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Fang Tang

Publications and source records attributed to Fang Tang.

7 recordsLinked to original sources
arXiv2024

Strategizing Equitable Transit Evacuations: A Data-Driven Reinforcement Learning Approach

As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process solved by reinforcement learning, using real-time transit data from General Transit Feed Specification and transportation networks extracted from OpenStreetMap. The reinforcement learning agent dynamically reroutes buses from their scheduled location to minimize total passengers' evacuation time while prioritizing equity-priority communities. Simulations on the San Francisco Bay Area transportation network indicate that the proposed framework achieves significant improvements in both evacuation efficiency and equitable service distribution compared to traditional rule-based and random strategies. These results highlight the potential of reinforcement learning to enhance system performance and urban resilience during emergency evacuations, offering a scalable solution for real-world applications in intelligent transportation systems.

cs.LG↗
arXiv2024

Room temperature quantum metric effect in TbMn6Sn6

Quantum geometry, including Berry curvature and the quantum metric, of the electronic Bloch bands has been studied via nonlinear responses in topological materials. Naturally, these material systems with intrinsic strong nonlinear responses also form the key component in nonlinear electronic devices. However, the previous reported quantum geometry effects are mainly observed at cryogenic temperatures, hindering their application in practical devices. Here we report the tuneable strong room-temperature second-harmonic transport response in a quantum magnet, TbMn6Sn6, which is governed by the quantum metric and can be tuned with applied magnetic fields. We show that around room temperature, which is close to the spontaneous spin-reorientation transition, the magnetic configurations, and therefore the related symmetry breaking phases, are easily controlled via magnetic fields. Our results also show that manipulation of the symmetries of the magnetic structure presents an effective route to tuneable quantum-geometry-based devices.

cond-mat.mtrl-sci↗
arXiv2023

Coexistence of Logarithmic and SdH Quantum Oscillations in Ferromagnetic Cr-doped Tellurium Single Crystals

We report the synthesis of transition-metal-doped ferromagnetic elemental single-crystal semiconductors with quantum oscillations using the physical vapor transport method. The 7.7 atom% Cr-doped Te crystals (Cr_Te) show ferromagnetism, butterfly-like negative magnetoresistance in the low temperature (< 3.8 K) and low field (< 0.15 T) region, and high Hall mobility, e.g., 1320 cm2 V-1 s-1 at 30 K and 350 cm2 V-1 s-1 at 300 K, implying that Cr_Te crystals are ferromagnetic elemental semiconductors. When B // c // I, the maximum negative MR is -27% at T = 20 K and B = 8 T. In the low temperature semiconducting region, Cr_Te crystals show strong discrete scale invariance dominated logarithmic quantum oscillations when the direction of the magnetic field B is parallel to the [100] crystallographic direction and show Landau quantization dominated Shubnikov-de Haas (SdH) oscillations for B // [210] direction, which suggests the broken rotation symmetry of the Fermi pockets in the Cr_Te crystals. The findings of coexistence of multiple quantum oscillations and ferromagnetism in such an elemental quantum material may inspire more study of narrow bandgap semiconductors with ferromagnetism and quantum phenomena.

cond-mat.mtrl-sci↗
arXiv2021

Topological quantum phase transition in the magnetic semimetal HoSb

Magnetic topological semimetals, a novel state of quantum matter with nontrivial band topology, have emerged as a new frontier in physics and materials science. An external stimulus like temperature or magnetic field could be expected to alter their spin states and thus the Fermi surface anisotropies and topological features. Here, we perform the angular magnetoresistance measurements and electronic band structure calculations to reveal the evolution of HoSb's Fermi surface anisotropies and topological nature in different magnetic states. The angular magnetoresistance results manifest that its Fermi surface anisotropy is robust in the paramagnetic state but is significantly modulated in the antiferromagnetic and ferromagnetic states. More interestingly, a transition from the trivial (nontrivial) to nontrivial (trivial) topological electronic phase is observed when HoSb undergoes a magnetic transition from the paramagnetic (antiferromagnetic) to antiferromagnetic (ferromagnetic) state induced by temperature (applied magnetic field). Our studying suggests that HoSb provides an archetype platform to study the correlations between magnetism and topological states of matter.

cond-mat.mtrl-sci↗
arXiv2021

Designation of Intra-layer and Intercalated High Entropy Quasi-2D Compounds

Here, we designed two promising schemes to realize the high-entropy structure in a series of quasi-two-dimensional compounds, transition metal dichalcogenides (TMDCs). In the intra-layer high-entropy plan, (HEM)X2 compounds with high-entropy structure in the MX2 slabs were obtained, here HEM means high-entropy metals, such as TiZrNbMoTa. And superconductivity with a Tc~7.4 K was found in a Mo-rich HEMX2. On the other hand, in the intercalation plan, we intercalated HEM-atoms (FeCoCrNiMn) into the gap between the sandwiched-MX2 slabs resulting in a series of (HEM)xMX2 compounds, x in the range of 0~0.5, in which HEM is mainly composed of 3d transition metal elements, such as FeCoCrNiMn. As the introduction of multi-component magnetic atoms, ferromagnetic spin-glass states with strong 2D characteristics ensued. Tuning the x content, three kinds of two in the high-entropy intercalated layer were observed including the 1*1 triangular lattice and two kinds of superlattices \sqrt3*\sqrt3 and \sqrt3*2 in x=0.333 and x>0.5, respectively. Meanwhile, the spin frustration in the two-dimensional high-entropy magnetic plane will be enhanced with the development of \sqrt3*\sqrt3 and will be reduced significantly when changing into the \sqrt3*2 phase. The high-entropy TMDCs and versatile two-dimensional high-entropy structures found by us possess great potentials to find new physics in low-dimensional high-entropy structures and future applications.

cond-mat.mtrl-sci↗
arXiv2016

Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections

Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data. However, implementing a deep neural network in embedded systems is a challenging task, because a typical deep neural network, such as a Deep Belief Network using 128x128 images as input, could exhaust Giga bytes of memory and result in bandwidth and computing bottleneck. To address this challenge, this paper presents a hardware-oriented deep learning algorithm, named as the Deep Adaptive Network, which attempts to exploit the sparsity in the neural connections. The proposed method adaptively reduces the weights associated with negligible features to zero, leading to sparse feedforward network architecture. Furthermore, since the small proportion of important weights are significantly larger than zero, they can be robustly thresholded and represented using single-bit integers (-1 and +1), leading to implementations of deep neural networks with sparse and binary connections. Our experiments showed that, for the application of recognizing MNIST handwritten digits, the features extracted by a two-layer Deep Adaptive Network with about 25% reserved important connections achieved 97.2% classification accuracy, which was almost the same with the standard Deep Belief Network (97.3%). Furthermore, for efficient hardware implementations, the sparse-and-binary-weighted deep neural network could save about 99.3% memory and 99.9% computation units without significant loss of classification accuracy for pattern recognition applications.

cs.LG↗