Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective manipulation and tunneling spectroscopy of broken-symmetry quantum Hall states in a hybrid-edge quantum point contact

We present a device architecture of hybrid-edge and dual-gated quantum point contact. We demonstrate improved electrostatic control over the separation, position, and coupling of each broken-symmetry compressible strip in graphene. Via low-temperature magneto-transport measurement, we demonstrate selective manipulation over the evolution, hybridization, and transmission of arbitrarily chosen quantum Hall states in the channel. With gate-tunable tunneling spectroscopy, we characterize the energy gap of each symmetry-broken quantum Hall state with high resolution on the order of ~0.1 meV.
Expression Invariant Face Recognition with a 3DMM

Expression normalisation for two scans of the same individual. The robust fitting gives a good estimate (b) of the true face surface given the noisy measurement (a). It fills in holes and re- moves artifacts using prior knowledge from the face model. The pose and expression normal- ized faces (c) are used for face recognition.
Neural-Based Test Oracle Generation: A Large-Scale Evaluation and Lessons Learned

Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techniques have shown promise, their real-world effectiveness remains a compelling question requiring further exploration and understanding. This paper investigates the effectiveness of TOGA, a recently developed neural-based method for automatic test oracle generation. TOGA utilizes EvoSuite-generated test inputs and generates both exception and assertion oracles. In a Defects4j study, TOGA outperformed specification, search, and neural-based techniques, detecting 57 bugs, including 30 unique bugs not detected by other methods. To gain a deeper understanding of its applicability in real-world settings, we conducted a series of external, extended, and conceptual replication studies of TOGA. In a large-scale study involving 25 real-world Java systems, 223.5K test cases, and 51K injected faults, we evaluate TOGA’s ability to improve fault-detection effectiveness relative to the state-of-the-practice and the state-of-the-art. We find that TOGA misclassifies the type of oracle needed 24% of the time and that when it classifies correctly around 62% of the time it is not confident enough to generate any assertion oracle. When it does generate an assertion oracle, more than 47% of them are false positives, and the true positive assertions only increase fault detection by 0.3% relative to prior work. These findings expose limitations of the state-of-the-art neural-based oracle generation technique, provide valuable insights for improvement, and offer lessons for evaluating future automated oracle generation methods.
Catching Elusive Depression via Facial Micro-Expression Recognition

Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the challenge, and describe a low-cost and privacy-preserving solution that enables self-diagnosis using portable mobile devices in a personal setting (e.g., at home). We present results and findings that validate our method, and discuss other technical challenges and future directions in applying such techniques to real clinical settings.
DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training

Expressive text-to-speech systems have undergone significant advancements owing to prosody modeling, but conventional methods can still be improved. Traditional approaches have relied on the autoregressive method to predict the quantized prosody vector; however, it suffers from the issues of long-term dependency and slow inference. This study proposes a novel approach called DiffProsody in which expressive speech is synthesized using a diffusion-based latent prosody generator and prosody conditional adversarial training. Our findings confirm the effectiveness of our prosody generator in generating a prosody vector. Furthermore, our prosody conditional discriminator significantly improves the quality of the generated speech by accurately emulating prosody. We use denoising diffusion generative adversarial networks to improve the prosody generation speed. Consequently, DiffProsody is capable of generating prosody 16 times faster than the conventional diffusion model. The superior performance of our proposed method has been demonstrated via experiments.
Study of blue metal-poor stars using UVIT/AstroSat

Blue metal-poor stars are main-sequence stars that are bluer and brighter than typical turn-off stars in metal-poor globular clusters. They are thought to have either evolved through post-mass transfer mechanisms as field blue straggler stars or have accreted from Milky Way dwarf satellite galaxies. It has been found that a considerable fraction of blue metal poor stars are binaries, possibly with a compact companion. We observed 27 blue metal poor stars using UV imaging telescope of AstroSat in two far-UV filters, F148W and F169M. In this work, we explain the possible formation channels of two stars, BMP17 and BMP37. We fit BMP17 with a single-component spectral energy distribution whereas BMP37 with a binary-component spectral energy distribution. As both of them are known SB1s, we suggest that the WD companion of BMP17 may have cooled down so that it is out of UV imaging telescope detection limit. On the other hand, we discover a normal mass white dwarf as the hot companion of BMP37, indicating mass transfer as the possible formation channel
Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates.

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for p K a $$ {K}_{\mathrm{a}} $$ prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems

In a transmit preprocessing aided frequency division duplex (FDD) massive multi-user (MU) multiple-input multiple-output (MIMO) scheme assisted orthogonal frequency-division multiplexing (OFDM) system, it is required to feed back the frequency domain channel transfer function (FDCHTF) of each subcarrier at the user equipment (UE) to the base station (BS). The amount of channel state information (CSI) to be fed back to the BS increases linearly with the number of antennas and subcarriers, which may become excessive. Hence we propose a novel CSI feedback compression algorithm based on compressive sensing (CS) by designing a common dictionary (CD) to reduce the CSI feedback of existing algorithms. Most of the prior work on CSI feedback compression considered single-UE systems. Explicitly, we propose a common dictionary learning (CDL) framework for practical frequency-selective channels and design a CD suitable for both single-UE and multi-UE systems. A set of two methods is proposed. Specifically, the first one is the CDL-K singular value decomposition (KSVD) method, which uses the K-SVD algorithm. The second one is the CDL-orthogonal Procrustes (OP) method, which relies on solving the orthogonal Procrustes problem. The CD conceived for exploiting the spatial correlation of channels across all the subcarriers and UEs compresses the CSI at each UE, and upon reception reconstructs it at the BS. Our simulation results show that the proposed dictionary's estimated channel vectors have lower normalized mean-squared error (NMSE) than the traditional fixed Discrete Fourier Transform (DFT) based dictionary. The CSI feedback is reduced by 50%, and the memory reduction at both the UE and BS starts from 50% and increases with the number of subcarriers.
On Decidability of Existence of Fortified Supervisors Against Covert Actuator Attackers

This work investigates the existence of fortified supervisors against covert actuator attackers. For a supervisor <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> that is nonresilient against covert actuator attackers, a fortified supervisor <inline-formula><tex-math notation="LaTeX">$S^{\prime }$</tex-math></inline-formula> satisfies two requirements: 1) <inline-formula><tex-math notation="LaTeX">$S^{\prime }$</tex-math></inline-formula> is resilient against any covert actuator attacker, and 2) the original closed-behavior of the closed-loop system under <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> is preserved. We design a sound and complete procedure to show the problem of determining the existence of a fortified supervisor against covert actuator attackers is decidable. The decidability result is also extended to the case against the worst-case attacker.
Metastability cascades and prewetting in the SOS model

We study Glauber dynamics for the low temperature $(2+1)$D Solid-On-Solid model on a box of side-length $n$ with a floor at height $0$ (inducing entropic repulsion) and a competing bulk external field $\lambda$ pointing down (the prewetting problem). In 1996, Cesi and Martinelli showed that if the inverse-temperature $\beta$ is large enough, then along a decreasing sequence of critical points $(\lambda_c^{(k)})_{k=0}^{K_\beta}$ the dynamics is torpid: its inverse spectral gap is $O(1)$ when $\lambda \in (\lambda_c^{(k+1)},\lambda_c^{(k)})$ whereas it is $\exp[\Theta(n)]$ at each $\lambda_c^{(k)}$ for each $k\leq K_\beta$, due to a coexistence of rigid phases at heights $k+1$ and $k$. Our focus is understanding (a) the onset of metastability as $\lambda_n\uparrow\lambda_c^{(k)}$; and (b) the effect of an unbounded number of layers, as we remove the restriction $k\le K_\beta$, and even allow for $\lambda_n\to 0$ towards the $\lambda = 0$ case which has $O(\log n)$ layers and was studied by Caputo et al. (2014). We show that for any $k$, possibly growing with $n$, the inverse gap is $\exp[\tilde\Theta(1/|\lambda_n-\lambda_c^{(k)}|)]$ as $\lambda\uparrow \lambda_c^{(k)}$ up to distance $n^{-1+o(1)}$ from this critical point, due to a metastable layer at height $k$ on the way to forming the desired layer at height $k+1$. By taking $\lambda_n = n^{-\alpha}$ (corresponding to $k_n\asymp \log n$), this also interpolates down to the behavior of the dynamics when $\lambda =0$. We compliment this by extending the fast mixing to all $\lambda$ uniformly bounded away from $(\lambda_c^{(k)})_{k=0}^\infty$. Together, these results provide a sharp understanding of the predicted infinite sequence of dynamical phase transitions governed by the layering phenomenon.
CMA Light: a novel Minibatch Algorithm for large-scale non convex finite sum optimization

The supervised training of a deep neural network on a given dataset consists in the unconstrained minimization a finite sum of continuously differentiable functions, commonly referred to as loss with respect to the samples. These functions depend on the network parameters and most of the times are non-convex. We develop CMA Light, a new globally convergent mini-batch gradient method to tackle this problem. We consider the recently introduced Controlled Minibatch Algorithm (CMA) framework and we overcome its main bottleneck, removing the need for at least one evaluation of the whole objective function per iteration. We proof globally convergence of CMA Light under mild assumptions and we discuss extensive computational results on the same experimental test-bed used for CMA, showing that CMA Light requires less computational effort than most of the state-of-the-art optimizers. Eventually, we present early results on a large-scale Image Classification task.
An Online Defense Framework against Cyber Attacks on Automatic Generation Control

We propose an online framework to defend against cyber attacks on Automatic Generation Control (AGC). A cyber attack detection algorithm is designed based on the approach of Dynamic Watermarking. The detection algorithm provides a theoretical guarantee of detection of cyber attacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems. The proposed framework is practically implementable, as it needs no hardware update on generation units. The efficacy of the proposed framework is validated by a four-area synthetic power system.
Reprojection Methods for Koopman-Based Modelling and Prediction

Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman framework. In eDMD a compression of the lifted system dynamics on the space spanned by finitely many observables is computed, in which the original space is embedded as a low-dimensional manifold. While this manifold is invariant for the infinite-dimensional Koopman operator, this invariance is typically not preserved for its eDMD-based approximation. Hence, an additional (re-)projection step is often tacitly incorporated to improve the prediction capability. We propose a novel framework for consistent reprojectors respecting the underlying manifold structure. Further, we present a new geometric reprojector based on maximum-likelihood arguments, which significantly enhances the approximation accuracy and preserves known finite-data error bounds.
Persistent homology analysis of type 2 diabetes genome-wide association studies in protein–protein interaction networks

Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein–protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P < 0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P < 0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.
Online Matching: A Real-time Bandit System for Large-scale Recommendations

The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users’ past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, making it hard to adapt to distribution shift and explore new items or user interests. Although online learning-based approaches (e.g., multi-armed bandits) have demonstrated promising theoretical results in tackling these challenges, their practical real-time implementation in large-scale recommender systems remains limited. First, the scalability of online approaches in servicing a massive online traffic while ensuring timely updates of bandit parameters poses a significant challenge. Additionally, exploring uncertainty in recommender systems can easily result in unfavorable user experience, highlighting the need for devising intricate strategies that effectively balance the trade-off between exploitation and exploration. In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users’ direct feedback on items in real time. We present a hybrid offline + online approach for constructing this system, accompanied by a comprehensive exposition of the end-to-end system architecture. We propose Diag-LinUCB – a novel extension of the LinUCB algorithm – to enable distributed updates of bandits parameter in a scalable and timely manner. We conduct live experiments in YouTube and show that Online Matching is able to enhance the capabilities of fresh content discovery and item exploration in the present platform.
Strain Tuning of the Anisotropy in the Optoelectronic Properties of TiS3

The benefits of two-dimensional (2D) materials for applications in nanotechnology can be widened by exploiting the intrinsic anisotropy of some of those crystals, being black phosphorus the most well-known example. In this work we demonstrate that the anisotropy of TiS3, which is even stronger than that of black phosphorus, can be tuned by means of strain engineering. Using density functional theory calculations, we find that the ellipticity of the valence band can be inverted under moderate compressive strain, which is accompanied by an enhancement of the optical absorption. It is shown that the strain tuning of the band anisotropy can be exploited to focus plasmons in the desired direction, a feature that could be used to design TiS3 nanostructures with switchable plasmon channeling.
VATP360: Viewport Adaptive 360-Degree Video Streaming based on Tile Priority

360-degree video becomes increasingly popular among users. In the current network bandwidth, serving high resolution 360 degree video to users is quite difficult. Most of the work has been devoted to the prediction of user viewports or tile-based adaptive algorithms. However, it is difficult to predict user viewports more accurately using only information such as user's historical viewports or video saliency maps. In this paper, we propose a viewport adaptive 360-degree video streaming method based on tile priority (VATP360), which tries to balance between the performance and the overhead. The proposed VATP360 consists of three main modules: viewport prediction, tile priority classification and bitrate allocation. In the viewport prediction module, object motion trajectory and predicted user's region-of-interest (ROI) are used to achieve accurate prediction of the user's future viewport. Then, the predicted viewport, along with the object motion trajectory, are fed into the proposed tile priority classification algorithm to assign different priorities to tiles, which would reduce the computational complexity of the bitrate allocation module. Finally in the bitrate allocation stage, we adaptively assign bitrates to tiles of different priority by reinforcement learning. Experimental results on publicly available datasets have demonstrated the effectiveness of the proposed method.
Primitive Skill-Based Robot Learning from Human Evaluative Feedback

Reinforcement learning (RL) algorithms face significant challenges when dealing with long-horizon robot manipulation tasks in real-world environments due to sample inefficiency and safety issues. To overcome these challenges, we propose a novel framework, SEED, which leverages two approaches: reinforcement learning from human feedback (RLHF) and primitive skill-based reinforcement learning. Both approaches are particularly effective in addressing sparse reward issues and the complexities involved in long-horizon tasks. By combining them, SEED reduces the human effort required in RLHF and increases safety in training robot manipulation with RL in real-world settings. Additionally, parameterized skills provide a clear view of the agent's high-level intentions, allowing humans to evaluate skill choices before they are executed. This feature makes the training process even safer and more efficient. To evaluate the performance of SEED, we conducted extensive experiments on five manipulation tasks with varying levels of complexity. Our results show that SEED significantly outperforms state-of-the-art RL algorithms in sample efficiency and safety. In addition, SEED also exhibits a substantial reduction of human effort compared to other RLHF methods. Further details and video results can be found at https://seediros23.github. io/.
AisLSM: Revolutionizing the Compaction with Asynchronous I/Os for LSM-tree

The log-structured merge tree (LSM-tree) is widely employed to build key-value (KV) stores. LSM-tree organizes multiple levels in memory and on disk. The compaction of LSM-tree, which is used to redeploy KV pairs between on-disk levels in the form of SST files, severely stalls its foreground service. We overhaul and analyze the procedure of compaction. Writing and persisting files with fsyncs for compacted KV pairs are time-consuming and, more important, occur synchronously on the critical path of compaction. The user-space compaction thread of LSM-tree stays waiting for completion signals from a kernel-space thread that is processing file write and fsync I/Os. We accordingly design a new LSM-tree variant named AisLSM with an asynchronous I/O model. In short, AisLSM conducts asynchronous writes and fsyncs for SST files generated in a compaction and overlaps CPU computations with disk I/Os for consecutive compactions. AisLSM tracks the generation dependency between input and output files for each compaction and utilizes a deferred check-up strategy to ensure the durability of compacted KV pairs. We prototype AisLSM with RocksDB and io_uring. Experiments show that AisLSM boosts the performance of RocksDB by up to 2.14x, without losing data accessibility and consistency. It also outperforms state-of-the-art LSM-tree variants with significantly higher throughput and lower tail latency.