Miracle-dz/UniFAR-FTUs huggingface dataset:
A paired microflap array for single cell interaction analysis

We propose a microflap array system for producing cell membrane contact at single cell level. Microflaps, composed of Parylene and Permalloy, function as the magnet-active flapping actuators. Single cells are cultured onto each microflap. Cell-cultured microflap can be lifted up to make cell-to-cell contact by applying a magnetic field or with the assistance of glass capillary manipulation. By using this device, two single adherent cells can be paired without detaching cells from substrates. This proposed pairing system is expected to facilitate the analysis of cell-cell communication between two or more single adherent cells.
From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form such that more information can be transferred beyond the existing electronic hardware bottleneck. To this end, we present a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy ($\partial \mu$) first uses learnable optical feature extractors as image compressors. The intensity representation produced by these networks is then captured by the imaging sensor. Finally, a reconstruction network running on electronic hardware decompresses the QPM images. In numerical experiments, the proposed system achieves compression of $\times$ 64 while maintaining the SSIM of $\sim 0.90$ and PSNR of $\sim 30$ dB on cells. The results demonstrated by our experiments open up a new pathway for achieving end-to-end optimized (i.e., optics and electronic) compact QPM systems that may provide unprecedented throughput improvements.
Circularly polarized scalar induced gravitational waves from the Chern-Simons modified gravity

We investigate the scalar induced gravitational waves (SIGWs) in the Chern-Simons (CS) modified gravity during the radiation dominated era. The SIGWs are circularly polarized, which provide us a tool to test the possible parity violation in the early universe. We derive the semianalytic expressions to evaluate the fractional energy density of the SIGWs, ΩGW, which receives contributions from the general relativity (GR) and the correction due to the parity-violating term, respectively. We find that the degree of the circular polarization of the SIGWs can be as large as of order unity, although the contribution to ΩGW from the CS term is at most of the same order as that from the GR.
Federated singular value decomposition for high dimensional data

Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors corresponding to the samples are never exchanged and the vectors associated with the features are only transmitted to an aggregator for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.
uGLAD: Sparse graph recovery by optimizing deep unrolled networks

Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for domain exploration and structure discovery in poorly understood domains. This work introduces a novel technique to perform sparse graph recovery by optimizing deep unrolled networks. Assuming that the input data $X\in\mathbb{R}^{M\times D}$ comes from an underlying multivariate Gaussian distribution, we apply a deep model on $X$ that outputs the precision matrix $\hat{\Theta}$, which can also be interpreted as the adjacency matrix. Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting. The key benefits of our model are (1) uGLAD automatically optimizes sparsity-related regularization parameters leading to better performance than existing algorithms. (2) We introduce multi-task learning based `consensus' strategy for robust handling of missing data in an unsupervised setting. We evaluate model results on synthetic Gaussian data, non-Gaussian data generated from Gene Regulatory Networks, and present a case study in anaerobic digestion.
Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

This paper investigates the ability of two soft computing methods including artificial neural network (ANN) and support vector machine (SVM) models in modeling monthly streamflow. The results of ANN and SVM models are compared on basis of determination coefficient (R 2 ), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the performance of the applied models. Comparison of results indicates that the SVM models with RMSE = 147.01 m 3 /s, MAE = 86.68 m 3 /s and R 2 = 0.872 in test period is superior in forecasting monthly streamflows than the ANN models with RMSE = 161.59 m 3 /s, MAE = 94.87 m 3 /s and R 2 = 0.869, respectively. It is found that SVM models can be successfully used in predicting monthly streamflows.
Byzantine-Robust Federated Learning with Optimal Statistical Rates

We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses. We benchmark against competing protocols and show the empirical superiority of the proposed protocols. Finally, we remark that our protocols with bucketing can be naturally combined with privacy-guaranteeing procedures to introduce security against a semi-honest server. The code for evaluation is provided in https://github.com/wanglun1996/secure-robust-federated-learning.
High bandwidth pixel detector modules for the ATLAS Insertable B-Layer

The investigation of the nature of the recently discovered electro-weak symmetry breaking mechanism of the standard model of particle physics as well as the search for physics beyond the standard model with the LHC require to collect even more data. To achieve this goal, the luminosity of the LHC will be increased in two steps. The increased luminosity results in serious challenges for the inner tracking systems of the experiments at the LHC. The ATLAS pixel detector will also be upgraded in a two stage program. During the shutdown in 2013 and 2014 a fourth hybrid pixel detector layer, the socalled Insertable B-Layer (IBL) is inserted inside the existing pixel detector. This thesis focuses on the characterization, performance measurement, and production quality assurance of the central sensitive elements of the IBL, the modules. This includes a full characterization of the readout chip (FE-I4) and of the assembled modules. A completely new inner tracking system is mandatory in ATLAS after the second luminosity increase in the shutdown of 2022 and 2023. The final chapter of this thesis introduces a new module concept that uses an industrial high voltage CMOS technology as sensor layer, which is capacitively coupled to the FE-I4 readout chip.
A Taxonomy of Schema Changes for NoSQL Databases

Schema evolution is a crucial aspect in database management. The proposed taxonomies of schema changes have neglected the set of operations that involves relationships between entity types: aggregation and references, as well as the possible existence of structural variations for schema types, as most of NoSQL systems are schemaless. The distinction between entity types and relationship types, which is typical of graph schemas, is also not taken into account in the published works. Moreover, NoSQL schema evolution poses the challenge of having diﬀerent data models, and no standard speciﬁcation exists for them. In this paper, a generic approach for evolving NoSQL and relational schemas is presented, which is based on the U-Schema uniﬁed data model that includes aggregation and reference relationships, and structural variations. For this data model, we introduce a taxonomy of schema changes for all the U-Schema elements, which is implemented by creating the Orion database-independent language. We will show how Orion can be used to automatically generate evolution scripts for a set of NoSQL databases, and the feasibility of each schema operation will be analyzed through the performance results obtained. The taxonomy has been formally validated by means of Alloy, and two case studies show the application of Orion.
Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constructed into a GPU datacenter. An efficient scheduler design for such GPU datacenter is crucially important to reduce the operational cost and improve resource utilization. However, traditional approaches designed for big data or high performance computing workloads can not support DL workloads to fully utilize the GPU resources. Recently, substantial schedulers are proposed to tailor for DL workloads in GPU datacenters. This paper surveys existing research efforts for both training and inference workloads. We primarily present how existing schedulers facilitate the respective workloads from the scheduling objectives and resource consumption features. Finally, we prospect several promising future research directions. More detailed summary with the surveyed paper and code links can be found at our project website: https://github.com/S-Lab-System-Group/Awesome-DL-Scheduling-Papers
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling

Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the distribution of the particles to evolve during the algorithm, as is the case in sequential simulation methods. Within the standard adaptive importance sampling framework, a simple weighted least squares approach is proposed to improve the procedure with control variates. The procedure takes the form of a quadrature rule with adapted quadrature weights to reflect the information brought in by the control variates. The quadrature points and weights do not depend on the integrand, a computational advantage in case of multiple integrands. Moreover, the target density needs to be known only up to a multiplicative constant. Our main result is a non-asymptotic bound on the probabilistic error of the procedure. The bound proves that for improving the estimate's accuracy, the benefits from adaptive importance sampling and control variates can be combined. The good behavior of the method is illustrated empirically on synthetic examples and real-world data for Bayesian linear regression.
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning

Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem of predicting the distribution of returns obtained by an agent interacting in a continuous-time, stochastic environment. Accurate return predictions have proven useful for determining optimal policies for risk-sensitive control, learning state representations, multiagent coordination, and more. We begin by establishing the distributional analogue of the Hamilton-Jacobi-Bellman (HJB) equation for It\^o diffusions and the broader class of Feller-Dynkin processes. We then specialize this equation to the setting in which the return distribution is approximated by $N$ uniformly-weighted particles, a common design choice in distributional algorithms. Our derivation highlights additional terms due to statistical diffusivity which arise from the proper handling of distributions in the continuous-time setting. Based on this, we propose a tractable algorithm for approximately solving the distributional HJB based on a JKO scheme, which can be implemented in an online control algorithm. We demonstrate the effectiveness of such an algorithm in a synthetic control problem.
Beyond Fact Verification: Comparing and Contrasting Claims on Contentious Topics

As the importance of identifying misinformation is increasing, many researchers focus on verifying textual claims on the web. One of the most popular tasks to achieve this is fact veriﬁcation, which retrieves an evidence sentence from a large knowledge source such as Wikipedia to either verify or refute each factual claim. However, while such problem formulation is helpful for detecting false claims and fake news, it is not applicable to catching subtle differences in factually consistent claims which still might implicitly bias the readers, especially in contentious topics such as political, gender, or racial issues. In this study, we propose ClaimDiff, a novel dataset to compare the nuance between claim pairs in both a discriminative and a generative manner, with the underlying assumption that one is not necessarily more true than the other. This differs from existing fact veriﬁcation datasets that verify the target sentence with respect to an absolute truth. We hope this task assists people in making more informed decisions among various sources of media.
Towards Better Privacy-preserving Electronic Voting System

This paper presents two approaches of privacy-preserving voting system: Blind Signature-based Voting (BSV) and Homorphic Encryption Based Voting (HEV). BSV is simple, stable, and scalable, but requires additional anonymous property in the communication with the blockchain. HEV simultaneously protects voting privacy against traffic-analysis attacks, prevents cooperation interruption by malicious voters with a high probability, but the scalability is limited. We further apply sampling to mitigate the scalability problem in HEV and simulate the performance under different voting group size and number of samples.
AILS-II: An Adaptive Iterated Local Search Heuristic for the Large-scale Capacitated Vehicle Routing Problem

A recent study on the classical Capacitated Vehicle Routing Problem (CVRP) introduced an adaptive version of the widely used Iterated Local Search (ILS) paradigm, hybridized with a path-relinking strategy (PR). The solution method, called AILS-PR, outperformed existing metaheuristics for the CVRP on benchmark instances. However, tests on large-scale instances of the CVRP suggested that PR was too slow, making AILS-PR less advantageous in this case. To over-come this challenge, this paper presents an Adaptive Iterated Local Search (AILS) with two phases in its search process. Both phases include the perturbation and local search steps of ILS. The main di ﬀ erence between them is that the reference solution in the ﬁrst phase is found by the acceptance criterion, while in the second phase it is selected from a pool of the best solutions found in the search process, the so-called elite set. This algorithm, called AILS-II, is very competitive on smaller instances, outperforming the other methods from the literature with respect to the average gap to the best known solutions. Moreover, AILS-II consistently outperformed the state of the art on larger instances with up to 30,000 vertices.
Discovery of a 584.65 Hz Burst Oscillation in the Low-mass X-Ray Binary 4U 1730–22

Type-I X-ray burst oscillations are powered by thermonuclear energy released on the neutron star (NS) surface in low-mass X-ray binaries (LMXBs), where the burst oscillation frequencies are close to the NS spin rates. In this work, we report the detection of oscillation at 584.65 Hz during the cooling tail of type-I X-ray bursts observed from the accreting NS LMXB 4U 1730–22 on 2022 March 20, by the Neutron star Interior Composition Explorer telescope. The oscillation signal showed a strong Leahy power, P m ∼ 54.04, around 584.65 Hz, which has single-trial and multiple-trial confidence levels of 7. 05σ and 4. 73σ, respectively. The folded pulse profile of the oscillation in the 0.2–10 keV band showed a sinusoidal shape with the fractional rms amplitude of (8.0 ± 1.1)%. We found the oscillation frequency showed insignificant upward drifting, i.e., less than 0.3 Hz, during the cooling tail, similar to the behavior appearing in accreting millisecond X-ray pulsars (AMXP), and indicate the source could be an AMXP spinning at 1.71 ms.
Transceiver design for wireless energy harvesting cooperative networks

In this thesis, the RF energy harvesting technique is studied in the cooperative wireless network, and different optimization studies are investigated. First, an energy-efficient optimization is considered in the cooperative system with the time switching relaying and power splitting relaying protocols. Then, a security issue in the cooperative network with energy harvesting is proposed, in which the optimization problem is designed to maximize the secrecy rate. We also consider the application of energy harvesting in the full-duplex relaying network with a self-energy recycling protocol. Finally, the energy harvesting is studied in the full-duplex cooperative cognitive radio network. The system performance of all studies is verified in the thesis with MATLAB simulation results.
Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present a novel technique for ﬁltering, and scoring data based on a practical inﬂuence approximation that can be implemented in a privacy-preserving manner. Each agent uses his own data to evaluate the inﬂuence of another agent’s batch, and reports to the center an obfuscated score using differential privacy. Our technique allows for almost perfect ( > 92% recall) ﬁltering of corrupted data in a variety of applications using real-data . Importantly, the accuracy does not degrade signiﬁcantly, even under really strong privacy guarantees ( ε ≤ 1 ), especially under realistic percentages of mislabeled data (for 15% mislabeled data we only lose 10% in accuracy).
Analysis of multi-agent systems under varying degrees of trust, cooperation, and competition

Multi-agent systems rely heavily on coordination and cooperation to achieve a variety of tasks. It is often assumed that these agents will be fully cooperative, or have reliable and equal performance among group members. Instead, we consider cooperation as a spectrum of possible interactions, ranging from performance variations within the group to adversarial agents. This thesis examines several scenarios where cooperation and performance are not guaranteed. Potential applications include sensor coverage, emergency response, wildlife management, tracking, and surveillance. We use geometric methods, such as Voronoi tessellations, for design insight and Lyapunov-based stability theory to analyze our proposed controllers. Performance is verified through simulations and experiments on a variety of ground and aerial robotic platforms. First, we consider the problem of Voronoi-based coverage control, where a group of robots must spread out over an environment to provide coverage. Our approach adapts online to sensing and actuation performance variations with the group. The robots have no prior knowledge of their relative performance, and
Minimax optimal clustering of bipartite graphs with a generalized power method

Clustering bipartite graphs is a fundamental task in network analysis. In the high-dimensional regime where the number of rows $n_{1}$ and the number of columns $n_{2}$ of the associated adjacency matrix are of different order, the existing methods derived from the ones used for symmetric graphs can come with sub-optimal guarantees. Due to increasing number of applications for bipartite graphs in the high-dimensional regime, it is of fundamental importance to design optimal algorithms for this setting. The recent work of Ndaoud et al. (2022, IEEE Trans. Inf. Theory, 68, 1960–1975) improves the existing upper-bound for the misclustering rate in the special case where the columns (resp. rows) can be partitioned into $L = 2$ (resp. $K = 2$) communities. Unfortunately, their algorithm cannot be extended to the more general setting where $K \neq L \geq 2$. We overcome this limitation by introducing a new algorithm based on the power method. We derive conditions for exact recovery in the general setting where $K \neq L \geq 2$, and show that it recovers the result in Ndaoud et al. (2022, IEEE Trans. Inf. Theory, 68, 1960–1975). We also derive a minimax lower bound on the misclustering error when $K=L$ under a symmetric version of our model, which matches the corresponding upper bound up to a factor depending on $K$.
Exploring Concept Contribution Spatially: Hidden Layer Interpretation with Spatial Activation Concept Vector

To interpret deep learning models, one mainstream is to explore the learned concepts by networks. Testing with Concept Activation Vector (TCAV) presents a powerful tool to quantify the contribution of query concepts (represented by user-defined guidance images) to a target class. For example, we can quantitatively evaluate whether and to what extent concept striped contributes to model prediction zebra with TCAV. Therefore, TCAV whitens the reasoning process of deep networks. And it has been applied to solve practical problems such as diagnosis. However, for some images where the target object only occupies a small fraction of the region, TCAV evaluation may be interfered with by redundant background features because TCAV calculates concept contribution to a target class based on a whole hidden layer. To tackle this problem, based on TCAV, we propose Spatial Activation Concept Vector (SACV) which identifies the relevant spatial locations to the query concept while evaluating their contributions to the model prediction of the target class. Experiment shows that SACV generates a more fine-grained explanation map for a hidden layer and quantifies concepts' contributions spatially. Moreover, it avoids interference from background features. The code is available on https://github.com/AntonotnaWang/Spatial-Activation-Concept-Vector.