WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection

In the field of domain adaptation (DA) on 3D object detection, most of the work is dedicated to unsupervised domain adaptation (UDA). Yet, without any target annotations, the performance gap between the UDA approaches and the fully-supervised approach is still noticeable, which is impractical for real-world applications. On the other hand, weakly-supervised domain adaptation (WDA) is an underexplored yet practical task that only requires few labeling effort on the target domain. To improve the DA performance in a cost-effective way, we propose a general weak labels guided self-training framework, WLST, designed for WDA on 3D object detection. By incorporating autolabeler, which can generate 3D pseudo labels from 2D bounding boxes, into the existing self-training pipeline, our method is able to generate more robust and consistent pseudo labels that would benefit the training process on the target domain. Extensive experiments demonstrate the effectiveness, robustness, and detector-agnosticism of our WLST framework. Notably, it outperforms previous state-of-the-art methods on all evaluation tasks

Concurrent Channel Access and Estimation for Scalable Multiuser MIMO Networking

This paper presents the design of MIMO/CON (“MIMO with concurrent channel access and estimation”), a PHY/MAC cross-layer design delivering throughput scalable to many users for multiuser MIMO wireless networking. By allowing concurrent launches of multiple data transmissions from multiple users, MIMO/CON can fully realize the capacity gain of a multi-antenna MIMO system. Using compressive sensing, MIMO/CON simultaneously estimates channel state information (CSI) of multiple channels from concurrently received preambles. Furthermore, MIMO/CON can boost channel utilization by allowing concurrent transmissions to exceed receive antennas momentarily. MIMO/CON has been implemented and evaluated on a lab testbed with software-defined radios. Further, simulation results suggest that MIMO/CON can achieve an improvement by up to 210% in MAC throughput over existing staggered access protocols in a 5×5 MIMO scenario.Engineering and Applied Science

Computing Sparse Representations in O(N log N) Time

Engineering and Applied Science

Compressive Sensing Medium Access Control for Wireless LANs

We propose a medium access control (MAC) protocol for wireless local area networks (LANs) that leverages the theory of compressive sensing. The proposed compressive sensing MAC (CS-MAC) exploits the sparse property that, at a given time, only a few hosts are expected to request for radio channel access. Under CS-MAC, a central coordinator, such as a wireless access point (AP) can recover a multitude of these requests in one decoding operation, and then schedule multiple hosts accordingly. The coordinator is only required to receive a relatively small number of random projections of host requests, rather than polling individual hosts. This results in an efficient request-grant method. Via a hardware prototype based on a software-de ned radio platform, we demonstrate the feasibility of realizing CS-MAC with compressive measurements formed in the air to achieve high efficiency.Engineering and Applied Science

Parallelization Primitives for Dynamic Sparse Computations

We characterize a general class of algorithms common in machine learning, scientific computing, and signal processing, whose computational dependencies are both sparse, and dynamically defined throughout execution. Existing parallel computing runtimes, like MapReduce and GraphLab, are a poor fit for this class because they assume statically defined dependencies for resource allocation and scheduling decisions. As a result, changing load characteristics and straggling compute units degrade performance significantly. However, we show that the sparsity of computational dependencies and these algorithms’ natural error tolerance can be exploited to implement a flexible execution model with large efficiency gains, using two simple primitives: selective push-pull and statistical barriers. With reconstruction for compressive time-lapse MRI as a motivating application, we deploy a large Orthogonal Matching Pursuit (OMP) computation on Amazon’s EC2 cluster to demonstrate a 19x speedup over current static execution models.Engineering and Applied Science

Stable and Efficient Representation Learning with Nonnegativity Constraints

Orthogonal matching pursuit (OMP) is an efficient approximation algorithm for computing sparse representations. However, prior research has shown that the representations computed by OMP may be of inferior quality, as they deliver suboptimal classification accuracy on several im- age datasets. We have found that this problem is caused by OMP’s relatively weak stability under data variations, which leads to unreliability in supervised classifier training. We show that by imposing a simple nonnegativity constraint, this nonnegative variant of OMP (NOMP) can mitigate OMP’s stability issue and is resistant to noise overfitting. In this work, we provide extensive analysis and experimental results to examine and validate the stability advantage of NOMP. In our experiments, we use a multi-layer deep architecture for representation learning, where we use K-means for feature learning and NOMP for representation encoding. The resulting learning framework is not only efficient and scalable to large feature dictionaries, but also is robust against input noise. This framework achieves the state-of-the-art accuracy on the STL-10 dataset.Engineering and Applied Science

Identifying Bad Measurements in Compressive Sensing

Abstract-We consider the problem of identifying bad measurements in compressive sensing. These bad measurements can be present due to malicious attacks and system malfunction. Since the system of linear equations in compressive sensing is underconstrained, errors introduced by these bad measurements can result in large changes in decoded solutions. We describe methods for identifying bad measurements so that they can be removed before decoding. In a new separation-based method we separate out top nonzero variables by ranking, eliminate the remaining variables from the system of equations, and then solve the reduced overconstrained problem to identify bad measurements. Comparing to prior methods based on direct or joint ℓ1-minimization, the separation-based method can work under a much smaller number of measurements. In analyzing the method we introduce the notion of inversions which governs the separability of large nonzero variables

Parametric modeling of cellular state transitions as measured with flow cytometry

<p>Abstract</p> <p>Background</p> <p>Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor.</p> <p>Results</p> <p>To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew <it>t</it>-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a well-fitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.</p> <p>Conclusions</p> <p>By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.</p

Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning

We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder. With such an alignment, a model can identify regions of an image corresponding to a given text input, and therefore transfer seamlessly to the task of open vocabulary semantic segmentation without requiring any segmentation annotations during training. Using pre-trained CLIP encoders with PACL, we are able to set the state-of-the-art on the task of open vocabulary zero-shot segmentation on 4 different segmentation benchmarks: Pascal VOC, Pascal Context, COCO Stuff and ADE20K. Furthermore, we show that PACL is also applicable to image-level predictions and when used with a CLIP backbone, provides a general improvement in zero-shot classification accuracy compared to CLIP, across a suite of 12 image classification datasets

Cell Anchorage Permits Efficient Signal Transduction Between Ras and Its Downstream Kinases

Cell anchorage strongly affects the signal transduction cascade initiated by peptide mitogens. For both epidermal growth factor and platelet-derived growth factor, activation of the consensus mitogen-activated protein kinase cascade is impaired when cells are held in suspension as compared with cells anchored to a fibronectin substratum. Upstream events in the signaling cascade, including tyrosine phosphorylation of the mitogen receptor and GTP loading of Ras, are similar in anchored and suspended cells. However, propagation of the signal to Raf and subsequently to the downstream kinases MEK and mitogen-activated protein kinase is markedly attenuated in suspended cells. Thus, there seems to be a distinct anchorage-dependent step between Ras and Raf in the signaling cascade initiated by peptide mitogens. These observations may have important implications for understanding the anchorage dependence of cell growth