Abstract

Quantum states least affected by interactions with environment play a pivotal role in both foundations and applications of quantum mechanics. Known as pointer states, they surprisingly lacked a systematic description. Working within the Born-Markov approximation, we combine methods of group theory and open quantum systems and derive general conditions describing pointer states. Contrary to common expectations, they are in general different from coherent states. Thus the two notions of being “closest to the classical”—one defined by the uncertainty relations and the other by the interaction with the environment—are in general different. As an example, we study spin-spin and spin-boson models with an arbitrary central spin J.

Abstract

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski’s rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Abstract

Living organisms are required to sense the environment accurately in order to ensure appropriate responses. The accuracy of estimating the environmental input is severely limited by noise stemming from inherent stochasticity of the chemical reactions involved in signaling pathways. Cells employ multiple strategies to improve the accuracy by tuning the reaction rates, for instance amplifying the response, reducing the noise etc.. However, the pathway also consumes energy through incorporating ATP in phosphorylating key signaling proteins involved in the reaction pathways. In many instances, improvements in accuracy elicit extra energetic cost. For example, higher deactivation rate suppresses the basal pathway activity effectively amplifying the dynamic range of the response which leads to improvement in accuracy. Higher deactivation rate also enhances the energy dissipation rate. Here, we employed a theoretical approach based on thermodynamics of information to explore the role of accuracy and energetic cost in the performance of a Mitogen Activated Protein Kinase signaling system. Our study shows that the accuracy-energy trade-off can explain the optimality of the reaction rates of the reaction pathways rather than accuracy alone. Our analysis elucidates the role of interplay between accuracy and energetic cost in evolutionary shaping of the parameter space of signaling pathways.

Abstract

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Abstract

This research paper presents a novel low computational approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed, and then employs a tuned PID controller to guide the UAV through and out of the passage while avoiding collisions with the walls. To evaluate the effectiveness of the proposed approach, a series of experiments were conducted using a micro-UAV navigating in and out of a custom-built test environment (constrained rectangular box). The results demonstrate that the system is able to successfully guide the UAV through the passages while avoiding collisions with the walls

Abstract

There is an emerging consensus that the future of intelligent and energy efficient homes is automation and cloud control. This paper presents a novel infrastructural concept using smart ceiling tiles and an energy-harvesting Bluetooth Low Energy (BLE) switch to control home appliances. The switch location is determined using trilateration of the signals received by the tiles installed in different rooms. In this paper, we localized the switch among three rooms separated by a brick wall by analyzing the received signal strength indicator (RSSI) values captured by three tiles located in three rooms. The tiles are powered with DC power using the support rails, thus minimizing wiring for power delivery. We used different machine learning classification algorithms with the received RSSI values as features for predicting the location of the switch accurately. Through experimentation, we achieved high accuracy of 93% in classifying the switch position among the rooms. The integration of smart tiles in the false ceiling along with the use of an energy-harvesting BLE switch offers numerous advantages, such as reducing wiring complexity, enhancing user convenience, and aesthetic design.

Abstract

In the aftermath of earthquakes, hospital buildings are expected to remain occupiable to treat the injured. For the purpose, firstly, an appropriate structural system that ensures no or limited structural damage should be provided in such buildings; wall-frames are the recommended structural systems in hospital buildings. Secondly, it is also essential to adequately design wall-frames to help meet the preferred Occupiability seismic performance. The work presented in this paper examines the contributing factors for failure of structural elements in hospital buildings and explores structural configuration and design strategies for mitigating these failures. Nonlinear analyses studies of a typical hospital building in high seismic region in India are carried out to help provide quantitative guidelines for: (a) Structural Plan Density (SPD) of structural walls, and (b) design parameters, for achieving the preferred performance. Displacement-based limit state of structural damage adopted from traditional displacement demand estimation rules is proposed. Lateral stiffness, strength, and ductility are evaluated of study buildings, and efficacy of results obtained from nonlinear static analyses confirmed by performing nonlinear time history analyses in commercial software PERFORM 3D. Results demonstrate inadequacy of moment frame structural system in hospital buildings under strong earthquake shaking, identifies optimum overall SPD of structural walls and relative column-to-beam strength ratio of frame members, required to achieve Occupiability. A minimum 3% SPD of structural walls in Wall – Special Moment Resisting Frame (WSMRF), 4% SPD in Wall – Ordinary moment resisting frame (WOMRF) hospital buildings, and a minimum column-to-beam strength ratio (CBSR) of 2 is required in hospital buildings to attain Occupiability performance.

Abstract

Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes. However, their capabilities lag behind those offered by conventional representations such as 2D videos because of algorithmic challenges and the lack of large-scale multi-view real-world datasets. We address the dataset limitation with DiVa-360, a real-world 360◦ dynamic visual dataset that contains synchronized high-resolution and long-duration multi-view video sequences of table-scale scenes captured using a customized low-cost system with 53 cameras. It contains 21 object-centric sequences categorized by different motion types, 25 intricate hand-object interaction sequences, and 8 long-duration sequences for a total of 17.4 M image frames. In addition, we provide foreground-background segmentation masks, synchronized audio, and text descriptions. We benchmark the state-of-the-art dynamic neural field methods on DiVa-360 and provide insights about existing methods and future challenges on long-duration neural field capture

Abstract

Understanding how we grasp objects with our hands has important applications in areas like robotics and mixed re ality. However, this challenging problem requires accurate modeling of the contact between hands and objects. To capture grasps, existing methods use skeletons, meshes, or parametric models that does not represent hand shape accu rately resulting in inaccurate contacts. We present MANUS, amethodforMarkerlessHand-ObjectGraspCaptureusing Articulated 3D Gaussians. We build a novel articulated 3D Gaussians representation that extends 3D Gaussian splatting [33] for high-fidelity representation of articulat ing hands. Since our representation uses Gaussian primi tives optimized from the multi-view pixel-aligned losses, it enables us to efficiently and accurately estimate contacts between the hand and the object.

Abstract

The scalar-leptoquark (sLQ) parameter space is well explored experimentally. The direct pair production searches at the LHC have excluded light sLQs almost model agnostically, and the high-pT dilepton tail data have put strong bounds on the leptoquark-quark-lepton Yukawa couplings for a wide range of sLQ masses. However, these do not show the complete picture. Previously, Mandal et al. [Single productions of colored particles at the LHC: An example with scalar leptoquarks, J. High Energy Phys. 07 (2015) 028] showed how the dilepton-dijet data from the pair production searches could give strong limits on these couplings. This was possible by including the single-production contribution to the dilepton-dijet signal. In this paper, we take a fresh look at the LHC limits on all sLQs by following the same principle and combine all significant contributions—from pair and single productions, t-channel sLQ exchange and its interference with the Standard Model background—to the μμjj final state and recast the limits. We notice that the sLQ exchange and its interference with the background processes play significant roles in the limits. The μμjj- recast limits are comparable to or, in some cases, significantly better than the currently known limits (from high-pT dilepton data and direct searches), i.e., the LHC data rules out more parameter space than what is considered in the current literature. For the first time, we also show how including the QED processes can noticeably improve the sLQ mass exclusion limits from the QCD-only limits.