Miracle-dz/UniFAR-FTUs huggingface dataset:
A silicon qubit platform for in situ single molecule structure determination

Imaging individual conformational instances of generic, inhomogeneous, transient or intrinsically disordered protein systems at the single molecule level in situ is one of the notable challenges in structural biology. Present techniques access averaged structural information by measuring over large ensembles of proteins in nearly uniform conformational states in synthetic environments. This poses significant implications for diagnostics and drug design which require a detailed understanding of subtle conformational changes, small molecule interactions and ligand dynamics. Here we tackle the problem by designing a single molecule imaging platform technology embracing the advantages silicon-based spin qubits offer in terms of quantum coherence and established fabrication pathways. We demonstrate through detailed simulation, that this platform enables scalable atomic-level structure-determination of individual molecular systems in native environments. The approach is particularly well suited to the high-value lipid-membrane context, and as such its experimental implementation could have far-reaching consequences for the understanding of membrane biology and drug development.
The mass and size of Herbig disks as seen by ALMA

Many population studies have been performed with the Atacama Large Millimeter/submillimeter Array (ALMA) to understand the bulk properties of protoplanetary disks around young stars. The studied populations mostly consisted of G, K&M stars, with relatively few more massive Herbig stars. With GAIA updated distances, now is a good time to use ALMA archival data for a Herbig disk population study and take an important step forward in our understanding of planet formation. This work determines the masses and sizes of all Herbig dust disks observed with ALMA to date out to 450 pc. These masses and sizes are put into context of the Lupus and Upper Sco T Tauri disk populations. ALMA Band 6 and Band 7 archival data of 36 Herbig stars are used, making this work 64% complete excluding Orion. Using stellar parameters and distances the dust masses and sizes of the disks are determined and survival analysis is used to make cumulative distributions of the dust masses and radii. Herbig disks have a higher dust mass than the T Tauri disk populations of Lupus and Upper Sco by factors of $\sim3$ and $\sim7$ respectively. In addition, Herbig disks are often larger than the typical T Tauri disk. Although the masses and sizes of Herbig disks extend over a similar range as those of T Tauri disks, the distributions of masses and sizes of Herbig disks are significantly skewed toward higher values. Lastly, group I disks are more massive than group II disks. Based on these findings we speculate that these differences between Herbig and T Tauri disks find their origin in an initial disk mass that scales with the stellar mass, and that subsequent disk evolution enlarges the observable differences, especially if (sub)mm continuum optical depth plays a role. Moreover, the larger disk masses and sizes of Herbig stars could be linked to the increasing prevalence of giant planets with host star mass.
SpaceSaving± An Optimal Algorithm for Frequency Estimation and Frequent items in the Bounded Deletion Model

In this paper, we propose the first deterministic algorithms to solve the frequency estimation and frequent item problems in the bounded-deletion model. We establish the space lower bound for solving the deterministic frequent items problem in the bounded-deletion model, and propose Lazy SpaceSaving ± and SpaceSaving ± algorithms with optimal space bound. We develop an efficient implementation of the SpaceSaving ± algorithm that minimizes the latency of update operations using novel data structures. The experimental evaluations testify that SpaceSaving ± has accurate frequency estimations and achieves very high recall and precision across different data distributions while using minimal space. Our experiments clearly demonstrate that, if allowed the same space, SpaceSaving± is more accurate than the state-of-the-art protocols with up to logU - 1/ logU of the items deleted, where U is the size of the input universe. Moreover, motivated by prior work, we propose Dyadic SpaceSaving ± , the first deterministic quantile approximation sketch in the bounded-deletion model.
Mesh-Based Solutions for Nonparametric Penalized Regression

It is often of interest to estimate regression functions non-parametrically. Penalized regression (PR) is one statistically-effective, well-studied solution to this problem. Unfortunately, in many cases, finding exact solutions to PR problems is computationally intractable. In this manuscript, we propose a mesh-based approximate solution (MBS) for those scenarios. MBS transforms the complicated functional minimization of NPR, to a finite parameter, discrete convex minimization; and allows us to leverage the tools of modern convex optimization. We show applications of MBS in a number of explicit examples (including both uni- and multi-variate regression), and explore how the number of parameters must increase with our sample-size in order for MBS to maintain the rate-optimality of NPR. We also give an efficient algorithm to minimize the MBS objective while effectively leveraging the sparsity inherent in MBS.
DOCUMENTATION OF CALIFORNIA'S COMMERCIAL MARKET SAMPLING DATA ENTRY AND EXPANSION PROGRAMS

This paper describes the data entry and expansion programs used by the California Department of Fish and Game for their Commercial Rockfish Fishery Management sampling program. The paper includes descriptions of how the programs work, error checking, and instructions for using the programs. Full descriptions of all data files are included as well as complete code lists. This paper is designed for users of the data as well as for users of the programs. As with many data management systems, it is subject to change.
Audio Deepfake Perceptions in College Going Populations

Deepfake is content or material that is generated or manipulated using AI methods, to pass off as real. There are four different deepfake types : audio, video, image and text. In this research we focus on audio deepfakes and how people perceive it. There are several audio deepfake generation frameworks, but we chose MelGAN which is a non-autoregressive and fast audio deepfake generating framework, requiring fewer parameters. This study tries to assess audio deepfake perceptions among college students from different majors. This study also answers the question of how their background and major can affect their perception towards AI generated deepfakes. We also analyzed the results based on different aspects of: grade level, complexity of the grammar used in the audio clips, length of the audio clips, those who knew the term deepfakes and those who did not, as well as the political angle. It is interesting that the results show when an audio clip has a political connotation, it can affect what people think about whether it is real or fake, even if the content is fairly similar. This study also explores the question of how students’ background and major can affect their perception towards deepfakes.
Preparation of Papers for IFAC Conferences & Symposia: FlipBot: A Lizard Inspired Stunt Robot

Abstract Inspired by the acrobatics of the lizard, we present a novel robot platform capable of performing a barrel roll from a ramp. The system is modeled using Euler-Lagrange mechanics, followed by controller design and numerical simulation. A robotic platform is then designed to perform the experiments. Finally, we show that purely by the use of the actuated tail, the robot can rapidly performing a 360 degree roll rotation in under a second.
Correlation between the static and dynamic characteristics of the 4.5 kV self-aligned trench IGBT

In this paper, a new procedure to obtain the Reverse Biased Safe Operating Area (RBSOA) of a 4.5 kV self-aligned trench IGBT is presented. Using this procedure, the high voltage RBSOA boundary of a trench IGBT is directly obtained from its static blocking characteristics extended to very high current densities, thereby saving a large amount of computation time and effort required in obtaining the RBSOA through extensive dynamic simulations. In addition, observation of humps in the collector current waveforms during inductive load turn-off are also reported and analyzed.
Policy Search for Model Predictive Control With Application to Agile Drone Flight

Policy search and model predictive control (MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using experienced data, and MPC can offer optimal control performance using models and trajectory optimization. An open research question is how to leverage and combine the advantages of both approaches. In this article, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel policy-search-for-model-predictive-control framework. Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Such a formulation allows optimizing policies in a self-supervised fashion. We validate this framework by focusing on a challenging problem in agile drone flight: flying a quadrotor through fast-moving gates. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world. The proposed framework offers a new perspective for merging learning and control.
Learning-Based Synthesis of Robust Linear Time-Invariant Controllers

Recent advances in learning for control allow to synthesize vehicle controllers from learned system dynamics and maintain robust stability guarantees. However, no approach is well-suited for training robustly-stabilizing linear time-invariant (LTI) controllers using arbitrary learned models of the dynamics. This article introduces a method to do so. It uses a robust control framework to derive robust stability criteria. It also uses simulated policy rollouts to obtain gradients on the controller parameters, which serve to improve the closed-loop performance. By formulating the stability criteria as penalties with computable gradients, they can be used to guide the controller parameters toward robust stability during gradient descent. The approach is flexible as it does not restrict the type of learned model for the simulated rollouts. The robust control framework ensures that the controller is already robustly stabilizing when first implemented on the actual system and no data is yet collected. It also ensures that the system stays stable in the event of a shift in dynamics, given the system behavior remains within assumed uncertainty bounds. We demonstrate the approach by synthesizing a controller for simulated autonomous lane-change maneuvers. This work thus presents a flexible approach to learning robustly stabilizing LTI controllers that takes advantage of modern machine learning techniques.
A Unifying Bayesian Approach for Sample Size Determination Using Design and Analysis Priors

Power and sample size analysis comprises a critical component of clinical trial study design. There is an extensive collection of methods addressing this problem from diverse perspectives. The Bayesian paradigm, in particular, has attracted noticeable attention and includes different perspectives for sample size determination. Building upon a cost-effectiveness analysis undertaken by O'Hagan and Stevens (2001) with different priors in the design and analysis stage, we develop a general Bayesian framework for simulation-based sample size determination that can be easily implemented on modest computing architectures. We further qualify the need for different priors for the design and analysis stage. We work primarily in the context of conjugate Bayesian linear regression models, where we consider the situation with known and unknown variances. Throughout, we draw parallels with frequentist solutions, which arise as special cases, and alternate Bayesian approaches with an emphasis on how the numerical results from existing methods arise as special cases in our framework.
Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization

Tensor decomposition is one of the fundamental technique for model compression of deep convolution neural networks owing to its ability to reveal the latent relations among complex structures. However, most existing methods compress the networks layer by layer, which cannot provide a satisfactory solution to achieve global optimization. In this paper, we proposed a model reduction method to compress the pre-trained networks using low-rank tensor decomposition of the convolution layers. Our method is based on the optimization techniques to select the proper ranks of decomposed network layers. A new regularization method, called funnel function, is proposed to suppress the unimportant factors during the compression, so the proper ranks can be revealed much easier. The experimental results show that our algorithm can reduce more model parameters than other tensor compression methods. For ResNet18 with ImageNet2012, our reduced model can reach more than twi times speed up in terms of GMAC with merely 0.7% Top-1 accuracy drop, which outperforms most existing methods in both metrics.
Convergence Guarantees for Deep Epsilon Greedy Policy Learning

Policy learning is a quickly growing area. As robotics and computers control day-to-day life, their error rate needs to be minimized and controlled. There are many policy learning methods and bandit methods with provable error rates that accompany them. We show an error or regret bound and convergence of the Deep Epsilon Greedy method which chooses actions with a neural network's prediction. We also show that Epsilon Greedy method regret upper bound is minimized with cubic root exploration. In experiments with the real-world dataset MNIST, we construct a nonlinear reinforcement learning problem. We witness how with either high or low noise, some methods do and some do not converge which agrees with our proof of convergence.
A Novel Convergence Analysis for Algorithms of the Adam Family

Since its invention in 2014, the Adam optimizer has received tremendous attention. On one hand, it has been widely used in deep learning and many variants have been proposed, while on the other hand their theoretical convergence property remains to be a mystery. It is far from satisfactory in the sense that some studies require strong assumptions about the updates, which are not necessarily applicable in practice, while other studies still follow the original problematic convergence analysis of Adam, which was shown to be not sufficient to ensure convergence. Although rigorous convergence analysis exists for Adam, they impose specific requirements on the update of the adaptive step size, which are not generic enough to cover many other variants of Adam. To address theses issues, in this extended abstract, we present a simple and generic proof of convergence for a family of Adam-style methods (including Adam, AMSGrad, Adabound, etc.). Our analysis only requires an increasing or large"momentum"parameter for the first-order moment, which is indeed the case used in practice, and a boundness condition on the adaptive factor of the step size, which applies to all variants of Adam under mild conditions of stochastic gradients. We also establish a variance diminishing result for the used stochastic gradient estimators. Indeed, our analysis of Adam is so simple and generic that it can be leveraged to establish the convergence for solving a broader family of non-convex optimization problems, including min-max, compositional, and bilevel optimization problems. For the full (earlier) version of this extended abstract, please refer to arXiv:2104.14840.
A SIMPLE AC-DC CONVERTER WITH INDIRECT FEEDBACK

A simple low power low voltage AC-DC converter with indirect feedback is presented in this paper. Low power driver circuits can be benefited; since it converts low AC voltage (millivoltage range) to high DC voltage (3.3 volt DC). Direct AC to DC conversion reduces the complexity of the system. The control and driver circuits play a key role in proposed system as the whole system is low voltage/power based. The lack of common ground has implementation difficulty both in feedback and control. Indirect feedback is used to avoid such conditions. The system is more efficient and compact. The feedback section is very simple and thus easy to implement, which also consume low power. An auxiliary supply is also designed to provide power to the gate drivers. For varying load the indirect feedback can be utilized. The design and selection of critical components are discussed. Using MATLAB/Simulink the proposed AC-DC converter is simulated and outputs are obtained.
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time

Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observational data sets constitute rich sources of information to learn from, they also contain biases as treatments are rarely assigned randomly in practice. To provide accurate and unbiased forecasts, we introduce the Disentangled Counterfactual Recurrent Network (DCRN), a novel sequence-to-sequence architecture that estimates treatment outcomes over time by learning representations of patient histories that are disentangled into three separate latent factors: a treatment factor, influencing only treatment selection; an outcome factor, influencing only the outcome; and a confounding factor, influencing both. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding, as our architecture allows for practitioners to infer which patient features influence which part in a patient's trajectory, contrasting other approaches in this domain. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
5 Levels of Agile Planning: From Enterprise Product Vision to Team Stand-up

This paper evaluates agile practices when applied in multi-team and multi-person-year projects. It does so through inspection of the requirements brought on by scaling projects, and applying basic agile principles to those requirements. In the introduction section the main agile principles are introduced, as well as the Lean principles upon which the agile methods are built. One of those Lean principles, Muri or overburdening of people, is addressed through the extension of the agile planning process. The extension of the most used agile planning technique (iteration planning) is described in detail, both the motivation for the extension as well as the collaboration practices behind each planning level. In the final chapter the impact of product complexities on the planning process is evaluated, and a solution to create a smooth flow in the planning/delivery cycle is presented.
Scaling Structured Inference with Randomization

Deep discrete structured models have seen considerable progress recently, but traditional inference using dynamic programming (DP) typically works with a small number of states (less than hundreds), which severely limits model capacity. At the same time, across machine learning, there is a recent trend of using randomized truncation techniques to accelerate computations involving large sums. Here, we propose a family of randomized dynamic programming (RDP) algorithms for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference (partition, marginal, reparameterization, entropy) and different graph structures (chains, trees, and more general hypergraphs). It is also compatible with automatic differentiation: it can be integrated with neural networks seamlessly and learned with gradient-based optimizers. Our core technique approximates the sum-product by restricting and reweighting DP on a small subset of nodes, which reduces computation by orders of magnitude. We further achieve low bias and variance via Rao-Blackwellization and importance sampling. Experiments over different graphs demonstrate the accuracy and efficiency of our approach. Furthermore, when using RDP for training a structured variational autoencoder with a scaled inference network, we achieve better test likelihood than baselines and successfully prevent posterior collapse. code at: https://github.com/FranxYao/RDP
Incompatibility as a Resource for Programmable Quantum Instruments

Quantum instruments represent the most general type of quantum measurement, as they incorporate processes with both classical and quantum outputs. In many scenarios, it may be desirable to have some"on-demand"device that is capable of implementing one of many possible instruments whenever the experimenter desires. We refer to such objects as programmable instrument devices (PIDs), and this paper studies PIDs from a resource-theoretic perspective. A physically important class of PIDs are those that do not require quantum memories to implement, and these are naturally"free"in this resource theory. Additionally, these free objects correspond precisely to the class of unsteerable channel assemblages in the study of channel steering. The traditional notion of measurement incompatibility emerges as a resource in this theory since any PID controlling an incompatible family of instruments requires a quantum memory to build. We identify an incompatibility preorder between PIDs based on whether one can be transformed into another using processes that do not require additional quantum memories. Necessary and sufficient conditions are derived for when such transformations are possible based on how well certain guessing games can be played using a given PID. Ultimately our results provide an operational characterization of incompatibility, and they offer semi-device-independent tests for incompatibility in the most general types of quantum instruments.
Matrix completion and semidefinite matrices

Positive semidefinite Hermitian matrices that are not fully specified can be completed provided their underlying graph is chordal. If the matrix is positive definite the completion can be uniquely characterized as the matrix that maximizes the determinant, or as the matrix whose inverse has zeroes in those places that were undetermined in the original matrix. This paper extends these uniqueness results to the case of semidefinite matrices. Because the determinant vanishes for singular matrices, and because the inverse does not exist, we introduce a generalized determinant and use generalized inverses to formulate equivalent characterizations in the semidefinite case. For a class of matrices that are singular but of maximal rank unique characterizations can be given, just as in the positive definite case. AMS classification: 15A10; 15A15; 15A18; 15A83; 15B48
Campaign Knowledge Network: Building Knowledge for Campaign Efficiency

In the landscape of exascale computing collaborative research campaigns are conducted as co-design activities of loosely coordinated experiments. But the higher level context and the knowledge of individual experimental activity is lost over time. We undertook a knowledge capture and representation aid called Campaign Knowledge Network(CKN), a co-design design and analysis tool. We demonstrate that CKN can satisfy the Hoarde abstraction and can distill campaign context from runtime information thereby creating a knowledge resource upon which analysis tools can run to provide more efficient experimentation