerable. Existing solutions to address the security issues of NVMs are mainly based on Advanced Encryption Standard (AES), which incurs significant performance and power overhead. In this paper, we propose a lightweight memory encryption/decryption scheme by exploiting in-situ memory operations with negligible overhead. To validate the feasibility of the encryption/decryption scheme, device-level and array-level experiments are performed using ferroelectric field effect transistor (FeFET) as an example NVM without loss of generality. Besides, a comprehensive evaluation is performed on a 128 × 128 FeFET AND-type memory array in terms of area, latency, power and throughput. Compared with the AES-based scheme, our scheme shows ~22.6×/~14.1× increase in encryption/decryption throughput with negligible power penalty. Furthermore, we evaluate the performance of our scheme over the AES-based scheme when deploying different neural network workloads. Our scheme yields significant latency reduction by 90% on average for encryption and decryption processes.Existing solutions based Advanced Encryption Standard to address the security issues of nonvolatile memories incurs significant performance and power overhead. Here, the authors propose a lightweight XOR-gate based encryption/decryption technique by exploiting in-situ array operations, which achieves significant area/latency/power reduction compared to conventional designs. [ABSTRACT FROM AUTHOR]

We report on the existence of two phosphatidic acid biosynthetic pathways in mycobacteria, a classical one wherein the acylation of the sn-1 position of glycerol-3-phosphate (G3P) precedes that of sn-2 and another wherein acylations proceed in the reverse order. Two unique acyltransferases, PlsM and PlsB2, participate in both pathways and hold the key to the unusual positional distribution of acyl chains typifying mycobacterial glycerolipids wherein unsaturated substituents principally esterify position sn-1 and palmitoyl principally occupies position sn-2. While PlsM selectively transfers a palmitoyl chain to the sn-2 position of G3P and sn-1-lysophosphatidic acid (LPA), PlsB2 preferentially transfers a stearoyl or oleoyl chain to the sn-1 position of G3P and an oleyl chain to sn-2-LPA. PlsM is the first example of an sn-2 G3P acyltransferase outside the plant kingdom and PlsB2 the first example of a 2-acyl-G3P acyltransferase. Both enzymes are unique in their ability to catalyze acyl transfer to both G3P and LPA. This paper reports on two mycobacterial glycerolipid acyltransferases capable of transferring acyl chains to position 1 or position 2 of both glycerol-3-phosphate and lysophosphatidic acid. [ABSTRACT FROM AUTHOR]

Ferromagnetism and superconductivity are two key ingredients for topological superconductors, which can serve as building blocks of fault-tolerant quantum computers. Adversely, ferromagnetism and superconductivity are typically also two hostile orderings competing to align spins in different configurations, and thus making the material design and experimental implementation extremely challenging. A single material platform with concurrent ferromagnetism and superconductivity is actively pursued. In this paper, we fabricate van der Waals Josephson junctions made with iron-based superconductor Fe(Te,Se), and report the global device-level transport signatures of interfacial ferromagnetism emerging with superconducting states for the first time. Magnetic hysteresis in the junction resistance is observed only below the superconducting critical temperature, suggesting an inherent correlation between ferromagnetic and superconducting order parameters. The 0-π phase mixing in the Fraunhofer patterns pinpoints the ferromagnetism on the junction interface. More importantly, a stochastic field-free superconducting diode effect was observed in Josephson junction devices, with a significant diode efficiency up to 10%, which unambiguously confirms the spontaneous time-reversal symmetry breaking. Our work demonstrates a new way to search for topological superconductivity in iron-based superconductors for future high Tc fault-tolerant qubit implementations from a device perspective. The authors study Josephson junctions where the superconductors are Fe(Te,Se) flakes and the weak link is just a 0.36 nm van-der-Waals gap between the two stacked flakes. They report global device-level transport signatures of interfacial ferromagnetism. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key process linking surface and atmospheric energy budgets, yet its drivers and patterns across wetlandscapes are poorly understood worldwide. Here we assess the ET dynamics in 12 wetland complexes across South America, revealing major differences under temperate, tropical, and equatorial climates. While net radiation is a dominant driver of ET seasonality in most environments, flooding also contributes strongly to ET in tropical and equatorial wetlands, especially in meeting the evaporative demand. Moreover, significant water losses through wetlands and ET differences between wetlands and uplands occur in temperate, more water-limited environments and in highly flooded areas such as the Pantanal, where slow river flood propagation drives the ET dynamics. Finally, floodplain forests produce the greatest ET in all environments except the Amazon River floodplains, where upland forests sustain high rates year round. Our findings highlight the unique hydrological functioning and ecosystem services provided by wetlands on a continental scale. Wetlands can affect regional climate by altering surface-atmosphere interactions. This paper investigates drivers and patterns of evapotranspiration in South American wetlands, from the Amazon floodplains to the large Pantanal system. [ABSTRACT FROM AUTHOR]

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.De novo peptide sequencing allows the identification of peptides without requiring target databases. Here, the authors present PepNet, a convolutional neural network model for accurate de novo peptide sequencing that is capable of analysing large-scale proteomics data. [ABSTRACT FROM AUTHOR]

Optical spectroscopic sensors are a powerful tool to reveal light-matter interactions in many fields. Miniaturizing the currently bulky spectrometers has become imperative for the wide range of applications that demand in situ or even in vitro characterization systems, a field that is growing rapidly. In this paper, we propose a novel integrated reconstructive spectrometer with programmable photonic circuits by simply using a few engineered MZI elements. This design effectively creates an exponentially scalable number of uncorrelated sampling channels over an ultra-broad bandwidth without incurring additional hardware costs, enabling ultra-high resolution down to single-digit picometers. Experimentally, we implement an on-chip spectrometer with a 6-stage cascaded MZI structure and demonstrate <10 pm resolution with >200 nm bandwidth using only 729 sampling channels. This achieves a bandwidth-to-resolution ratio of over 20,000, which is, to our best knowledge, about one order of magnitude greater than any reported miniaturized spectrometers to date. Recent years have seen a growing need for miniaturized spectroscopic tools. Here, authors present a novel integrated spectrometer with programmable photonic circuits, achieving record-high resolution and bandwidth via only a few filtering components. [ABSTRACT FROM AUTHOR]

Structural superlubricity, a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in micro-electro-mechanical systems devices, mechanical engineering, and energy resources. A critical step towards the practical application of structural superlubricity is the mass transfer and high throughput performance evaluation. Limited by the yield rate of material preparation, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and tribological performance measurement for structural superlubricity materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with structural superlubricity properties and complete the 100 graphite flakes assembly array to form various pre-designed patterns within 100 mins, which is 15 times faster than manual operation. Besides, the system is capable of automatically measuring the tribological performance of over 100 selected flakes on Si3N4, delivering statistical results for new interface which is beyond the reach of traditional methods. With its high accuracy, efficiency, and robustness, this machine learning-assisted system promotes the fundamental research and practical application of structural superlubricity. A critical step to enable practical structural superlubricity (SSL) applications is to enable high throughput to both fabrication and performance evaluation. Here, the authors demonstrate an automated system for efficient and multiple SSL materials transfer and tribological measurement. [ABSTRACT FROM AUTHOR]

The cross-dehydrogenative coupling (CDC) reaction is the most direct and efficient method for constructing α-tertiary amino acids (ATAAs), which avoids the pre-activation of C(sp3)-H substrates. However, the use of transition metals and harsh reaction conditions are still significant challenges for these reactions that urgently require solutions. This paper presents a mild, metal-free CDC reaction for the construction of ATAAs, which is compatible with various benzyl C-H substrates, functionalized C-H substrates, and alkyl substrates, with good regioselectivity. Notably, our method exhibits excellent functional group tolerance and late-stage applicability. According to mechanistic studies, the one-step synthesized and bench-stable N-alkoxyphtalimide generates a highly electrophilic trifluoro ethoxy radical that serves as a key intermediate in the reaction process and acts as a hydrogen atom transfer reagent. Therefore, our metal-free and additive-free method offers a promising strategy for the synthesis of ATAAs under mild conditions. The cross-dehydrogenative coupling (CDC) reaction is the most efficient method for constructing α-tertiary amino acids (ATAAs). Here, the authors present a mild, metal-free CDC reaction for the construction of ATAAs with good regioselectivity. [ABSTRACT FROM AUTHOR]

Electrocatalytic CO2 reduction into value-added multicarbon products offers a means to close the anthropogenic carbon cycle using renewable electricity. However, the unsatisfactory catalytic selectivity for multicarbon products severely hinders the practical application of this technology. In this paper, we report a cascade AgCu single-atom and nanoparticle electrocatalyst, in which Ag nanoparticles produce CO and AgCu single-atom alloys promote C-C coupling kinetics. As a result, a Faradaic efficiency (FE) of 94 ± 4% toward multicarbon products is achieved with the as-prepared AgCu single-atom and nanoparticle catalyst under ~720 mA cm−2 working current density at −0.65 V in a flow cell with alkaline electrolyte. Density functional theory calculations further demonstrate that the high multicarbon product selectivity results from cooperation between AgCu single-atom alloys and Ag nanoparticles, wherein the Ag single-atom doping of Cu nanoparticles increases the adsorption energy of *CO on Cu sites due to the asymmetric bonding of the Cu atom to the adjacent Ag atom with a compressive strain. Electrocatalytic CO2 reduction into multicarbon products offers a means to close the anthropogenic carbon cycle using renewable electricity. Here, the authors report a cascade AgCu single-atom and nanoparticle electrocatalyst with favorable properties to improve the selectivity of multicarbon products. [ABSTRACT FROM AUTHOR]

Bandwidth and noise are fundamental considerations in all communication and signal processing systems. The group-velocity dispersion of optical fibers creates nulls in their frequency response, limiting the bandwidth and hence the temporal response of communication and signal processing systems. Intensity noise is often the dominant optical noise source for semiconductor lasers in data communication. In this paper, we propose and demonstrate a class of electrooptic modulators that is capable of mitigating both of these problems. The modulator, fabricated in thin-film lithium niobate, simultaneously achieves phase diversity and differential operations. The former compensates for the fiber's dispersion penalty, while the latter overcomes intensity noise and other common mode fluctuations. Applications of the so-called four-phase electrooptic modulator in time-stretch data acquisition and in optical communication are demonstrated. In this work, the authors showcase four-phase electrooptic modulators (FEOMs) implemented on thin-film lithium niobate. This innovation effectively addresses challenges related to dispersion and semiconductor laser noise limitations, offering a promising solution for integrated photonic applications. [ABSTRACT FROM AUTHOR]

Pour sand into a container and only the grains near the top surface move. The collective motion associated with the translational and rotational energy of the grains in a thin flowing layer is quickly dissipated as friction through multibody interactions. Alternatively, consider what will happen to a bed of particles if one applies a torque to each individual particle. In this paper, we demonstrate an experimental system where torque is applied at the constituent level through a rotating magnetic field in a dense bed of microrollers. The net result is the grains roll uphill, forming a heap with a negative angle of repose. Two different regimes have been identified related to the degree of mobility or fluidisation of the particles in the bulk. Velocimetry of the near surface flowing layer reveals the collective motion of these responsive particles scales in a similar way to flowing bulk granular flows. A simple granular model that includes cohesion accurately predicts the apparent negative coefficient of friction. In contrast to the response of active or responsive particles that mimic thermodynamic principles, this system results in macroscopic collective behavior that has the kinematics of a purely dissipative granular system. Kinetic energy put into a granular medium as a collective is typically dissipated as friction. The situation is different when forces are applied to the individual particles. An experiment now shows that when torques are applied to particles in a dense bed of microrollers, the grains roll uphill. [ABSTRACT FROM AUTHOR]

Peptide-based therapeutics have gained attention as promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. In this paper, we report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry, with decafluoro-diphenylsulfone (DFS). Testing of the binding of the selected peptides to albumin identified SICRFFC as the lead sequence. We replaced DFS with isosteric pentafluorophenyl sulfide (PFS) and the PFS-SICRFFCGG exhibited KD = 4–6 µM towards human serum albumin. When injected in mice, the concentration of the PFS-SICRFFCGG in plasma was indistinguishable from the reference peptide, SA-21. More importantly, a conjugate of PFS-SICRFFCGG and peptide apelin-17 analogue (N3-PEG6-NMe17A2) showed retention in circulation similar to SA-21; in contrast, apelin-17 analogue was cleared from the circulation after 2 min. The PFS-SICRFFC is the smallest known peptide macrocycle with a significant affinity for human albumin and substantial in vivo circulation half-life. It is a productive starting point for future development of compact macrocycles with extended half-life in vivo. Peptide-based therapeutics are promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. Here, the authors report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine chemistry, with decafluoro-diphenylsulfone. [ABSTRACT FROM AUTHOR]

Abnormal high temperature (HT) caused by global warming threatens plant survival and food security, but the effects of HT on plant organ identity are elusive. Here, we show that Class II TEOSINTE BRANCHED 1/CYCLOIDEA/ PCF (TCP) transcription factors redundantly protect ovule identity under HT. The duodecuple tcp2/3/4/5/10/13/17/24/1/12/18/16 (tcpDUO) mutant displays HT-induced ovule conversion into carpelloid structures. Expression of TCP4 in tcpDUO complements the ovule identity conversion. TCP4 interacts with AGAMOUS (AG), SEPALLATA3 (SEP3), and the homeodomain transcription factor BELL1 (BEL1) to strengthen the association of BEL1 with AG-SEP3. The tcpDUO mutant synergistically interacts with bel1 and the ovule identity gene seedstick (STK) mutant stk in tcpDUO bel1 and tcpDUO stk. Our findings reveal the critical roles of Class II TCPs in maintaining ovule identity under HT and shed light on the molecular mechanisms by which ovule identity is determined by the integration of internal factors and environmental temperature. Global warming threatens food security and plant survival. This paper demonstrates that Class II TCP transcription factors are critical for specifying ovule identity and inhibiting the conversion of ovules into carpelloids under high temperature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multimodal flexible sensory interface for interactively teaching soft robots to perform skilled locomotion using bare human hands. First, we develop a flexible bimodal smart skin (FBSS) based on triboelectric nanogenerator and liquid metal sensing that can perform simultaneous tactile and touchless sensing and distinguish these two modes in real time. With the FBSS, soft robots can react on their own to tactile and touchless stimuli. We then propose a distance control method that enabled humans to teach soft robots movements via bare hand-eye coordination. The results showed that participants can effectively teach a self-reacting soft continuum manipulator complex motions in three-dimensional space through a "shifting sensors and teaching" method within just a few minutes. The soft manipulator can repeat the human-taught motions and replay them at different speeds. Finally, we demonstrate that humans can easily teach the soft manipulator to complete specific tasks such as completing a pen-and-paper maze, taking a throat swab, and crossing a barrier to grasp an object. We envision that this user-friendly, non-programmable teaching method based on flexible multimodal sensory interfaces could broadly expand the domains in which humans interact with and utilize soft robots. Soft robots are challenging to model and program. Non-specialists face non-negligible obstacles when working with soft robots to perform tasks. Here, the authors propose a method to interactively teach soft robots complex motions through flexible touchless and tactile multimodal sensors. [ABSTRACT FROM AUTHOR]

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2. Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. In this paper, the authors propose a strategy that uses homomorphic encryption, which guarantees information confidentiality while retaining action detection. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]