Miracle-dz/UniFAR-FTUs huggingface dataset:
Graduate Group Chairperson

PROCESSING DATA STREAMS Andrew McGregor Sampath Kannan Data streams are ubiquitous. Examples include the network traffic flowing past a router, data generated by an SQL query, readings taken in a sensor network, and files read from an external memory device. The data-stream model abstracts the main algorithmic constraints when processing such data: sequential access to data, limited time to process each data item, and limited working memory. The challenge of designing efficient algorithms in this model has enjoyed significant attention over the last ten years. In this thesis we investigate two new directions of data-streams research: stochastic streams where data-streams are considered through a statistical or learningtheoretic lens and graph streams where highly-structured data needs to be processed. Much of the work in this thesis is motivated by the following general questions. • Stream Ordering : Almost all prior work has considered streams that are assumed to be adversarially ordered. What happens if we relax this assumption? • Multiple Passes : Previous work has focused on the single-pass model. What trade-offs arise if algorithms are permitted multiple passes over the stream. • Space-Efficient Sampling : Rather than approximating functions of the empirical data in the stream, what can be learned about the source of the stream? • Sketchability : A fundamental problem in the data-stream model is to compare two streams. What notions of difference can be estimated in small space? In the process of considering these questions, we present algorithms and lowerbounds for a range of problems including estimating quantiles, various forms of entropy, information divergences, graph-diameter and girth; learning histograms and piecewise-linear probability density functions; and constructing graph-matchings.
Properties of Random Block Copolymer Morphologies: Molecular Dynamics and Single-Chain-in-Mean-Field Simulations

The equilibrium structure and ordering kinetics of random AB block copolymers is investigated using a Lennard-Jones bead–spring model and a soft, coarse-grained model. Upon increasing the incompatibility a disordered microemulsion-like structure is formed, whose length scale slightly increases with segregation. The structure factor of composition fluctuations, molecular conformations, single-chain dynamics and collective ordering kinetics are investigated as a function of the segregation between A and B blocks. The harsh repulsion of the Lennard-Jones potential gives rise to pronounced fluid-like packing effects that affect the liquid structure on the length scale of the bead size and, upon cooling and increase of the local density, result in an additional slowing down of the dynamics. The soft, coarse-grained model does not exhibit pronounced packing effects and the softness of the potential allows for a faster equilibration in computer simulation. The structure and dynamics of the two different models a.
Water: Structure, dynamics and solvation

In recent years it has become clear that water has a much more significant function in biological processes on the nano scale than it was assumed previously. Especially hydrophobic effects play an important role in aggregation processes and protein folding. It is the goal of this work to gain a thorough understanding of the behavior of water at curved interfaces by the use of molecular dynamics simulation methods. As a prerequisite, in a first step structural correlations in bulk water are investigated. By studying solvation processes and fluctuations of air/water interfaces we succeed in further clarifying the role of the hydrophobic effect in protein folding and develop a consistent description of hydrophobic solvation over several length scales. Furthermore, we describe the dynamics of water molecules near interfaces by means of stochastic methods.
The Role of Surface Functionalization of Silica Nanoparticles for Bioimaging

Among the several types of inorganic nanoparticles available, silica nanoparticles (SNP) have earned their relevance in biological applications namely, as bioimaging agents. In fact, fluorescent SNP (FSNP) have been explored in this field as protective nanocarriers, overcoming some limitations presented by conventional organic dyes such as high photobleaching rates. A crucial aspect on the use of fluorescent SNP relates to their surface properties, since it determines the extent of interaction between nanoparticles and biological systems, namely in terms of colloidal stability in water, cellular recognition and internalization, tracking, biodistribution and specificity, among others. Therefore, it is imperative to understand the mechanisms underlying the interaction between biosystems and the SNP surfaces, making surface functionalization a relevant step in order to take full advantage of particle properties. The versatility of the surface chemistry on silica platforms, together with the intrinsic hydroph.
DESCRIPTIVE LITERATURE REVIEW TO CLASSIFY AND ANALYZE GROWTH OF CHINESE FIRMS FROM 1990’s UNTIL 2018

This paper presents an understanding of the economic situation of the Chinese firms and their growth model, using a descriptive research model with an extensive literature review of factors argued to be generating growth and persistent high-growth in China. The selection of reviewed and embedded papers included several quantitative, as well as qualitative papers for analysis from the 1990s until 2018. Thus, the descriptive literature review was used as the methodology by which researchers present the history of the firm’s development and discuss the work of others using scientific arguments and comments. The examination has revealed that numerous factors discussed in this paper affect growth. However, there is no specific structural characteristics or performance of promising persistent-high-growth. Finally, there is a paucity of existing literature focusing on sustainable high-growth dynamics. Studies do not delve into details on this subject; accordingly, it is suggested that the lack of review articles has been hindering the progress of this area, thus, more investigations are requested from future researchers with attention to the use of a plurality of methods.
Fluctuations of the Stieltjes transform of the empirical spectral distribution of selfadjoint polynomials in Wigner and deterministic diagonal matrices

We investigate the fluctuations around the mean of the Stieltjes transform of the empirical spectral distribution of any selfadjoint noncommutative polynomial in a Wigner matrix and a deterministic diagonal matrix. We obtain the convergence in distribution to a centred complex Gaussian process whose covariance is expressed in terms of operator-valued subordination functions.
V-band photometry of asteroids from ASAS-SN. Finding asteroids with slow spin

1 Institute of Astronomy, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000 Prague, Czech Republic 2 Institute of Theoretical Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 18000 Prague, Czech Republic 3 Institute for Astronomy, University of Hawai’i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA 4 Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA 5 Center for Cosmology and Astroparticle Physics, The Ohio State University, 191 W. Woodruff Avenue, Columbus, OH 43210, USA 6 The Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101, USA
DOA Estimation for Hybrid Massive MIMO Systems Using Mixed-ADCs: Performance Loss and Energy Efficiency

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multiple-input multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing low-resolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choices with acceptable performance loss. In this paper, the combination of the mixed-ADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closed-form expression of the Cramér-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixed-ADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2–4 bits of resolution may be a good choice in practical massive MIMO systems.
Bayesian controller fusion: Leveraging control priors in deep reinforcement learning for robotics

We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL). BCF thrives in the robotics domain, where reliable but suboptimal control priors exist for many tasks, but RL from scratch remains unsafe and data-inefficient. By fusing uncertainty-aware distributional outputs from each system, BCF arbitrates control between them, exploiting their respective strengths. We study BCF on two real-world robotics tasks involving navigation in a vast and long-horizon environment, and a complex reaching task that involves manipulability maximisation. For both these domains, simple handcrafted controllers exist that can solve the task at hand in a risk-averse manner but do not necessarily exhibit the optimal solution given limitations in analytical modelling, controller miscalibration and task variation. As exploration is naturally guided by the prior in the early stages of training, BCF accelerates learning, while substantially improving beyond the performance of the control prior, as the policy gains more experience. More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy. We additionally show BCF’s applicability to the zero-shot sim-to-real setting and its ability to deal with out-of-distribution states in the real world. BCF is a promising approach towards combining the complementary strengths of deep RL and traditional robotic control, surpassing what either can achieve independently. The code and supplementary video material are made publicly available at https://krishanrana.github. io/bcf.
How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines

One of the most important humanitarian responsibility of every individual is to protect the future of our children. This entails not only protection of physical welfare but also from ill events that can potentially affect the mental well-being of a child such as sexual coercion and abuse which, in worst-case scenarios, can result to lifelong trauma. In this study, we perform a preliminary investigation of how child sex peddlers spread illegal pornographic content and target minors for sexual activities on Twitter in the Philippines using Natural Language Processing techniques. Results of our studies show frequently used and co-occurring words that traffickers use to spread content as well as four main roles played by these entities that contribute to the proliferation of child pornography in the country.
cGANs with Auxiliary Discriminative Classifier

Conditional generative models aim to learn the underlying joint distribution of data and labels, and thus realize conditional generation. Among them, auxiliary classiﬁer generative adversarial networks (AC-GAN) have been widely used, but suffer from the issue of low intra-class diversity on generated samples. In this paper, we point out that the fundamental reason is that the classiﬁer of AC-GAN is generator-agnostic, and thus cannot provide informative guidance to the generator to approximate the target joint distribution, leading to a minimization of conditional entropy that decreases the intra-class diversity. Based on this ﬁnding, we propose novel cGANs with auxiliary discriminative classiﬁer (ADC-GAN) to address the issue of AC-GAN. Speciﬁcally, the auxiliary discriminative classiﬁer becomes generator-aware by distinguishing between the real and fake data while recognizing their labels. We then optimize the generator based on the auxiliary classiﬁer along with the original discriminator to match the joint and marginal distributions of the generated samples with those of the real samples. We provide theoretical analysis and empirical evidence on synthetic and real-world datasets to demonstrate the superiority of the proposed ADC-GAN compared to competitive cGANs.
A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder

Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dynamics remains elusive. Here, we propose a Network Control Theory of Psychopathology allowing us to formally derive a theoretical control energy which we hypothesize quantifies resistance to future symptom improvement in Major Depressive Disorder (MDD). We test this hypothesis and investigate the relation to genetic and environmental risk as well as resilience. Methods: We modelled longitudinal symptom-network dynamics derived from N=2,059 Beck Depression Inventory measurements acquired over a median of 134 days in a sample of N=109 patients suffering from MDD. We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E0) and 1) tested if E0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and selfreported resilience. Outcomes: We show that E0 indeed predicts symptom reduction at the next measurement and reveal that this coupling between E0 and future symptom change increases with higher genetic risk and childhood maltreatment while it decreases with resilience. Interpretation: Our study provides a mechanistic framework capable of predicting future symptom improvement based on individual symptom-network topology and clarifies the role of genetic and environmental risk as well as resilience. Our control-theoretic framework makes testable, quantitative predictions for individual therapeutic response and provides a starting-point for the theory-driven design of personalized interventions. Funding: German Research Foundation and Interdisciplinary Centre for Clinical Research, Münster
Optimal Operation of Power Systems With Energy Storage Under Uncertainty: A Scenario-Based Method With Strategic Sampling

The multi-period dynamics of energy storage (ES), intermittent renewable generation and uncontrollable power loads, make the optimization of power system operation (PSO) challenging. A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models. Based on the emerging scenario optimization method which does not rely on pre-known probability distribution, this paper develops a novel solution method for this challenging CCO problem. The proposed method is computationally effective for mainly two reasons. First, the original AC power flow constraints are approximated by a set of learning-assisted quadratic convex inequalities based on a generalized least absolute shrinkage and selection operator (LASSSO). Second, considering the physical patterns of data and driven by the learning-based sampling, the strategic sampling method is developed to significantly reduce the required number of scenarios by different sampling strategies. The simulation results on IEEE standard systems indicate that 1) the proposed strategic sampling significantly improves the computational efficiency of the scenario-based approach for solving the chance-constrained optimal PSO problem, 2) the data-driven convex approximation of power flow can be promising alternatives of nonlinear and nonconvex AC power flow.
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework

In multimodal tasks, the importance of text and image modal information often varies for different input cases. To model the difference of importance of different modal information, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit. The text router and image router in Dual-Router take text modal information and image modal information respectively, and MWF-Layer is responsible to determine the importance of modal information. Based on the result of the determination, MWF-Layer generates fused weights for the subsequent experts fusion. Experts can adopt a variety of backbones that match the current multimodal or unimodal task. DRDF features high generality and modularity, and we test 12 backbones such as Visual BERT and their corresponding DRDF instances on the multimodal dataset Hateful memes, and unimodal datasets CIFAR10, CIFAR100, and TinyImagenet. Our DRDF instance outperforms those backbones. We also validate the effectiveness of components of DRDF by ablation studies, and discuss the reasons and ideas of DRDF design.
Hilbert space fragmentation in a 2D quantum spin system with subsystem symmetries

We consider a 2D quantum spin model with ring-exchange interaction that has subsystem symmetries associated to conserved magnetization along rows and columns of a square lattice, which implies the conservation of the global dipole moment. The model is not integrable, but violates the eigenstate thermalization hypothesis through an extensive Hilbert space fragmentation, including an exponential number of inert subsectors with trivial dynamics, arising from kinetic constraints. While subsystem symmetries are quite restrictive for the dynamics, we show that they alone cannot account for such a number of inert states, even with infinite-range interactions. We present a procedure for constructing shielding structures that can separate and disentangle dynamically active regions from each other. Notably, subsystem symmetries allow the thickness of the shields to be dependent only on the interaction range rather than on the size of the active regions, unlike in the case of generic dipole-conserving systems.
Neural Fixed-Point Acceleration for Convex Optimization

Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy. We present neural fixed-point acceleration which combines ideas from meta-learning and classical acceleration methods to automatically learn to accelerate fixed-point problems that are drawn from a distribution. We apply our framework to SCS, the state-of-the-art solver for convex cone programming, and design models and loss functions to overcome the challenges of learning over unrolled optimization and acceleration instabilities. Our work brings neural acceleration into any optimization problem expressible with CVXPY. The source code behind this paper is available at https://github.com/facebookresearch/neural-scs
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection

Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the computational burden, SA-LSTM is designed in two stages. It first locates the coarse landmarks via heatmap regression on a down-sampled CBCT volume and then progressively refines landmarks by attentive offset regression using multi-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a novel graph attention module implicitly encodes the landmark’s global structure to rationalize the predicted position. Moreover, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments conducted on an in-house dataset and a public dataset show that our method outperforms state-of-the-art methods, achieving 1.64 mm and 2.37 mm average errors, respectively. Furthermore, our method is very efficient, taking only 0.5 seconds for inferring the whole CBCT volume of resolution $768\times 768\times 576$ .
Foliations formed by generic coadjoint orbits of a class of 7-dimensional real solvable Lie groups

In this paper, we consider exponential, connected and simply connected Lie groups which are corresponding to Lie algebras of dimension 7 such that the nilradical of them is 5-dimensional nilpotent Lie algebra $\mathfrak{g}_{5,2}$ in Table 1. In particular, we give a description of the geometry of the generic orbits in the coadjoint representation of some considered Lie groups. We prove that, for each considered group, the family of the generic coadjoint orbits forms a measurable foliation in the sense of Connes. The topological classification of these foliations is also provided.
PoF: Proof-of-Following for Vehicle Platoons

Cooperative vehicle platooning significantly improves highway safety, fuel efficiency, and traffic flow. In this model, a set of vehicles move in line formation and coordinate acceleration, braking, and steering using a combination of physical sensing and vehicle-to-vehicle (V2V) messaging. The authenticity and integrity of the V2V messages are paramount to safety. For this reason, recent V2V and V2X standards support the integration of a PKI. However, a PKI cannot bind a vehicle's digital identity to the vehicle's physical state (location, velocity, etc.). As a result, a vehicle with valid cryptographic credentials can impact platoons from a remote location. In this paper, we seek to provide the missing link between the physical and the digital world in the context of vehicle platooning. We propose a new access control protocol we call Proof-of-Following (PoF) that verifies the following distance between a candidate and a verifier. The main idea is to draw security from the common, but constantly changing environment experienced by the closely traveling vehicles. We use the large-scale fading effect of ambient RF signals as a common source of randomness to construct a {\em PoF} primitive. The correlation of large-scale fading is an ideal candidate for the mobile outdoor environment because it exponentially decays with distance and time. We evaluate our PoF protocol on an experimental platoon of two vehicles in freeway, highway, and urban driving conditions. We demonstrate that the PoF withstands both the pre-recording and following attacks with overwhelming probability.