Search

This work introduces a pair of novel universal MJ spintronic models that precisely mirror the complex spin textures observed in spintronic materials. The spin-orbit coupled (SOC) Hamiltonians H_MJ1 and H_MJ2 reveal a range of novel and intriguing spin phenomena by modulating spin-orbit interactions. The Hamiltonian H_MJ1 reshapes the existing paradigm, providing a more robust and versatile framework than the Hamiltonian H_RD, with the potential to catalyze new advancements in the study of quantum materials. H_MJ1 encapsulates two distinct spin textures: a unidirectional, momentum-independent persistent spin texture (PST), and a bidirectional (partial) PST. In contrast Hamiltonian H_MJ2 portrays a spiral spin texture, drawing a conceptual link to the cosmological process of expansion and contraction, mirrored within a two-dimensional quantum framework. We also explore the fundamental aspects of earlier analytical models that underpin the construction of the present MJ spintronic model. The physical interpretations of these models are illustrated graphically, and the emerging spin phenomena resulting from complex SOC are elucidated using a simple vector model.

We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena, Mamba, SimA, Conv2Former, and Castling-ViT, can be viewed as specific instances of our PADRe framework. PADRe leverages polynomial functions and draws upon established results from approximation theory, enhancing computational efficiency without compromising accuracy. PADRe's key components include multiplicative nonlinearities, which we implement using straightforward, hardware-friendly operations such as Hadamard products, incurring only linear computational and memory costs. PADRe further avoids the need for using complex functions such as Softmax, yet it maintains comparable or superior accuracy compared to traditional self-attention. We assess the effectiveness of PADRe as a drop-in replacement for self-attention across diverse computer vision tasks. These tasks include image classification, image-based 2D object detection, and 3D point cloud object detection. Empirical results demonstrate that PADRe runs significantly faster than the conventional self-attention (11x ~ 43x faster on server GPU and mobile NPU) while maintaining similar accuracy when substituting self-attention in the transformer models.

The proper planning of different types of public transportation such as metro, highway, waterways, and so on, can increase the efficiency, reduce the congestion and improve the safety of the country. There are certain challenges associated with route planning, such as high cost of implementation, need for adequate resource & infrastructure and resistance to change. The goal of this research is to examine the working, applications, complexity factors, advantages & disadvantages of Floyd- Warshall, Bellman-Ford, Johnson, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), & Grey Wolf Optimizer (GWO), to find the best choice for the above application. In this paper, comparative analysis of above-mentioned algorithms is presented. The Floyd-Warshall method and ACO algorithm are chosen based on the comparisons. Also, a combination of modified Floyd-Warshall with ACO algorithm is proposed. The proposed algorithm showed better results with less time complexity, when applied on randomly structured points within a boundary called quasi-structured points. In addition, this paper also discusses the future works of integrating Floyd-Warshall with ACO to develop a real-time model for overcoming above mentioned-challenges during transportation route planning., Comment: 10 pages, 8 figures

This study introduces an innovative methodology for the planning of metro network routes within the urban environment of Chennai, Tamil Nadu, India. A comparative analysis of the modified Ant Colony Optimization (ACO) method (previously developed) with recent breakthroughs in nature-inspired algorithms demonstrates the modified ACO's superiority over modern techniques. By utilizing the modified ACO algorithm, the most efficient routes connecting the origin and destination of the metro route are generated. Additionally, the model is applied to the existing metro network to highlight variations between the model's results and the current network. The Google Maps platform, integrated with Python, handles real-time data, including land utilization, Geographical Information Systems (GIS) data, census information, and points of interest. This processing enables the identification of stops within the city and along the chosen routes. The resulting metro network showcases substantial benefits compared to conventional route planning methods, with noteworthy enhancements in workforce productivity, decreased planning time, and cost-efficiency. This study significantly enhances the efficiency of urban transport systems, specifically in rapidly changing metropolitan settings such as chennai., Comment: 10 pages, 17 figures

Visual perception tasks are predominantly solved by Vision Transformer (ViT) architectures, which, despite their effectiveness, encounter a computational bottleneck due to the quadratic complexity of computing self-attention. This inefficiency is largely due to the self-attention heads capturing redundant token interactions, reflecting inherent redundancy within visual data. Many works have aimed to reduce the computational complexity of self-attention in ViTs, leading to the development of efficient and sparse transformer architectures. In this paper, viewing through the efficiency lens, we realized that introducing any sparse self-attention strategy in ViTs can keep the computational overhead low. However, these strategies are sub-optimal as they often fail to capture fine-grained visual details. This observation leads us to propose a general, efficient, sparse architecture, named Fibottention, for approximating self-attention with superlinear complexity that is built upon Fibonacci sequences. The key strategies in Fibottention include: it excludes proximate tokens to reduce redundancy, employs structured sparsity by design to decrease computational demands, and incorporates inception-like diversity across attention heads. This diversity ensures the capture of complementary information through non-overlapping token interactions, optimizing both performance and resource utilization in ViTs for visual representation learning. We embed our Fibottention mechanism into multiple state-of-the-art transformer architectures dedicated to visual tasks. Leveraging only 2-6% of the elements in the self-attention heads, Fibottention in conjunction with ViT and its variants, consistently achieves significant performance boosts compared to standard ViTs in nine datasets across three domains $\unicode{x2013}$ image classification, video understanding, and robot learning tasks., Comment: The code is publicly available at https://github.com/Charlotte-CharMLab/Fibottention

Large Language Vision Models (LLVMs) have demonstrated effectiveness in processing internet videos, yet they struggle with the visually perplexing dynamics present in Activities of Daily Living (ADL) due to limited pertinent datasets and models tailored to relevant cues. To this end, we propose a framework for curating ADL multiview datasets to fine-tune LLVMs, resulting in the creation of ADL-X, comprising 100K RGB video-instruction pairs, language descriptions, 3D skeletons, and action-conditioned object trajectories. We introduce LLAVIDAL, an LLVM capable of incorporating 3D poses and relevant object trajectories to understand the intricate spatiotemporal relationships within ADLs. Furthermore, we present a novel benchmark, ADLMCQ, for quantifying LLVM effectiveness in ADL scenarios. When trained on ADL-X, LLAVIDAL consistently achieves state-of-the-art performance across all ADL evaluation metrics. Qualitative analysis reveals LLAVIDAL's temporal reasoning capabilities in understanding ADL. The link to the dataset is provided at: https://adl-x.github.io/

In this paper, we propose a novel token selective attention approach, ToSA, which can identify tokens that need to be attended as well as those that can skip a transformer layer. More specifically, a token selector parses the current attention maps and predicts the attention maps for the next layer, which are then used to select the important tokens that should participate in the attention operation. The remaining tokens simply bypass the next layer and are concatenated with the attended ones to re-form a complete set of tokens. In this way, we reduce the quadratic computation and memory costs as fewer tokens participate in self-attention while maintaining the features for all the image patches throughout the network, which allows it to be used for dense prediction tasks. Our experiments show that by applying ToSA, we can significantly reduce computation costs while maintaining accuracy on the ImageNet classification benchmark. Furthermore, we evaluate on the dense prediction task of monocular depth estimation on NYU Depth V2, and show that we can achieve similar depth prediction accuracy using a considerably lighter backbone with ToSA., Comment: Accepted at CVPRW 2024

This research introduces an innovative forest monitoring system designed to detect and mitigate the threats of forest fires. The proposed system leverages Arduino-based technology integrated with state-of-the-art sensors, including DHT11 for temperature and humidity detection and Flame sensor along with GSM module for gas and smoke detection. The integration of these sensors enables real-time data acquisition and analysis, providing a comprehensive and accurate assessment of environmental conditions within the forest ecosystem. The Arduino platform serves as the central processing unit, orchestrating the communication and synchronization of the sensor data. The DHT11 sensor monitors ambient temperature and humidity levels, crucial indicators for assessing fire risk and identifying potential deforestation activities. Simultaneously, the Flame sensor module detects the occurrence of fire flames nearby thus indicating it by a buzzer. The collected data is processed through an intelligent algorithm that employs machine learning techniques to discern patterns indicative of potential threats. The system is equipped with an adaptive threshold mechanism, allowing it to dynamically adjust to changing environmental conditions. In the event of abnormal readings or anomalies, the system triggers immediate alerts, notifying forest rangers and relevant authorities to facilitate timely response and intervention. The integration of low-cost, easily deployable Arduino-based devices makes this solution scalable and accessible for implementation across diverse forest environments. The proposed system represents a significant step towards leveraging technology to address environmental challenges and protect our forests.

Our investigation explores the intricate domain of thermoelectric phenomena within silver (Ag)-infused chalcopyrites, focusing on compositions such as AgXTe$_2$ (where X=Ga, In) and the complex quaternary system Ag$_2$ZnSn/GeY$_2$ (with Y=S, Se). Using a sophisticated combination of methodologies, we integrate a non-empirical screened dielectric-dependent hybrid (DDH) functional with semiclassical Boltzmann transport theory. This approach allows us to conduct a detailed analysis of critical thermoelectric properties, including electrical conductivity, Seebeck coefficient, and power factor. Our methodology goes beyond superficial assessments, delving into the intricate interplay of material properties to reveal their true thermoelectric potential. Additionally, we investigate the often-overlooked phenomena of phonon scattering by leveraging both the elastic constant tensor and the deformation potential method. This enables a rigorous examination of electron relaxation time and lattice thermal conductivity, enhancing the robustness of our predictions and demonstrating our commitment to thorough exploration.Through our rigorous investigation, we identify materials with a thermoelectric figure of merit (ZT = $\sigma S^{2}T/ \kappa$) exceeding the critical threshold of unity. This significant achievement signals the discovery of materials capable of revolutionizing efficient thermoelectric systems. Our findings delineate a promising trajectory, laying the groundwork for the emergence of a new class of Ag-based chalcopyrites distinguished by their exceptional thermoelectric characteristics. This research not only contributes to the understanding of materials science principles but also catalyzes transformative advancements in thermoelectric technology.

In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specifically, we propose a future prediction network, F-Net, which takes the features of multiple consecutive frames and is trained to predict multi-frame features one time step ahead iteratively. In this way, F-Net learns the underlying motion and correspondence information, and we incorporate its features into the depth decoding process. Additionally, to enrich the learning of multiframe correspondence cues, we further leverage a reconstruction network, R-Net, which is trained via adaptively masked auto-encoding of multiframe feature volumes. At inference time, both F-Net and R-Net are used to produce queries to work with the depth decoder, as well as a final refinement network. Through extensive experiments on several benchmarks, i.e., NYUDv2, KITTI, DDAD, and Sintel, which cover indoor, driving, and open-domain scenarios, we show that FutureDepth significantly improves upon baseline models, outperforms existing video depth estimation methods, and sets new state-of-the-art (SOTA) accuracy. Furthermore, FutureDepth is more efficient than existing SOTA video depth estimation models and has similar latencies when comparing to monocular models

In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically, we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest, complex transformer-based models. Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features. In addition, we propose normalization techniques to process the point cloud, which improves learning and leads to better accuracy than directly using point transformers off the shelf. Furthermore, we incorporate global attention on downsampled point cloud features, which enables long-range context while still being computationally feasible. We evaluate our method, DeCoTR, on established depth completion benchmarks, including NYU Depth V2 and KITTI, showcasing that it sets new state-of-the-art performance. We further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and demonstrate that DeCoTR has superior generalizability compared to existing approaches., Comment: Accepted at CVPR 2024

We investigated the entanglement in a diluted magnetic semiconductor quantum dot, crucial for quantum technologies. Despite their potential, these systems exhibit low extraction rates. We explore self-assembled InGaAs quantum dots, focusing on entanglement between them based on spin states. Our analysis involves defining wavefunctions, employing density matrix operators, and measuring entanglement entropy. Numerical assessments reveal few promising pairs among various quantum dot combinations that exhibit significant entanglement. Additionally, this work discusses theoretical developments and statistical evaluations of entanglement in diluted magnetic semiconductor quantum dots, providing insights into their potential for quantum applications., Comment: 9 pages 4 figures

In the article, we consider a question concerning the estimation of summatory function of the Fourier coefficients of Hecke eigenforms indexed by a sparse set of integers. In particular, we provide an estimate for the following sum; \begin{equation*} \begin{split} S(f, \mathcal{Q}; X ) &:= \sideset{}{^{\flat }}\sum_{n= \mathcal{Q}(\underline{x}) \le X \atop \gcd(n,N) =1 } \lambda_{f}(n), \end{split}\end{equation*} where $\flat$ means that sum runs over the square-free positive integers, $\lambda_{f}(n)$ denotes the normalised $n^{\rm th}$ Fourier coefficients of a Hecke eigenform $f$ of integral weight $k$ for the congruence subgroup $\Gamma_{0}(N)$ and $\mathcal{Q}$ is a primitive integral positive-definite binary quadratic forms of fixed discriminant $D<0$ with the class number $h(D)=1$. As a consequence, we determine the size, in terms of conductor of associated $L$-function, for the first sign change of Hecke eigenvalues indexed by the integers which are represented by $\mathcal{Q}$. This work is an improvement and generalisation of the previous results., Comment: 16 pages

Unlike, momentum-dependent Rashba spin-splitting, materials exhibiting intrinsic momentum-independent unidirectional spin polarization also known as persistent spin texture (PST) in the full Brillouin zone are scarce. In this work, a list of characteristic electronic properties for identifying an ideal PST material is provided based on earlier analytical models, and a new semiconductor, the MgTe(110) facet is proposed which satisfies all these conditions and exhibits PST in the full Brillouin zone. The atomic arrangement in this particular facet exhibits three basic symmetries found in nature: rotation, reflection, and translation. Using the method of invariance, an effective Hamiltonian is constructed which reproduces the results obtained using the density functional theory. Further, mono/few layers of MgTe (110) facets of the zinc-blende structure are proposed for a ferromagnet-free non-ballistic spin-field effect transistor (s-FET) that combines both the spin-Hall effect and inverse spin-Hall effect, thus harmonizing spintronics with conventional electronics. Although only quantum well structures have been experimentally studied for nonballistic s-FET under the stringent condition of equal Rashba and Dresselhaus strength, PST originating intrinsically in the proposed 2D structures makes them an ideal alternate.

With their shielded 4f orbitals, rare-earth ions (REIs) offer optical and electron spin transitions with good coherence properties even when embedded in a host crystal matrix, highlighting their utility as promising quantum emitters and memories for quantum information processing. Among REIs, trivalent erbium (Er$^{3+}$) uniquely has an optical transition in the telecom C-band, ideal for transmission over optical fibers, and making it well-suited for applications in quantum communication. The deployment of Er$^{3+}$ emitters into a thin film TiO$_2$ platform has been a promising step towards scalable integration; however, like many solid-state systems, the deterministic spatial placement of quantum emitters remains an open challenge. We investigate laser annealing as a means to locally tune the optical resonance of Er$^{3+}$ emitters in TiO$_2$ thin films on Si. Using both nanoscale X-ray diffraction measurements and cryogenic photoluminescence spectroscopy, we show that tightly focused below-gap laser annealing can induce anatase to rutile phase transitions in a nearly diffraction-limited area of the films and improve local crystallinity through grain growth. As a percentage of the Er:TiO$_2$ is converted to rutile, the Er$^{3+}$ optical transition blueshifts by 13 nm. We explore the effects of changing laser annealing time and show that the amount of optically active Er:rutile increases linearly with laser power. We additionally demonstrate local phase conversion on microfabricated Si structures, which holds significance for quantum photonics., Comment: 7 pages, 4 figures