Optimal Distributed Beamforming for MISO Interference Channels

We consider the problem of quantifying the Pareto optimal boundary in the achievable rate region over multiple-input single-output (MISO) interference channels, where the problem boils down to solving a sequence of convex feasibility problems after certain transformations. The feasibility problem is solved by two new distributed optimal beamforming algorithms, where the first one is to parallelize the computation based on the method of alternating projections, and the second one is to localize the computation based on the method of cyclic projections. Convergence proofs are established for both algorithms.Comment: 7 Pages, 6 figures, extended version for the one in Proceeding of Asilomar, CA, 201

Estimation Diversity and Energy Efficiency in Distributed Sensing

Distributed estimation based on measurements from multiple wireless sensors is investigated. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. The observations are transmitted using amplify-and-forward (analog) transmissions over non-ideal fading wireless channels from the sensors to a fusion center, where they are combined to generate an estimate of the observed quantity. Assuming that the Best Linear Unbiased Estimator (BLUE) is used by the fusion center, the equal-power transmission strategy is first discussed, where the system performance is analyzed by introducing the concept of estimation outage and estimation diversity, and it is shown that there is an achievable diversity gain on the order of the number of sensors. The optimal power allocation strategies are then considered for two cases: minimum distortion under power constraints; and minimum power under distortion constraints. In the first case, it is shown that by turning off bad sensors, i.e., sensors with bad channels and bad observation quality, adaptive power gain can be achieved without sacrificing diversity gain. Here, the adaptive power gain is similar to the array gain achieved in Multiple-Input Single-Output (MISO) multi-antenna systems when channel conditions are known to the transmitter. In the second case, the sum power is minimized under zero-outage estimation distortion constraint, and some related energy efficiency issues in sensor networks are discussed.Comment: To appear at IEEE Transactions on Signal Processin

Energy-Efficient Joint Estimation in Sensor Networks: Analog vs. Digital

Sensor networks in which energy is a limited resource so that energy consumption must be minimized for the intended application are considered. In this context, an energy-efficient method for the joint estimation of an unknown analog source under a given distortion constraint is proposed. The approach is purely analog, in which each sensor simply amplifies and forwards the noise-corrupted analog bservation to the fusion center for joint estimation. The total transmission power across all the sensor nodes is minimized while satisfying a distortion requirement on the joint estimate. The energy efficiency of this analog approach is compared with previously proposed digital approaches with and without coding. It is shown in our simulation that the analog approach is more energy-efficient than the digital system without coding, and in some cases outperforms the digital system with optimal coding.Comment: To appear in Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing, Philadelphia, PA, March 19 - 23, 200

A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context

In the open book question answering (OBQA) task, selecting the relevant passages and sentences from distracting information is crucial to reason the answer to a question. HotpotQA dataset is designed to teach and evaluate systems to do both passage ranking and sentence selection. Many existing frameworks use separate models to select relevant passages and sentences respectively. Such systems not only have high complexity in terms of the parameters of models but also fail to take the advantage of training these two tasks together since one task can be beneficial for the other one. In this work, we present a simple yet effective framework to address these limitations by jointly ranking passages and selecting sentences. Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection.The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28\% in terms of exact matching of relevant sentences on the HotpotQA dataset.Comment: Accepted to NAACL SWR 202

LATR: 3D Lane Detection from Monocular Images with Transformer

3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye view) built from front-view image features and camera parameters. However, the depth ambiguity in monocular images inevitably causes misalignment between the constructed surrogate feature map and the original image, posing a great challenge for accurate lane detection. To address the above issue, we present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation. Specifically, LATR detects 3D lanes via cross-attention based on query and key-value pairs, constructed using our lane-aware query generator and dynamic 3D ground positional embedding. On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information. On the other hand, 3D space information is injected as positional embedding from an iteratively-updated 3D ground plane. LATR outperforms previous state-of-the-art methods on both synthetic Apollo, realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms of F1 score on OpenLane). Code will be released at https://github.com/JMoonr/LATR .Comment: Accepted by ICCV2023 (Oral

Designer lipid-like peptides

A crucial bottleneck in membrane protein studies, particularly G-protein coupled receptors, is the notorious difficulty of finding an optimal detergent that can solubilize them and maintain their stability and function. Here we report rapid production of 12 unique mammalian olfactory receptors using short designer lipid-like peptides as detergents. The peptides were able to solubilize and stabilize each receptor. Circular dichroism showed that the purified olfactory receptors had alpha-helical secondary structures. Microscale thermophoresis suggested that the receptors were functional and bound their odorants. Blot intensity measurements indicated that milligram quantities of each olfactory receptor could be produced with at least one peptide detergent. The peptide detergents' capability was comparable to that of the detergent Brij-35. The ability of 10 peptide detergents to functionally solubilize 12 olfactory receptors demonstrates their usefulness as a new class of detergents for olfactory receptors, and possibly other G-protein coupled receptors and membrane proteins

M^2-3DLaneNet: Multi-Modal 3D Lane Detection

Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. In this work, we propose the M^2-3DLaneNet, a Multi-Modal framework for effective 3D lane detection. Aiming at integrating complementary information from multi-sensors, M^2-3DLaneNet first extracts multi-modal features with modal-specific backbones, then fuses them in a unified Bird's-Eye View (BEV) space. Specifically, our method consists of two core components. 1) To achieve accurate 2D-3D mapping, we propose the top-down BEV generation. Within it, a Line-Restricted Deform-Attention (LRDA) module is utilized to effectively enhance image features in a top-down manner, fully capturing the slenderness features of lanes. After that, it casts the 2D pyramidal features into 3D space using depth-aware lifting and generates BEV features through pillarization. 2) We further propose the bottom-up BEV fusion, which aggregates multi-modal features through multi-scale cascaded attention, integrating complementary information from camera and LiDAR sensors. Sufficient experiments demonstrate the effectiveness of M^2-3DLaneNet, which outperforms previous state-of-the-art methods by a large margin, i.e., 12.1% F1-score improvement on OpenLane dataset

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. Experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, a high consistency between the region of detected clump and that of simulated clump, and is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises $^{13}CO$ ($J = 1-0$) emission line of the MWISP within $11.7^{\circ} \leq l \leq 13.4^{\circ}$, $0.22^{\circ} \leq b \leq 1.05^{\circ}$ and 5 km s$^{-1}$ $\leq v \leq$ 35 km s$^{-1}$ and simulated clumps, reaches 90.2\%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.Comment: 27pages,28figure

Structure and Magnetotransport Properties of Epitaxial Nanocomposite La0.67Ca0.33MnO3:SrTiO3 Thin Films Grown by a Chemical Solution Approach

Epitaxial La0.67Ca0.33MnO3:SrTiO3 (LCMO:STO) composite thin films have been grown on single crystal LaAlO3(001) substrates by a cost effective polymer-assisted deposition method. Both x-ray diffraction and high-resolution transmission electron microscopy confirm the growth of epitaxial films with an epitaxial relationship between the films and the substrates as (002)film||(002)sub and [202]film||[202]sub. The transport property measurement shows that the STO phase significantly increases the resistivity and enhances the magnetoresistance (MR) effect of LCMO and moves the metal-insulator transition to lower temperatures. For example, the MR values measured at magnetic fields of 0 and 3 T are −44.6% at 255 K for LCMO, −94.2% at 125 K for LCMO:3% STO, and −99.4% at 100 K for LCMO:5% STO, respectively