Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

PREADD: Prefix-Adaptive Decoding for Controlled Text Generation

We propose Prefix-Adaptive Decoding (PREADD), a flexible method for controlled text generation. Unlike existing methods that use auxiliary expert models to control for attributes, PREADD does not require an external model, instead relying on linearly combining output logits from multiple prompts. Specifically, PREADD contrasts the output logits generated using a raw prompt against those generated using a prefix-prepended prompt, enabling both positive and negative control with respect to any attribute encapsulated by the prefix. We evaluate PREADD on three tasks -- toxic output mitigation, gender bias reduction, and sentiment control -- and find that PREADD outperforms not only prompting baselines, but also an auxiliary-expert control method, by 12% or more in relative gain on our main metrics for each task.Comment: ACL Findings 202

Superfluid and magnetic states of an ultracold Bose gas with synthetic three-dimensional spin-orbit coupling in an optical lattice

We study ultracold bosonic atoms with the synthetic three-dimensional spin-orbit (SO) coupling in a cubic optical lattice. In the superfluidity phase, the lowest energy band exhibits one, two or four pairs of degenerate single-particle ground states depending on the SO-coupling strengths, which can give rise to the condensate states with spin-stripes for the weak atomic interactions. In the deep Mott-insulator regime, the effective spin Hamiltonian of the system combines three-dimensional Heisenberg exchange interactions, anisotropy interactions and Dzyaloshinskii-Moriya interactions. Based on Monte Carlo simulations, we numerically demonstrate that the resulting Hamiltonian with an additional Zeeman field has a rich phase diagram with spiral, stripe, vortex crystal, and especially Skyrmion crystal spin-textures in each xy-plane layer. The obtained Skyrmion crystals can be tunable with square and hexagonal symmetries in a columnar manner along the z axis, and moreover are stable against the inter-layer spin-spin interactions in a large parameter region.Comment: 9 pages, 4 figures; title modified, references and discussions added; accepted by PR

Tunable Frequency Comb Generation from a Microring with a Thermal Heater

We demonstrate a novel comb tuning method for microresonator-based Kerr comb generators. Continuously tunable, low-noise, and coherent comb generation is achieved in a CMOS-compatible silicon nitride microring resonator.Comment: submitted to CLEO201

TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown promise. However, implicit cross-task consistency learning in multi-task networks is limited. To address this, we propose a novel "decoupled cross-task consistency loss" that explicitly enhances cross-task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88\% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4\% mIoU/46.2 FPS on Cityscapes, showcasing real-time inference capabilities at full resolution. With multi-scale inference, performance is further enhanced to 77.8\%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multi-task learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation.Comment: Accepted for publication in the journal "International Journal of Intelligent Systems