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Statistics of natural scenes are not uniform—their structure varies dramatically from ground to sky. It remains unknown whether these nonuniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. Using the mouse ( Mus musculus ) as a model species, we show that receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon, in agreement with our predictions. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell types. The statistics of natural scenes are not uniform—their structure varies dramatically from ground to sky. A combination of theory and experiments revealed that the visual system has adapted to these inhomogeneities to increase coding efficiency.

Unidirectional cable-drive actuators are highly effective for applications requiring high force transmission with minimal back-driving resistance. However, motor limitations, particularly rotor inertia, often hinder precise force tracking across broader ranges. For instance, high torque motors exhibit reduced sensitivity in low-torque ranges, complicating low-force control. This thesis introduces a novel actuator with non-linear series elasticity to address this challenge. The series elastic element transitions from a soft-spring enabling low-force sensitivity to a stiffer spring for high-force output, ultimately disengaging to function as a traditional stiff cable-drive actuator at large forces. This design features a mechanically programmable stiffness using CAM, enabling precise and versatile force control for wide applications with force-sensitive systems.

Aquaponic system in greenhouses which can recycle and reuse the water and nutrients is gaining importance across the world to counter the uncertainties due to weather fluctuations. However, there is a slow pace of growth in aquaculture practices around the globe in general and India in particular. There are many barriers to adopt the aquaponic culture. In this study an analysis of the barriers for aquaponics culture in Indian context during the COVID-19 period is presented. Literature review and interactions with various stakeholders help to find out the list of potential factors while gauging the success of their prospective aquaponics project. The “best-worst” methodology (BWM) is employed for ranking of barriers, whereas categorizing of barriers is carried out with the help of fuzzy DEMATEL. Furthermore, the results of this research work are of great value to corporations or start-up companies looking to invest in this technology as well as to farmers who wish to adopt this farming technique.

Pose estimation is an important and challenging task in computer vision. Hand pose estimation has drawn increasing attention during the past decade and has been utilized in a wide range of applications including augmented reality, virtual reality, human-computer interaction, and action recognition. Hand pose is more challenging than general human body pose estimation due to the large number of degrees of freedom and the frequent occlusions of joints. To address these challenges, we propose HandyPose, a single-pass, end-to-end trainable architecture for hand pose estimation. Adopting an encoder-decoder framework with multi-level features, our method achieves high accuracy in hand pose while maintaining manageable size complexity and modularity of the network. HandyPose takes a multi-scale approach to representing context by incorporating spatial information at various levels of the network to mitigate the loss of resolution due to pooling. Our advanced multi-level waterfall architecture leverages the efficiency of progressive cascade filtering while maintaining larger fields-of-view through the concatenation of multi-level features from different levels of the network in the waterfall module. The decoder incorporates both the waterfall and multi-scale features for the generation of accurate joint heatmaps in a single stage. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We also propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach thatachieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. It incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network. Our HandyPose architecture has a baseline of vehipose with an improvement in performance by incorporating multi-level features from different levels of the backbone and introducing novel multi-level modules. HandyPose and VehiPose more thoroughly leverage the image contextual information and deal with the issue of spatial loss of resolution due to successive pooling while maintaining the size complexity, modularity of the network, and preserve the spatial information at various levels of the network. Our results demonstrate state-of-the-art performance on popular datasets and show that HandyPose and VehiPose are robust and efficient architectures for hand and vehicle pose estimation.

Visual systems have adapted to the structure of natural stimuli. In the retina, center-surround receptive fields (RFs) of retinal ganglion cells (RGCs) appear to efficiently encode natural sensory signals. Conventionally, it has been assumed that natural scenes are isotropic and homogeneous; thus, the RF properties are expected to be uniform across the visual field. However, natural scene statistics such as luminance and contrast are not uniform and vary significantly across elevation. Here, by combining theory and novel experimental approaches, we demonstrate that this inhomogeneity is exploited by RGC RFs across the entire retina to increase the coding efficiency. We formulated three predictions derived from the efficient coding theory: (i) optimal RFs should strengthen their surround from the dimmer ground to the brighter sky, (ii) RFs should simultaneously decrease their center size and (iii) RFs centered at the horizon should have a marked surround asymmetry due to a stark contrast drop-off. To test these predictions, we developed a new method to image high-resolution RFs of thousands of RGCs in individual retinas. We found that the RF properties match theoretical predictions, and consistently change their shape from dorsal to the ventral retina, with a distinct shift in the RF surround at the horizon. These effects are observed across RGC subtypes, which were thought to represent visual space homogeneously, indicating that functional retinal streams share common adaptations to visual scenes. Our work shows that RFs of mouse RGCs exploit the non-uniform, panoramic structure of natural scenes at a previously unappreciated scale, to increase coding efficiency. Competing Interest Statement The authors have declared no competing interest.

Our current knowledge of scholarly plagiarism is largely based on the similarity between full text research articles. In this paper, we propose an innovative and novel conceptualization of scholarly plagiarism in the form of reuse of explicit citation sentences in scientific research articles. Note that while full-text plagiarism is an indicator of a gross-level behavior, copying of citation sentences is a more nuanced micro-scale phenomenon observed even for well-known researchers. The current work poses several interesting questions and attempts to answer them by empirically investigating a large bibliographic text dataset from computer science containing millions of lines of citation sentences. In particular, we report evidences of massive copying behavior. We also present several striking real examples throughout the paper to showcase widespread adoption of this undesirable practice. In contrast to the popular perception, we find that copying tendency increases as an author matures. The copying behavior is reported to exist in all fields of computer science; however, the theoretical fields indicate more copying than the applied fields.

With the advent of several tools and web engines trained for finding journal articles out of billions of research papers on millions of topics in different databases with a high degree of generalizability, it often leads to a loss of specificity. Scientific pursuits need a tool to extract data from selected resources for performing domain-specific tasks. Current algorithms and generalized tools lack specificity and are challenged by errors in analysing data from a bundle of specific documents selected eclectically. Current work addresses the need for such a tool, which focuses on specificity based on users' input keywords and phrases to find relevant information from bundles of articles from the web. Reseractor is based on a customized algorithm, Whitespace, in synergy with output from open-access tools for document image analysis and focused domain data extraction using NLP. The current tool is designed for the material science domain with the features of adopting various generalized and scientific corpora as layers. It is tested on two sets of different bundles of papers and gives an accuracy of 81.12% along with a recall of 78.38% and a precision of 84.06%. Owing to the simple and direct applicability of algorithms, users from other domains can directly use their corpora in algorithms and remodel the tool for their purpose. Current work fulfills the need for domain-specific experimental data extraction stored in organized and structured databases for upcoming computational researchers.

Importance The exceedingly high US spending per capita on prescription medications is mediated, at least in part, by the inefficiencies of existing generic pharmaceutical distribution and reimbursement systems; yet, the extent of potential savings and areas for targeted interventions for generic drug prescribers remains underexplored. Objective We aimed to analyze 2021 Medicare Part D spending on generic drugs in comparison with pricing of a low-cost generic drug program, the Mark Cuban Cost Plus Drug Company (MCCPDC), to gauge the extent of achievable potential savings. Design, Setting, and Participants In this retrospective, observational study, we performed a systematic analysis of potential Medicare Part D savings when using MCCPDC generic pricing. The 2023 MCCPDC data, as of August 2023, were obtained from the provider’s publicly available database. The 2021 Medicare Part D data and prescriber datasets were obtained from the US Centers for Medicare and Medicaid Services. Main Outcomes and Measures Outcomes included total prescription volume, proportion of drugs with savings, total US dollar Medicare savings, and average weighted price reduction per unit drug. Results were stratified by medical and surgical subspecialties to identify areas for targeted interventions. Subspecialty-wise contribution to total savings versus contribution to total prescription volume was characterized. Results Total estimated Medicare Part D savings were $8.6 billion using 90-day MCCPDC pricing, with surgical drugs accounting for over $900 million. Nearly 80% of the examined drugs were more price effective through MCCPDC using 90-day supply. Commonly prescribed drugs in cardiology, psychiatry, neurology, transplant surgery, and urology demonstrated the highest estimated absolute savings. The most disproportionate savings relative to prescription volume were observed for drugs in oncology, gynecology, infectious disease, transplant surgery, and colorectal surgery. Conclusions and Relevance This study underscores the significant potential for Medicare Part D savings through strategies that address the systemic overpayment for generic medications. We identified key areas for reform as well as specific medical and surgical subspecialties where targeted interventions could yield substantial savings.

The organic p-nitrophenol (4NP) bulk crystal was harvested through the slow evaporation solution growth method. The ingot underwent various characterizations to assess its suitability for device fabrication. The structural parameters of the ingot mentioned above were assessed by powder X-ray diffraction (PXRD) using Rietveld refinement. The optical characteristics of the 4NP crystal were investigated using UV–Vis spectroscopy, photoluminescence (PL) and time-resolved photoluminescence (TRPL) techniques. The UV–Vis spectrum analysis of the 4NP crystal reveals a lower cutoff wavelength of 451 nm with a band gap of 3.148 eV. From the transmittance data, various optical constants were computed. The optical conductivity ( σ ) of the 4NP crystal at a wavelength of 568 nm was found to be 1.950 × 10 10 s −1 . The photoluminescence (PL) analysis indicated a significant emission peak at 390 nm which is associated with the violet region of the electromagnetic spectrum, while the average lifetime was determined using time-resolved photoluminescence (TRPL). The thermal stability of the compound was studied by thermogravimetric analysis and examination of differential thermal curves. The TG/DTA analysis shows that the titled material is stable up to 132 °C. The chemical structure of 4NP crystal was optimized and wavefunction analysis was performed using Gaussian09W. The software programs Multiwfn and VMD 9.1 are utilized to visualize “LOL, RDG, ELF and Inter and Intramolecular relationship”. The Hirshfeld analysis was carried out through Crystalexplorer.