Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of coherence in quantum-dot-based nanomachines within the Coulomb blockade regime

During the last decades, quantum dots within the Coulomb blockade regime of transport have been proposed as essential building blocks for a wide variety of nanomachines. This includes thermoelectric devices, quantum shuttles, quantum pumps, and even quantum motors. However, in this regime, the role of quantum mechanics is commonly limited to provide energy quantization while the working principle of the devices is ultimately the same as their classic counterparts. Here, we study quantum-dot-based nanomachines in the Coulomb blockade regime, but in a configuration that resembles the quantum mechanics' paradigmatic experiment: the double-slit. We show that the coherent superposition of states appearing in this configuration can be used as the basis for different forms of "true" quantum machines. We analyze the efficiency of these machines against different non-equilibrium sources (bias voltage, temperature gradient, and external driving) and the factors that limit it, including decoherence and the role of the different orders appearing in the adiabatic expansion of the charge/heat currents.
Debye model for the surface phonons

A quantum description of the surface waves in an isotropic elastic body without the use of the semiclassical quantization is proposed. The problem about the surface waves is formulated in the Lagrangian and Hamiltonian representations. Within the framework of the generalized Debye model, the contribution of the surface phonons (“rayleighons”) to thermodynamic functions is calculated. It is emphasized that the role of the surface phonons can be significant and even decisive in lowdimensional systems, granular and porous media, and that their contribution to the total heat capacity increases with decreasing temperature.
Large-data determinantal clustering

Determinantal consensus clustering is a promising and attractive alternative to partitioning about medoids and k-means for ensemble clustering. Based on a determinantal point process or DPP sampling, it ensures that subsets of similar points are less likely to be selected as centroids. It favors more diverse subsets of points. The sampling algorithm of the determinantal point process requires the eigendecomposition of a Gram matrix. This becomes computationally intensive when the data size is very large. This is particularly an issue in consensus clustering, where a given clustering algorithm is run several times in order to produce a final consolidated clustering. We propose two efficient alternatives to carry out determinantal consensus clustering on large datasets. They consist in DPP sampling based on sparse and small kernel matrices whose eigenvalue distributions are close to that of the original Gram matrix.
Rihanna versus Bollywood: Twitter Influencers and the Indian Farmers' Protest

A tweet from popular entertainer and businesswoman, Rihanna, bringing attention to farmers' protests around Delhi set off heightened activity on Indian social media. An immediate consequence was the weighing in by Indian politicians, entertainers, media and other influencers on the issue. In this paper, we use data from Twitter and an archive of debunked misinformation stories to understand some of the patterns around influencer engagement with a political issue. We found that more followed influencers were less likely to come out in support of the tweet. We also find that the later engagement of major influencers on the side of the government's position shows suggestion's of collusion. Irrespective of their position on the issue, influencers who engaged saw a significant rise in their following after their tweets. While a number of tweets thanked Rihanna for raising awareness on the issue, she was systematically trolled on the grounds of her gender, race, nationality and religion. Finally, we observed how misinformation existing prior to the tweet set up the grounds for alternative narratives that emerged.
The harmonic theory on a vector bundle with singular Hermitian metrics and positivity

Let E be a holomorphic vector bundle endowed with a singular Hermitian metric H . In this paper, we develop the harmonic theory on (E,H). Then we extend several canonical results of J. Kollár and K. Takegoshi to this situation. In the end, we generalise Nakano’s vanishing theorem.
Causal version of Principle of Insufficient Reason and MaxEnt

The Principle of insufficient Reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. Maximum Entropy (MaxEnt) generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradox probability updates for joint distributions of cause and effect. This is because constraints on the conditional P(effect | cause) result in changes of P(cause) that assign higher probability to those values of the cause that offer more options for the effect, suggesting 'intentional behaviour'. Earlier work therefore suggested sequentially maximizing (conditional) entropy according to the causal order, but without further justification apart from plausibility for toy examples. We justify causal modifications of PIR and MaxEnt by separating constraints into restrictions for the cause and restrictions for the mechanism that generates the effect from the cause. We further sketch why Causal PIR also entails 'Information Geometric Causal Inference'. We briefly discuss problems of generalizing the causal version of MaxEnt to arbitrary causal DAGs.
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare

Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g.message passing methods are run differently from node embedding ones. Despite their apparent differences, all these methods utilize the graph structure, and therefore, their learning can be approximated with stochastic graph traversals. We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs. GTTF is founded upon a data structure (stored as a sparse tensor) and a stochastic graph traversal algorithm (described using tensor operations). The algorithm is a functional that accept two functions, and can be specialized to obtain a variety of GRL models and objectives, simply by changing those two functions. We show for a wide class of methods, our algorithm learns in an unbiased fashion and, in expectation, approximates the learning as if the specialized implementations were run directly. With these capabilities, we scale otherwise non-scalable methods to set state-of-the-art on large graph datasets while being more efficient than existing GRL libraries - with only a handful of lines of code for each method specialization. GTTF and its various GRL implementations are on: https://github.com/isi-usc-edu/gttf.
Switching Variational Auto-Encoders for Noise-Agnostic Audio-Visual Speech Enhancement

Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to train a generative model for speech, which at test time is combined with a noise model, e.g. nonnegative matrix factorization (NMF), whose parameters are learned without supervision. Consequently, the proposed model is agnostic to the noise type. When visual data are clean, audio-visual VAE-based architectures usually outperform the audio-only counterpart. The opposite happens when the visual data are corrupted by clutter, e.g. the speaker not facing the camera. In this paper, we propose to find the optimal combination of these two architectures through time. More precisely, we introduce the use of a latent sequential variable with Markovian dependencies to switch between different VAE architectures through time in an unsupervised manner: leading to switching variational auto-encoder (SwVAE). We propose a variational factorization to approximate the computationally intractable posterior distribution. We also derive the corresponding variational expectation-maximization algorithm to estimate the parameters of the model and enhance the speech signal. Our experiments demonstrate the promising performance of SwVAE.
Unsupervised Sentence-embeddings by Manifold Approximation and Projection

The concept of unsupervised universal sentence encoders has gained traction recently, wherein pre-trained models generate effective task-agnostic fixed-dimensional representations for phrases, sentences and paragraphs. Such methods are of varying complexity, from simple weighted-averages of word vectors to complex language-models based on bidirectional transformers. In this work we propose a novel technique to generate sentence-embeddings in an unsupervised fashion by projecting the sentences onto a fixed-dimensional manifold with the objective of preserving local neighbourhoods in the original space. To delineate such neighbourhoods we experiment with several set-distance metrics, including the recently proposed Word Mover’s distance, while the fixed-dimensional projection is achieved by employing a scalable and efficient manifold approximation method rooted in topological data analysis. We test our approach, which we term EMAP or Embeddings by Manifold Approximation and Projection, on six publicly available text-classification datasets of varying size and complexity. Empirical results show that our method consistently performs similar to or better than several alternative state-of-the-art approaches.
Robust Principal Component Analysis: A Median of Means Approach

Principal component analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in statistics, machine learning, computer vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This article proposes a PCA procedure based on the MoM principle. Called the MoMPCA, the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the nonasymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Testing and evaluation is a crucial step in the development and deployment of connected and automated vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is necessary to test the CAVs in safety-critical scenarios, which rarely happen in a naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put into decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high-dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on the Markov decision process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.
A Probabilistic Interpretation of Motion Correlation Selection Techniques

Motion correlation interfaces are those that present targets moving in different patterns, which the user can select by matching their motion. In this paper, we re-formulate the task of target selection as a probabilistic inference problem. We demonstrate that previous interaction techniques can be modelled using a Bayesian approach and that how modelling the selection task as transmission of information can help us make explicit the assumptions behind similarity measures. We propose ways of incorporating uncertainty into the decision-making process and demonstrate how the concept of entropy can illuminate the measurement of the quality of a design. We apply these techniques in a case study and suggest guidelines for future work.
Robust propagation of internal coastal Kelvin waves in complex domains

Internal coastal Kelvin waves were experimentally generated in a two-layer fluid system on a rotating table. Waves are exponentially localized near the tank boundary and propagate in the same direction as the table rotation along boundaries of complex geometries without being scattered. Our experiments suggest a connection between these unusual wave characteristics and topological properties of the underlying governing equations.
MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multi-metric linear least square iterative closest point algorithm. Point-to-point (plane, line) error metrics within each point class are jointly optimized with a linear approximation to estimate the ego-motion. Static feature points of the registered frame are appended into the local map to keep it updated. For the back-end, hierarchical pose graph optimization is conducted among regularly stored history submaps to reduce the drift resulting from dead reckoning. Extensive experiments are carried out on three datasets with more than 100,000 frames collected by seven types of LiDAR on various outdoor and indoor scenarios. On the KITTI benchmark, MULLS ranks among the top LiDAR-only SLAM systems with real-time performance.
Empowering Investigative Journalism with Graph-based Heterogeneous Data Management

Investigative Journalism (IJ, in short) is staple of modern, democratic societies. IJ often necessitates working with large, dynamic sets of heterogeneous, schema-less data sources, which can be structured, semi-structured, or textual, limiting the applicability of classical data integration approaches. In prior work, we have developed ConnectionLens, a system capable of integrating such sources into a single heterogeneous graph, leveraging Information Extraction (IE) techniques; users can then query the graph by means of keywords, and explore query results and their neighborhood using an interactive GUI. Our keyword search problem is complicated by the graph heterogeneity, and by the lack of a result score function that would allow to prune some of the search space. In this work, we describe an actual IJ application studying conflicts of interest in the biomedical domain, and we show how ConnectionLens supports it. Then, we present novel techniques addressing the scalability challenges raised by this application: one allows to reduce the significant IE costs while building the graph, while the other is a novel, parallel, in-memory keyword search engine, which achieves orders of magnitude speed-up over our previous engine. Our experimental study on the real-world IJ application data confirms the benefits of our contributions.
Analysis of Tumor-infiltrating Lymphocytes Following CD45 Enrichment

[Abstract] Measuring antigen-specific T cell responses in the blood and lymphoid organs of vaccinated mice can give us a useful indication of the potency of a vaccine formulation. Unfortunately, systemic or even localized lymphoid T cell responses are not always predictive of the ability of a vaccine to induce tumor protection. Measuring the antigen-specific T cell response within the tumor infiltrating lymphocytes is a more accurate indicator or vaccine efficacy. However, multi-parameter flow cytometric analysis of T cells isolated from tumor tissue can be quite challenging due to the over-whelming number of tumor cells present in relation to the tumor infiltrating lymphocytes (TIL) and to problems associated to the large and adhesive nature of many tumor cell. Here we take advantage of a pre-flow separation of CD45 + leukocytes from the tumor tissue using the MACS magnetic cell sorting system, resulting in a much cleaner cell preparation with which to proceed to flow cytometric staining and analysis.
Mesh-robustness of the variable steps BDF2 method for the Cahn-Hilliard model

The two-step backward differential formula (BDF2) implicit method with unequal time-steps is investigated for the Cahn-Hilliard model by focusing on the numerical influences of time-step variations. The suggested method is proved to preserve a modified energy dissipation law at the discrete levels if the adjoint time-step ratios fulfill a new step-ratio restriction $0<r_k:=\tau_k/\tau_{k-1}\le r_{\mathrm{user}}$ ($r_{\mathrm{user}}$ can be chosen by the user such that $r_{\mathrm{user}}<4.864$), such that it is mesh-robustly stable in an energy norm. We view the BDF2 formula as a convolution approximation of the first time derivative and perform the error analysis by using the recent suggested discrete orthogonal convolution kernels. By developing some novel convolution embedding inequalities with respect to the orthogonal convolution kernels, an $L^2$ norm error estimate is established at the first time under the updated step-ratio restriction $0<r_k\le r_{\mathrm{user}}$. The time-stepping scheme is mesh-robustly convergent in the sense that the convergence constant (prefactor) in the error estimate is independent of the adjoint time-step ratios. On the basis of ample tests on random time meshes, a useful adaptive time-stepping strategy is applied to efficiently capture the multi-scale behaviors and to accelerate the long-time simulation approaching the steady state.
Self-gravitating Equilibria of Non-minimally Coupled Dark Matter Halos

We investigate self-gravitating equilibria of halos constituted by dark matter (DM) non-minimally coupled to gravity. In particular, we consider a theoretically motivated non-minimal coupling that may arise when the averaging/coherence length L associated with the fluid description of the DM collective behavior is comparable to the local curvature scale. In the Newtonian limit, such a non-minimal coupling amounts to a modification of the Poisson equation by a term L 2∇2 ρ proportional to the Laplacian of the DM density ρ itself. We further adopt a general power-law equation of state p ∝ ρ Γ r α relating the DM dynamical pressure p to density ρ and radius r, as expected for phase-space density stratification during the gravitational assembly of halos in a cosmological context. We confirm previous findings that, in the absence of non-minimal coupling, the resulting density ρ(r) features a steep central cusp and an overall shape mirroring the outcomes of N-body simulations in the standard ΛCDM cosmology, as described by the classic Navarro–Frenk–White or Einasto profiles. Most importantly, we find that the non-minimal coupling causes the density distribution to develop an inner core and a shape that closely follows the Burkert profile out to several core scale radii. In fact, we highlight that the resulting mass distributions can fit, with an accuracy comparable to Burkert’s one, the coadded rotation curves of dwarf, DM-dominated galaxies. Finally, we show that non-minimally coupled DM halos are consistent with the observed scaling relation between the core radius r 0 and core density ρ 0, in terms of a universal core surface density ρ 0 × r 0 among different galaxies.
Speaker and Direction Inferred Dual-Channel Speech Separation

Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough generalization capabilities for real scenarios where the number of input sounds is usually uncertain and even dynamic. In this work, we employ ideas from auditory attention with two ears and propose a speaker and direction inferred speech separation network (dubbed SDNet) to solve the cocktail party problem. Specifically, our SDNet first parses out the respective perceptual representations with their speaker and direction characteristics from the mixture of the scene in a sequential manner. Then, the perceptual representations are utilized to attend to each corresponding speech. Our model generates more precise perceptual representations with the help of spatial features and successfully deals with the problem of the unknown number of sources and the selection of outputs. The experiments on standard fully-overlapped speech separation benchmarks, WSJ0-2mix, WSJ0-3mix, and WSJ0-2&3mix, show the effectiveness, and our method achieves SDR improvements of 25.31 dB, 17.26 dB, and 21.56 dB under anechoic settings. Our codes will be released at https://github.com/aispeech-lab/SDNet.
Spike-based Residual Blocks

Deep Spiking Neural Networks (SNNs) are harder to train than ANNs because of their discrete binary activation and spatio-temporal domain error back-propagation. Considering the huge success of ResNet in ANNs’ deep learning, it is natural to attempt to use residual learning to train deep SNNs. Previous Spiking ResNet used a similar residual block to the standard block of ResNet, which we regard as inadequate for SNNs and which still causes the degradation problem. In this paper, we propose the spike-element-wise (SEW) residual block and prove that it can easily implement the residual learning. We evaluate our SEW ResNet on ImageNet. The experiment results show that the SEW ResNet can obtain higher performance by simply adding more layers, providing a simple method to train deep SNNs.