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Empirical investigation of the quintillion-scale, functionally diverse antibody repertoires that can be generated synthetically or naturally is critical for identifying potential biotherapeutic leads, yet remains burdensome. We present high-throughput nanophotonics- and bioprinter-enabled screening (HT-NaBS), a multiplexed assay for large-scale, sample-efficient, and rapid characterization of antibody libraries. Our platform is built upon independently addressable pixelated nanoantennas exhibiting wavelength-scale mode volumes, high-quality factors (high-Q) exceeding 5000, and pattern densities exceeding one million sensors per square centimeter. Our custom-built acoustic bioprinter enables individual sensor functionalization via the deposition of picoliter droplets from a library of capture antigens at rates up to 25,000 droplets per second. We detect subtle differentiation in the target binding signature through spatially-resolved spectral imaging of hundreds of resonators simultaneously, elucidating antigen-antibody binding kinetic rates, affinity constant, and specificity. We demonstrate HT-NaBS on a panel of antibodies targeting SARS-CoV-2, Influenza A, and Influenza B antigens, with a sub-picomolar limit of detection within 30 minutes. Furthermore, through epitope binning analysis, we demonstrate the competence and diversity of a library of native antibodies targeting functional epitopes on a priority pathogen (H5N1 bird flu) and on glycosylated therapeutic Cetuximab antibodies against epidermal growth factor receptor. With a roadmap to image tens of thousands of sensors simultaneously, this high-throughput, resource-efficient, and label-free platform can rapidly screen for high-affinity and broad epitope coverage, accelerating biotherapeutic discovery and de novo protein design.

In this paper, using the ensemble-averaged theory, we define the thermodynamic free energy of Einstein-Gauss-Bonnet (EGB) black holes in anti-de Sitter (AdS) spacetime. This approach derives the gravitational partition function by incorporating non-saddle geometries besides the classical solutions. Unlike the sharp transition points seen in free energy calculated via saddle-point approximation, the ensemble-averaged free energy plotted against temperature shows a smoother behavior, suggesting that black hole phase transitions may be viewed as a small $G_N$ (Newton's gravitational constant) limit of the ensemble theory. This is similar to the behavior of black hole solutions in Einstein's gravity theory in AdS spacetime. We have obtained an expression for the quantum-corrected free energy for EGB-AdS black holes, and in the six-dimensional case, we observe a well-defined local minimum after the transition temperature which was absent in the earlier analysis of the classical free energy landscape. Furthermore, we expand the ensemble-averaged free energy in powers of $G_N$ to identify non-classical contributions. Our findings indicate that the similarities in the thermodynamic behavior between five-dimensional EGB-AdS and Reissner-Nordstr\"om-AdS (RN-AdS) black holes, as well as between six-dimensional EGB-AdS and Schwarzschild-AdS black holes, extend beyond the classical regime., Comment: 8 pages, 9 figures

The dynamics of the state-switching process of black holes in dRGT massive gravity theory is presented using free energy landscape and stochastic Langevin equations. The free energy landscape is constructed using the Gibbons-Hawking path integral method. The black hole phases are characterized by taking its horizon radius as the order parameter. The free energy landscape provides three black hole phases: small, intermediate, and large. The small and large black holes are thermodynamically stable whereas the intermediate one is unstable. The Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) functional describes the stochastic dynamics of black hole phase transition. The Hamiltonian flow lines are obtained from the MSRJD functional and are used to analyze the stability and the phase transition properties. The dominant kinetic path between different phases is discussed for various configurations of the free energy landscape. We discuss the effect of black hole charge and the graviton mass on the critical behavior of black hole phase transition., Comment: 13 pages, 10 figures

Metasurfaces provide a versatile and compact approach to free-space optical manipulation and wavefront shaping. Comprised of arrays of judiciously-arranged dipolar resonators, metasurfaces precisely control the amplitude, polarization, and phase of light, with applications spanning imaging, sensing, modulation, and computing. Three crucial performance metrics of metasurfaces and their constituent resonators are the quality factor ($Q$-factor), mode-volume ($V_m$), and the ability to control far-field radiation. Often, resonators face a trade-off between these parameters: a reduction in $V_m$ leads to an equivalent reduction in $Q$, albeit with more control over radiation. Here, we demonstrate that this perceived compromise is not inevitable $-$ high-$Q$, subwavelength $V_m$, and controlled dipole-like radiation can be achieved, simultaneously. We design high-$Q$, very-large-scale integrated silicon nanoantenna pixels $-$ VINPix $-$ that combine guided mode resonance waveguides with photonic crystal cavities. With optimized nanoantennas, we achieve $Q$-factors exceeding 1500 with $V_m$ less than 0.1 $(\lambda/n_{\text{air}})^3$. Each nanoantenna is individually addressable by free-space light, and exhibits dipole-like scattering to the far-field. Resonator densities exceeding a million nanoantennas per $\text{cm}^2$ can be achieved, as demonstrated by our fabrication of an 8 mm x 8 mm VINPix array. As a proof-of-concept application, we demonstrate spectrometer-free, spatially localized, refractive-index sensing utilizing a VINPix array. Our platform provides a foundation for compact, densely multiplexed devices such as spatial light modulators, computational spectrometers, and in-situ environmental sensors.

In this article, we investigate the solvability of $k$-set agreement among $n$ processes in distributed systems prone to different types of process failures. Specifically, we explore two scenarios: synchronous message-passing systems prone to up to $t$ Byzantine failures of processes. And asynchronous shared memory systems prone to up to $t$ crash failures of processes. Our goal is to address the gaps left by previous works\cite{SSS,AsynchKset} in these areas. For Byzantine failures case we consider systems with authentication where processes have unforgeable signatures. For synchronous message-passing systems, we present an authenticated algorithm that achieves $k$-set agreement in only two rounds, with no constraints on the number of faults $t$, with $k$ determined as $k \geq \lfloor \frac{n}{n-t} \rfloor + 1$. In fact the lower bound for $k$ is $k \geq \lfloor \frac{n}{n-t} \rfloor $ that is obtained by an algorithm based on traditional consensus with $t+1$ rounds. In asynchronous shared memory systems, we introduce an algorithm that accomplishes $k$-set agreement for values of $k$ greater than $ \lfloor \frac{n-t}{n-2t} \rfloor +1$. This algorithm uses a snapshot primitive to handle crash failures and enable effective set agreement.

The dynamical properties of small-large black hole phase transition in dRGT non-linear massive gravity theory are studied based on the underlying free energy landscape. The free energy landscape is constructed by specifying the Gibbs free energy to every state, and the free energy profile is used to study the different black hole phases. The small-large black hole states are characterized by probability distribution functions and the kinetics of phase transition are described by the Fokker-Planck equation. Further, a detailed study of the first passage process is presented which describes the dynamics of phase transitions. Finally, we have investigated the effect of mass and topology on the dynamical properties of phase transitions of black holes in dRGT non-linear massive gravity theory., Comment: 23 pages, 18 figures. Revised thoroughly after peer review. Accepted for publication in Annals of Physics

We probe the microstructure of the dRGT massive black hole in an anti-de Sitter background. The calculations are performed in an extended phase space with pressure and volume taken as fluctuation variables. We analyze the microstructure by exploiting the Ruppeiner geometry, where the thermodynamic curvature scalar is constructed via the adiabatic compressibility. The nature of the curvature scalar along the coexistence line of small (SBH) and large (LBH) black holes is investigated. In the microscopic interaction, we observe that the SBH phase behaves as an anyonic gas and the LBH phase is analogous to a boson gas. Further, we study the effect of graviton mass on the underlying microstructure of the black hole., Comment: 16 pages,12 figures. arXiv admin note: text overlap with arXiv:2304.02889 by other authors

The physical process version of the first law can be obtained for bifurcate Killing horizons with certain assumptions. Especially, one has to restrict to the situations where the horizon evolution is quasi-stationary, under perturbations. We revisit the analysis of this assumption considering the horizon perturbations of Rindler horizon by a spherically symmetric object. We demonstrate that even if the quasi-stationary assumption holds, the change in entropy, in four space-time dimensions, diverges when considered between asymptotic cross-sections. However, these divergences do not appear in higher dimensions. We also analyze these features in the presence of a positive cosmological constant. In the process, we prescribe a recipe to establish the physical process first law in such ill-behaved scenarios., Comment: 17 pages, 1 figure

Sales of Electric Vehicles (EVs) in the United States have grown fast in the past decade. We analyze the Electric Vehicle Drive Clean Rebate data from the New York State Energy Research and Development Authority (NYSERDA) to understand consumer behavior in EV purchasing and their potential environmental impact. Based on completed rebate applications since 2017, this dataset features the make and model of the EV that consumers purchased, the geographic location of EV consumers, transaction type to obtain the EV, projected environmental impact, and tax incentive issued. This analysis consists of a mapped and calculated statistical data analysis over an established period. Using the SAP Analytics Cloud (SAC), we first import and clean the data to generate statistical snapshots for some primary attributes. Next, different EV options were evaluated based on environmental carbon footprints and rebate amounts. Finally, visualization, geo, and time-series analysis presented further insights and recommendations. This analysis helps the reader to understand consumers' EV buying behavior, such as the change of most popular maker and model over time, acceptance of EVs in different regions in New York State, and funds required to support clean air initiatives. Conclusions from the current study will facilitate the use of renewable energy, reduce reliance on fossil fuels, and accelerate economic growth sustainably, in addition to analyzing the trend of rebate funding size over the years and predicting future funding., Comment: 4 pages, 6 figures

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when mixed with gold nanorods (GNRs), SERS enhancements of up to 1500x are achieved.We then train a ML model and achieve >=99% classification accuracy from cellularly-pure samples, and >=87% accuracy from cellularly-mixed samples. We also obtain >=90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings., Comment: Minor correction to Fig. 2a caption to fix transposed label. Minor update to Supplementary Fig. 31, top figure, to correct the referenced plot

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). Here, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. On spatial interpolation and regression tasks, we show the effectiveness of our approach, improving performance over different state-of-the-art GNN approaches. We observe that our approach not only vastly improves over the GNN baselines, but can match Gaussian processes, the most commonly utilized method for spatial interpolation problems., Comment: AISTATS 2023

Genetic analysis methods are foundational to advancing personalized and preventative medicine, accelerating disease diagnostics, and monitoring the health of organisms and ecosystems. Current nucleic acid technologies such as polymerase chain reaction (PCR), next-generation sequencing (NGS), and DNA microarrays rely on fluorescence and absorbance, necessitating sample amplification or replication and leading to increased processing time and cost. Here, we introduce a label-free genetic screening platform based on high quality (high-Q) factor silicon nanoantennas functionalized with monolayers of nucleic acid fragments. Each nanoantenna exhibits substantial electromagnetic field enhancements with sufficiently localized fields to ensure isolation from neighboring resonators, enabling dense biosensor integration. We quantitatively detect complementary target sequences using DNA hybridization simultaneously for arrays of sensing elements patterned at densities of 160,000 pixels per cm$^2$. In physiological buffer, our nanoantennas exhibit average resonant quality factors of 2,200, allowing detection of two gene fragments, SARS-CoV-2 envelope (E) and open reading frame 1b (ORF1b), down to femtomolar concentrations. We also demonstrate high specificity sensing in clinical nasopharyngeal eluates within 5 minutes of sample introduction. Combined with advances in biomarker isolation from complex samples (e.g., mucus, blood, wastewater), our work provides a foundation for rapid, compact, amplification-free and high throughput multiplexed genetic screening assays spanning medical diagnostics to environmental monitoring.

Optical character recognition (OCR) is a process of converting analogue documents into digital using document images. Currently, many commercial and non-commercial OCR systems exist for both handwritten and printed copies for different languages. Despite this, very few works are available in case of recognising Bengali words. Among them, most of the works focused on OCR of printed Bengali characters. This paper introduces an end-to-end OCR system for Bengali language. The proposed architecture implements an end to end strategy that recognises handwritten Bengali words from handwritten word images. We experiment with popular convolutional neural network (CNN) architectures, including DenseNet, Xception, NASNet, and MobileNet to build the OCR architecture. Further, we experiment with two different recurrent neural networks (RNN) methods, LSTM and GRU. We evaluate the proposed architecture using BanglaWritting dataset, which is a peer-reviewed Bengali handwritten image dataset. The proposed method achieves 0.091 character error rate and 0.273 word error rate performed using DenseNet121 model with GRU recurrent layer., Comment: Accepted in "The 4th National Computing Colleges Conference"