Single particle reconstruction (SPR) in cryo-electron microscopy (cryo-EM) is a prominent imaging method that recovers the 3D shape of a biomolecule, given a large number of its noisy projections from random and unknown views. Recently, CryoGAN [1] cast SPR as an unsupervised distribution matching problem and solved it via a Wasserstein generative adversarial network (WGAN) framework. The approach bypasses the estimation of the projection parameters. The reconstruction criterion in CryoGAN is Wasserstein-1 distance. Despite the desirable properties of Wasserstein distances (WD) such as continuity and almost everywhere differentiability, they are difficult to compute and require careful tuning for a stable training. Sliced Wasserstein distance (SWD), on the other hand, has shown desirable training stability and ease to compute. Therefore, we propose to re-place Wasserstein-1 distance with SWD in the CryoGAN framework, hence the name CryoSWD. In low noise regimes, we show how CryoSWD eliminates the need to have a discriminator which is crucial in CryoGAN. However, coupling CryoSWD with a discriminator boosts its performance, especially in high noise settings. While performing as good as CryoGAN, CryoSWD does not require a gradient penalty term for stabilizing the training and imposing Lipschitz continuity of the discriminator.

Abstract. Unknown view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. A line of work starting with Kam (1980) employs the method of moments with rotation-invariant Fourier features to solve UVT in the frequency domain, assuming that the orientations are uniformly distributed. This line of work includes the recent orthogonal matrix retrieval (OMR) approaches based on matrix factorization, which, while elegant, either require side information about the density that is not available or fail to be sufficiently robust. For OMR to break free from those restrictions, we propose to jointly recover the density map and the orthogonal matrices by requiring that they be mutually consistent. We regularize the resulting nonconvex optimization problem by a denoised reference projection and a nonnegativity constraint. This is enabled by the new closed-form expressions for spatial autocorrelation features. Further, we design an easy-to-compute initial density map which effectively mitigates the nonconvexity of the reconstruction problem. Experimental results show that the proposed OMR with spatial consensus is more robust and performs significantly better than the previous state-of-the-art OMR approach in the typical low signal-to-noise-ratio scenario of 3D UVT.

The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of neurologists. Current neurological digital biomarker pipelines, however, are narrowed down to a specific neurological exam component or applied for assessing specific conditions. In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet. Through our DNE software, healthcare providers in clinical settings and people at home are enabled to video capture an examination while performing instructed neurological tests, including finger tapping, finger to finger, forearm roll, and stand-up and walk. Our modular design of the DNE software supports integrations of additional tests. The DNE extracts from the recorded examinations the 2D/3D human-body pose and quantifies kinematic and spatio-temporal features. The features are clinically relevant and allow clinicians to document and observe the quantified movements and the changes of these metrics over time. A web server and a user interface for recordings viewing and feature visualizations are available. DNE was evaluated on a collected dataset of 21 subjects containing normal and simulated-impaired movements. The overall accuracy of DNE is demonstrated by classifying the recorded movements using various machine learning models. Our tests show an accuracy beyond 90% for upper-limb tests and 80% for the stand-up and walk tests.

Multi-segment reconstruction (MSR) is the problem of estimating a signal given noisy partial observations. Here each observation corresponds to a randomly located segment of the signal. While previous works address this problem using template or moment-matching, in this paper we address MSR from an unsupervised adversarial learning standpoint, named MSR-GAN. We formulate MSR as a distribution matching problem where the goal is to recover the signal and the probability distribution of the segments such that the distribution of the generated measurements following a known forward model is close to the real observations. This is achieved once a min-max optimization involving a generator-discriminator pair is solved. MSR-GAN is mainly inspired by CryoGAN [1]. However, in MSR-GAN we no longer assume the probability distribution of the latent variables, i.e. segment locations, is given and seek to recover it alongside the unknown signal. For this purpose, we show that the loss at the generator side originally is non-differentiable with respect to the segment distribution. Thus, we propose to approximate it using Gumbel-Softmax reparametrization trick. Our proposed solution is generalizable to a wide range of inverse problems. Our simulation results and comparison with various baselines verify the potential of our approach in different settings.

The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.

In this paper, we study the problem of reconstructing a 3D point source model from a set of 2D projections at unknown view angles. Our method obviates the need to recover the projection angles by extracting a set of rotation-invariant features from the noisy projection data. From the features, we reconstruct the density map through a constrained nonconvex optimization. We show that the features have geometric interpretations in the form of radial and pairwise distances of the model. We further perform an ablation study to examine the effect of various parameters on the quality of the estimated features from the projection data. Our results showcase the potential of the proposed method in reconstructing point source models in various noise regimes.

Single-particle cryo-electron microscopy (cryo-EM) reconstructs the three-dimensional (3D) structure of biomolecules from a large set of 2D projection images with random and unknown orientations. A crucial step in the single-particle cryo-EM pipeline is 3D refinement, which resolves a highresolution 3D structure from an initial approximate volume by refining the estimation of the orientation of each projection. In this work, we propose a new approach that refines the projection angles on the continuum. We formulate the optimization problem over the density map and the orientations jointly. The density map is updated using the efficient alternating-direction method of multipliers, while the orientations are updated through a semicoordinate- wise gradient descent for which we provide an explicit derivation of the gradient. Our method eliminates the requirement for a fine discretization of the orientation space and does away with the classical but computationally expensive templatematching step. Numerical results demonstrate the feasibility and performance of our approach compared to several baselines.

In this paper, we study a 2D tomography problem for point source models with random unknown view angles. Rather than recovering the projection angles, we reconstruct the model through a set of rotation-invariant features that are estimated from the projection data. For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances. This establishes a connection between unknown view tomography and unassigned distance geometry problem (uDGP). We propose new methods to extract the distances and approximate the pairwise distance distribution of the underlying points. We then use the recovered distribution to estimate the locations of the points through constrained non-convex optimization. Our simulation results show that our point source reconstruction pipeline is robust to noise and outperforms the regularized expectation maximization (EM) baseline.

Multi-segment reconstruction (MSR) problem consists of recovering a signal from noisy segments with unknown positions of the observation windows. One example arises in DNA sequence assembly, which is typically solved by matching short reads to form longer sequences. Instead of trying to locate the segment within the sequence through pair-wise matching, we propose a new approach that uses shift-invariant features to estimate both the underlying signal and the distribution of the positions of the segments. Using the invariant features, we formulate the problem as a constrained nonlinear least-squares. The non-convexity of the problem leads to its sensitivity to the initialization. However, with clean data, we show empirically that for longer segment lengths, random initialization achieves exact recovery. Furthermore, we compare the performance of our approach to the results of expectation maximization and demonstrate that the new approach is robust to noise and computationally more efficient.

We focus on an alignment-free method to estimate the underlying signal from a large number of noisy randomly shifted observations. Specifically, we estimate the mean, power spectrum, and bispectrum of the signal from the observations. Since the bispectrum contains the phase information of the signal, reliable algorithms for bispectrum inversion are useful in many applications. We propose a new algorithm using spectral decomposition of the bispectrum phase matrix for this task. For clean signals, we show that the eigenvectors of the bispectrum phase matrix correspond to the true phases of the signal and its shifted copies. In addition, the spectral method is robust to noise. It can be used as a stable and efficient initialization technique for local nonconvex optimization for bispectrum inversion.

Device-to-Device communication (D2D) integrated in cellular networks emerges as a new trend in response to notable rise in traffic demand. Resource allocation is one of the important challenges in deployment of D2D networks. In this paper, we formulate an optimization problem for optimal resource allocation and then propose a novel algorithm namely maximum clique based resource allocation (MCRA) for improving the spectral reuse based on graph theoretic concept of maximum clique. Practical application of D2D communications requires each node to receive and transmit signals during the communication process. We have considered this issue in constructing the interference graph and mathematical formulations in our system model. We have proposed an efficient algorithm called binary code-based maximum clique detector (BCMD) to find the maximum clique solution with reduced complexity in interference graphs. Finally, to mitigate the overall interference and complexity of the problem, a two level resource allocation algorithm (TLRA) is suggested. The results demonstrate that substantial gains are achieved in terms of sum rate, time and computational complexity compared to the existing approaches in literature.

In order to respond to drastically increased traffic demands in mobile networks such as cellulars, opportunistic communication has been introduced as a data offloading strategy. Delivering the contents using peer-to-peer data dissemination is more preferable compared to epidemic diffusion schemes. To implement peer-to-peer dissemination, one needs to define the peer selection and content dissemination policies. Efficient content dissemination relies on the selection of suitable peers. Besides that, the peers require to choose what contents to be transmitted between them. In this paper, we propose multi-level master/slave peer selection algorithm (MMSA) and variance-based multi-metric chunk selection algorithm (VMMA) which ensure efficient content dissemination with reduced signaling overhead. VMMA can use the local and global features of the contents to prioritize the contents. A mathematical analysis that considers the time varying nature of the network and traces the evolution of distribution process is provided. The simulation results demonstrate the advantages provided by these algorithms. The simulation results match the mathematical analysis.