Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of population structure inferred by principal component analysis or the admixture model

Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome wide human data we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.
FDD Massive MIMO Without CSI Feedback

Transmitter channel state information (CSIT) is indispensable for the spectral efficiency gains offered by massive multiple-input multiple-output (MIMO) systems. In a frequency-division-duplexing (FDD) massive MIMO system, CSIT is typically acquired through downlink channel estimation and user feedback, but as the number of antennas increases, the overhead for CSI training and feedback per user grows, leading to a decrease in spectral efficiency. In this paper, we show that, using uplink pilots in FDD, the downlink sum spectral efficiency gain with perfect downlink CSIT is achievable when the number of antennas at a base station is infinite under some mild channel conditions. The key idea showing our result is the mean squared error-optimal downlink channel reconstruction method using uplink pilots, which exploits the geometry reciprocity of uplink and downlink channels. We also present a robust downlink precoding method harnessing the reconstructed channel with the error covariance matrix. Our system-level simulations show that our proposed precoding method can attain comparable sum spectral efficiency to zero-forcing precoding with perfect downlink CSIT, without CSI training and feedback.
Learning by Asking for Embodied Visual Navigation and Task Completion

The research community has shown increasing interest in designing intelligent embodied agents that can assist humans in accomplishing tasks. Despite recent progress on related vision-language benchmarks, most prior work has focused on building agents that follow instructions rather than endowing agents the ability to ask questions to actively resolve ambiguities arising naturally in embodied environments. To empower embodied agents with the ability to interact with humans, in this work, we propose an Embodied Learning-By-Asking (ELBA) model that learns when and what questions to ask to dynamically acquire additional information for completing the task. We evaluate our model on the TEACH vision-dialog navigation and task completion dataset. Experimental results show that ELBA achieves improved task performance compared to baseline models without question-answering capabilities.
Operator and diagram techniques for solving intensity moment equations in random media: Application to strong scattering

The moment equations for the intensity of an electromagnetic wave propagating in an almost-transparent extended random medium are cast in an integral-operator form. Transport equations and iterative series are derived. Solutions in several limits are given, and series of correction terms are discussed with use of diagram techniques. For simplicity, the treatment is restricted to plane waves, uniform isotropic turbulence, and only to the intensity covariance as far as correction terms are concerned.
Robust trajectory optimisation for transitions of tiltwing VTOL aircraft

We propose a method to generate robust and optimal trajectories for the transition of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft leveraging concepts from convex optimisation, tube-based nonlinear Model Predictive Control (MPC) and Difference of Convex (DC) functions decomposition. The approach relies on computing DC decompositions of dynamic models in order to exploit convexity properties and develop a tractable robust optimisation that solves a sequence of convex programs converging to a local optimum of the trajectory generation problem. The algorithm developed is applied to an Urban Air Mobility case study. The resulting solutions are robust to approximation errors in dynamic models and provide safe trajectories for aggressive transition manoeuvres at constant altitude.
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning

Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained the state-of-the-art results in image-to-text generation. However, these models store all the knowledge within their parameters, thus often requiring enormous model parameters to model the abundant visual concepts and very rich textual descriptions. Additionally, they are inefficient in incorporating new data, requiring a computational-expensive fine-tuning process. In this work, we introduce a Retrieval-augmented Visual Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the relevant knowledge from the external database for zero and in-context few-shot image-to-text generations. By storing certain knowledge explicitly in the external database, our approach reduces the number of model parameters and can easily accommodate new data during evaluation by simply updating the database. We also construct an interleaved image and text data that facilitates in-context few-shot learning capabilities. We demonstrate that Re-ViLM significantly boosts performance for image-to-text generation tasks, especially for zero-shot and few-shot generation in out-of-domain settings with 4 times less parameters compared with baseline methods.
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning

Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effective pre-training technique for various downstream tasks in terms of sample efficiency, systematic generalization, and reasoning. Although image-based reinforcement learning (RL) is one of the most important and thus frequently mentioned such downstream tasks, the benefit in RL has surprisingly not been investigated systematically thus far. Instead, most of the evaluations have focused on rather indirect metrics such as segmentation quality and object property prediction accuracy. In this paper, we investigate the effectiveness of OCR pre-training for image-based reinforcement learning via empirical experiments. For systematic evaluation, we introduce a simple object-centric visual RL benchmark and conduct experiments to answer questions such as ``Does OCR pre-training improve performance on object-centric tasks? '' and ``Can OCR pre-training help with out-of-distribution generalization? ''. Our results provide empirical evidence for valuable insights into the effectiveness of OCR pre-training for RL and the potential limitations of its use in certain scenarios. Additionally, this study also examines the critical aspects of incorporating OCR pre-training in RL, including performance in a visually complex environment and the appropriate pooling layer to aggregate the object representations.
Strengthening of Mg–Al–Ca alloys with C15 and C36 Laves phases

Laves phases such as CaMg2 (C14), Ca(Al,Mg)2 (C36), and CaAl2 (C15) have high melting points, high hardness at room and elevated temperatures, but are inherently brittle. Cast Mg–Al–Ca alloys having Laves phase skeleton thus have good creep properties but limited ductility. An understanding of the co-deformation behaviour of α-Mg and Laves phases is essential for further improving the mechanical performance of these alloys. Here, we study the mechanical behaviour of a Mg–4.65Al–2.82Ca alloy using micropillar compression in the α-Mg matrix, at α-Mg/C36 and α-Mg/C15 interfaces and in the C15 phase in combination with scanning electron microscopy. We show that both, C15 and C36 phases, provide considerable strengthening to the α-Mg matrix. The amount of strengthening primarily depends on the volume fraction and geometry of Laves phases in composite micropillars and correlates well with the iso-strain and iso-stress bounds predicted for α-Mg/C36 and α-Mg/C15 composites.
A globally convergent SQP-type method with least constraint violation for nonlinear semidefinite programming

We present a globally convergent SQP-type method with the least constraint violation for nonlinear semidefinite programming. The proposed algorithm employs a two-phase strategy coupled with a line search technique. In the first phase, a subproblem based on a local model of infeasibility is formulated to determine a corrective step. In the second phase, a search direction that moves toward optimality is computed by minimizing a local model of the objective function. Importantly, regardless of the feasibility of the original problem, the iterative sequence generated by our proposed method converges to a Fritz-John point of a transformed problem, wherein the constraint violation is minimized. Numerical experiments have been conducted on various complex scenarios to demonstrate the effectiveness of our approach.
Analysis of business process automation as linear time-invariant system network

In this work, we examined Business Process (BP) production as a signal; this novel approach explores a BP workflow as a linear time-invariant (LTI) system. We analysed BP productivity in the frequency domain; this standpoint examines how labour and capital act as BP input signals and how their fundamental frequencies affect BP production. Our research also proposes a simulation framework of a BP in the frequency domain for estimating productivity gains due to the introduction of automation steps. Our ultimate goal was to supply evidence to address Solow's Paradox.
Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimized Retrieval Framework

The chromatic contamination that arises from photospheric heterogeneities, e.g., spots and faculae on the host star presents a significant noise source for exoplanet transmission spectra. If this contamination is not corrected for, it can introduce substantial bias in our analysis of the planetary atmosphere. We utilize two stellar models of differing complexity, StARPA (Stellar Activity Removal for Planetary Atmospheres) and ASteRA (Active Stellar Retrieval Algorithm), to explore the biases introduced by stellar contamination in retrieval under differing degrees of stellar activity. We use the retrieval framework TauREx3 and a grid of 27 synthetic, spot-contaminated transmission spectra to investigate potential biases and to determine how complex our stellar models must be in order to accurately extract the planetary parameters from transmission spectra. The input observation is generated using the more complex model (StARPA), in which the spot latitude is an additional, fixable parameter. This observation is then fed into a combined stellar-planetary retrieval, which contains a simplified stellar model (ASteRA). Our results confirm that the inclusion of stellar activity parameters in retrieval minimizes bias under all activity regimes considered. ASteRA performs very well under low-to-moderate activity conditions, retrieving the planetary parameters with a high degree of accuracy. For the most active cases, characterized by larger, higher-temperature contrast spots, some minor residual bias remains due to ASteRA neglecting the interplay between the spot and the limb-darkening effect. As a result of this, we find larger errors in retrieved planetary parameters for central spots (0°) and those found close to the limb (60°) than those at intermediate latitudes (30°).
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit interactions (e.g., purchasing and clicking). Humans perceive the world by processing the modality signals (e.g., audio, text and image), which inspired researchers to build a recommender system that can understand and interpret data from different modalities. Those models could capture the hidden relations between different modalities and possibly recover the complementary information which can not be captured by a uni-modal approach and implicit interactions. The goal of this survey is to provide a comprehensive review of the recent research efforts on the multimodal recommendation. Specifically, it shows a clear pipeline with commonly used techniques in each step and classifies the models by the methods used. Additionally, a code framework has been designed that helps researchers new in this area to understand the principles and techniques, and easily runs the SOTA models. Our framework is located at: https://github.com/enoche/MMRec
Exact computations with quasiseparable matrices

Quasiseparable matrices are a class of rank-structured matrices widely used in numerical linear algebra and of growing interest in computer algebra, with applications in e.g. the linearization of polynomial matrices. Various representation formats exist for these matrices that have rarely been compared. We show how the most central formats SSS and HSS can be adapted to symbolic computation, where the exact rank replaces threshold based numerical ranks. We clarify their links and compare them with the Bruhat format. To this end, we state their space and time cost estimates based on fast matrix multiplication, and compare them, with their leading constants. The comparison is supported by software experiments. We make further progresses for the Bruhat format, for which we give a generation algorithm, following a Crout elimination scheme, which specializes into fast algorithms for the construction from a sparse matrix or from the sum of Bruhat representations.
Sedimentation of particles with very small inertia in Stokes flows I: convergence to the transport-Stokes equations

We consider the sedimentation of $N$ spherical particles with identical radii $R$ in a Stokes flow in $\mathbb R^3$. The particles satisfy a no-slip boundary condition and are subject to constant gravity. The dynamics of the particles is modeled by Newton's law but with very small particle inertia as $N$ tends to infinity and $R$ to $0$. In a mean-field scaling, we show that the particle evolution is well approximated by the transport-Stokes system which has been derived previously as the mean-field limit of inertialess particles. In particular this justifies to neglect the particle inertia in the microscopic system, which is a typical modelling assumption in this and related contexts. The proof is based on a relative energy argument that exploits the coercivity of the particle forces with respect to the particle velocities in a Stokes flow. We combine this with an adaptation of Hauray's method for mean-field limits to $2$-Wasserstein distances. Moreover, in order to control the minimal distance between particles, we prove a representation of the particle forces. This representation makes the heuristic \enquote{Stokes law} rigorous that the force on each particle is proportional to the difference of the velocity of the individual particle and the mean-field fluid velocity generated by the other particles.
Learning Dynamical Systems by Leveraging Data from Similar Systems

We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide a finite sample error bound of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide a data-dependent bound that is computable when some prior knowledge about the systems is available. This bound can also be used to determine the weight that should be assigned to the auxiliary data during the model training stage.
BLO spaces associated with Laguerre polynomials expansions

In this paper we introduce spaces of $\textup{BLO}$-type related to Laguerre polynomial expansions. We consider the probability measure on $(0,\infty)$ defined by $d\gamma_\alpha(x)=\frac{2}{\Gamma(\alpha+1)}e^{-x^2}x^{2\alpha+1}dx$ with $\alpha>-\frac12$. For every $a>0$, the space $\textup{BLO}_a((0,\infty),\gamma_\alpha)$ consists of all those measurable functions defined on $(0,\infty)$ having bounded lower oscillation with respect to $\gamma_\alpha$ over an admissible family $\mathcal{B}_a$ of intervals in $(0,\infty)$. The space $\textup{BLO}_a((0,\infty),\gamma_\alpha)$ is a subspace of the space $\textup{BMO}_a((0,\infty),\gamma_\alpha)$ of bounded mean oscillation functions with respect to $\gamma_\alpha$ and $\mathcal{B}_a$. The natural $a$-local centered maximal function defined by $\gamma_\alpha$ is bounded from $\textup{BMO}_a((0,\infty),\gamma_\alpha)$ into $\textup{BLO}_a((0,\infty),\gamma_\alpha)$. We prove that the maximal operator, the $\rho$-variation and the oscillation operators associated with local truncations of the Riesz transforms in the Laguerre setting are bounded from $L^\infty((0,\infty),\gamma_\alpha)$ into $\textup{BLO}_a((0,\infty),\gamma_\alpha)$. Also, we obtain a similar result for the maximal operator of local truncations for spectral Laplace transform type multipliers.
Efficient Attention via Control Variates

Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied. Built upon previous progress of RFA, we characterize this gap through the lens of control variates and show that RFA can be decomposed into a sum of multiple control variate estimators for each element in the sequence. This new framework reveals that exact softmax attention can be recovered from RFA by manipulating each control variate. Besides, it allows us to develop a more flexible form of control variates, resulting in a novel attention mechanism that significantly reduces the approximation gap while maintaining linear complexity. Extensive experiments demonstrate that our model outperforms state-of-the-art efficient attention mechanisms on both vision and language tasks.
Joint source-channel coding of uniform memoryless sources over binary symmetric channels

A joint design of source and channel coding is considered for digital transmission over a binary symmetric channel (BSC). Three major techniques are used. For a binary source, channel optimized coder design (COCD) is proposed. This method has non-binary decoding values and minimizes the MSE distortion measure. It can be understood as a channel coder specifically designed to be robust under transmission errors. Then, this tool is applied to the transmission of a uniform source. This is done through a bitwise decomposition structure in which the samples of a uniform source are first expressed in binary representation and bits of the same weight are encoded together. Finally, a practical optimization algorithm is explained that delivers the best set of coders, for a given raw error probability (BER) of the channel.
An Information-Theoretic Analysis of Nonstationary Bandit Learning

In nonstationary bandit learning problems, the decision-maker must continually gather information and adapt their action selection as the latent state of the environment evolves. In each time period, some latent optimal action maximizes expected reward under the environment state. We view the optimal action sequence as a stochastic process, and take an information-theoretic approach to analyze attainable performance. We bound limiting per-period regret in terms of the entropy rate of the optimal action process. The bound applies to a wide array of problems studied in the literature and reflects the problem's information structure through its information-ratio.
Scalarization of planar anti–de Sitter charged black holes in Einstein-Maxwell-scalar theory

We construct scalarized planar charged black holes in Einstein-Maxwell-scalar (EMS) theory with the presence of a negative cosmological constant. Domains of existence of black hole solutions are given in term of nonminimally coupling constant $\alpha$. Perturbative stability of a scalarized black hole is investigated by calculating its quasinormal modes. Thermodynamic properties of the scalarized planar solution are also discussed. We observe no evidence of instability of the scalarized black holes. Moreover, we find that scalarized planar charged AdS black holes are thermodynamically preferred over scalar-free solutions in grand canonical and canonical ensembles. The transition between scalar-free solutions and scalarized solutions is found to be the thermal second order phase transition. The transition of these solutions shares some similar features with conductor-superconductor phase transition.
Complex field reversal dynamics in nanomagnetic systems

Nanomagnetic materials, built from thin, patterned films of ferromagnetic materials, began as analogues to frustrated magnetism. Their low energy of operation and emergent properties make them strong candidates for physics-based devices. A recent model of how nanomagnetic domains flip, the Glauber mean-field model, is used here to understand how systems of nanomagnets evolve when opposed by external field. This reversal can be expressed in an analytical form in the case of one-dimensional chains and trees at zero temperature, where the cascade of spin flips gives rise to harmonic power spectra. The same cascades in two and three dimensions form fractal field reversal clusters whose shape depends on the strength of the field and the tuning of interactions between nanomagnets.
A Nonparametric Approach to the Truncated Regression Problem

Abstract A description is given of a new method of estimating the regression parameters in the linear regression model from data where the dependent variable is subject to truncation. The residual distribution is allowed to be unspecified. The method is iterative and involves estimation of the residual distribution under the truncated sampling scheme. The technique can be interpreted as an iterative bias adjustment of the observations in order to correct the regression relationship in the sampled population to match that of the model. A simulation study compares the performance of various estimators, including one suggested by Bhattacharya, Chernoff, and Yang (1983). This truncation regression problem arises in many contexts of scientific and social research. In economics Tobin (1958) analyzed household expenditure on durable goods using a regression model that took account of the fact that the expenditure is always nonnegative. A more general situation was studied by Hausman and Wise (1976, 1977) in conn.