Integrated Mode-Hop-Free Tunable Lasers at 780 nm for Chip-Scale Classical and Quantum Photonic Applications

Posted by UCSB

Kavli Affiliate: John E. Bowers | First 5 Authors: Joshua E. Castro, Eber Nolasco-Martinez, Paolo Pintus, Zeyu Zhang, Boqiang Shen | Summary: In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. […]


Continue.. Integrated Mode-Hop-Free Tunable Lasers at 780 nm for Chip-Scale Classical and Quantum Photonic Applications

Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Bo Gu | First 5 Authors: Ziqi Lin, Xu Zhang, Shimin Gong, Lanhua Li, Zhou Su | Summary: Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access control, which […]


Continue.. Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Zheng Zhu | First 5 Authors: Yuchen Liu, Zheng Zhu, , , | Summary: We propose the realization of interaction-driven insulators in periodically strained monolayer graphene and transition metal dichalcogenides (TMDs). By analyzing the tunable band structure and band geometry via strain, and performing extensive many-body exact diagonalization of a realistic model, we […]


Continue.. Engineering Fractional Chern Insulators through Periodic Strain in Monolayer Graphene and Transition Metal Dichalcogenides

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Yi Zhou | First 5 Authors: Shuli Jiang, Swanand Ravindra Kadhe, Yi Zhou, Farhan Ahmed, Ling Cai | Summary: The increasing use of large language models (LLMs) trained by third parties raises significant security concerns. In particular, malicious actors can introduce backdoors through poisoning attacks to generate undesirable outputs. While such attacks have […]


Continue.. Turning Generative Models Degenerate: The Power of Data Poisoning Attacks

Motion and Structure from Event-based Normal Flow

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Yi Zhou | First 5 Authors: Zhongyang Ren, Bangyan Liao, Delei Kong, Jinghang Li, Peidong Liu | Summary: Recovering the camera motion and scene geometry from visual data is a fundamental problem in the field of computer vision. Its success in standard vision is attributed to the maturity of feature extraction, data association […]


Continue.. Motion and Structure from Event-based Normal Flow

OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Zheng Zhu | First 5 Authors: Zijian Zhou, Zheng Zhu, Holger Caesar, Miaojing Shi, | Summary: Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their relations, enabling the structured understanding of an image. Previous methods focus on predicting predefined object and relation categories, hence limiting their applications in the open […]


Continue.. OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models

Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates

Posted by dbos This Week: Theoretical Physics, UCAS

Kavli Affiliate: Gang Su | First 5 Authors: Gang Su, Sun Yang, Zhishuai Li, , | Summary: The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is challenging due to complex non-linear process dynamics originating from […]


Continue.. Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates

An infrared census of R Coronae Borealis Stars II — Spectroscopic classifications and implications for the rate of low-mass white dwarf mergers

Posted by dbos UCSB

Kavli Affiliate: Lars Bildsten | First 5 Authors: Viraj R. Karambelkar, Mansi M. Kasliwal, Patrick Tisserand, Shreya Anand, Michael C. B. Ashley | Summary: We present results from a systematic infrared (IR) census of R Coronae Borealis (RCB) stars in the Milky Way, using data from the Palomar Gattini IR (PGIR) survey. R Coronae Borealis […]


Continue.. An infrared census of R Coronae Borealis Stars II — Spectroscopic classifications and implications for the rate of low-mass white dwarf mergers

Information dynamics in decohered quantum memory with repeated syndrome measurements: a dual approach

Posted by dbos UCSB

Kavli Affiliate: Matthew P. A. Fisher | First 5 Authors: Jacob Hauser, Yimu Bao, Shengqi Sang, Ali Lavasani, Utkarsh Agrawal | Summary: Measurements can detect errors in a decohered quantum memory allowing active error correction to increase the memory time. Previous understanding of this mechanism has focused on evaluating the performance of error correction algorithms […]


Continue.. Information dynamics in decohered quantum memory with repeated syndrome measurements: a dual approach

Event-Aided Time-to-Collision Estimation for Autonomous Driving

Posted by dbos UCAS

Kavli Affiliate: Yi Zhou | First 5 Authors: Jinghang Li, Bangyan Liao, Xiuyuan LU, Peidong Liu, Shaojie Shen | Summary: Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard […]


Continue.. Event-Aided Time-to-Collision Estimation for Autonomous Driving