Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access

This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmitters. We consider an edge device equipped with a camera that is taking images of potential objects such as vehicles that may harbor transmitters. Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not. To achieve this, we employ task-oriented communications, utilizing an encoder at the transmitter for joint source coding, channel coding, and modulation. This architecture efficiently transmits essential information of reduced dimension for object classification. Simultaneously, the transmitted signals may reflect off objects and return to the transmitter, allowing for the collection of target sensing data. Then the collected sensing data undergoes a second round of encoding at the transmitter, with the reduced-dimensional information communicated back to the fusion center through task-oriented communications. On the receiver side, a decoder performs the task of identifying a transmitter by fusing data received through joint sensing and task-oriented communications. The two encoders at the transmitter and the decoder at the receiver are jointly trained, enabling a seamless integration of image classification and wireless signal detection. Using AWGN and Rayleigh channel models, we demonstrate the effectiveness of the proposed approach, showcasing high accuracy in transmitter identification across diverse channel conditions while sustaining low latency in decision making.
Gaussian Splatting with NeRF-based Color and Opacity

Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of neural networks to capture the intricacies of 3D objects. By encoding the shape and color information within neural network weights, NeRFs excel at producing strikingly sharp novel views of 3D objects. Recently, numerous generalizations of NeRFs utilizing generative models have emerged, expanding its versatility. In contrast, Gaussian Splatting (GS) offers a similar render quality with faster training and inference as it does not need neural networks to work. We encode information about the 3D objects in the set of Gaussian distributions that can be rendered in 3D similarly to classical meshes. Unfortunately, GS are difficult to condition since they usually require circa hundred thousand Gaussian components. To mitigate the caveats of both models, we propose a hybrid model Viewing Direction Gaussian Splatting (VDGS) that uses GS representation of the 3D object's shape and NeRF-based encoding of color and opacity. Our model uses Gaussian distributions with trainable positions (i.e. means of Gaussian), shape (i.e. covariance of Gaussian), color and opacity, and neural network, which takes parameters of Gaussian and viewing direction to produce changes in color and opacity. Consequently, our model better describes shadows, light reflections, and transparency of 3D objects.
Evaluating individualized treatment effect predictions: a model-based perspective on discrimination and calibration assessment

In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined as based on the potential outcomes framework, which facilitates a comparison of existing and novel measures. In particular, we examine existing measures of measures of discrimination for benefit (variations of the c-for-benefit), and propose model-based extensions to the treatment effect setting for discrimination and calibration metrics that have a strong basis in outcome risk prediction. The main focus is on randomized trial data with binary endpoints and on models that provide individualized treatment effect predictions and potential outcome predictions. We use simulated data to provide insight into the characteristics of the examined discrimination and calibration statistics under consideration, and further illustrate all methods in a trial of acute ischemic stroke treatment. The results show that the proposed model-based statistics had the best characteristics in terms of bias and accuracy. While resampling methods adjusted for the optimism of performance estimates in the development data, they had a high variance across replications that limited their accuracy. Therefore, individualized treatment effect models are best validated in independent data. To aid implementation, a software implementation of the proposed methods was made available in R.
Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments

The generation of cold atom clouds is a complex process which involves the optimization of noisy data in high dimensional parameter spaces. Optimization can be challenging both in and especially outside of the lab due to lack of time, expertise, or access for lengthy manual optimization. In recent years, it was demonstrated that machine learning offers a solution since it can optimize high dimensional problems quickly, without knowledge of the experiment itself. In this paper we present results showing the benchmarking of nine different optimization techniques and implementations, alongside their ability to optimize a Rubidium (Rb) cold atom experiment. The investigations are performed on a 3D $^{87}$Rb molasses with 10 and 18 adjustable parameters, respectively, where the atom number obtained by absorption imaging was chosen as the test problem. We further compare the best performing optimizers under different effective noise conditions by reducing the Signal-to-Noise ratio of the images via adapting the atomic vapor pressure in the 2D+ MOT and the detection laser frequency stability.
Spatially Adaptive Variable Screening in Presurgical fMRI Data Analysis

Accurate delineation of tumor-adjacent functional brain regions is essential for planning function-preserving neurosurgery. Functional magnetic resonance imaging (fMRI) is increasingly used for presurgical counseling and planning. When analyzing presurgical fMRI data, false negatives are more dangerous to the patients than false positives because patients are more likely to experience significant harm from failing to identify functional regions and subsequently resecting critical tissues. In this paper, we propose a novel spatially adaptive variable screening procedure to enable effective control of false negatives while leveraging the spatial structure of fMRI data. Compared to existing statistical methods in fMRI data analysis, the new procedure directly controls false negatives at a desirable level and is completely data-driven. The new method is also substantially different from existing false-negative control procedures which do not take spatial information into account. Numerical examples show that the new method outperforms several state-of-the-art methods in retaining signal voxels, especially the subtle ones at the boundaries of functional regions, while providing cleaner separation of functional regions from background noise. Such results could be valuable to preserve critical tissues in neurosurgery.
Neural Contextual Bandits for Personalized Recommendation

In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning, the quest for adaptive, user-centric recommendations has led to the emergence of the formulation of contextual bandits. This tutorial investigates the contextual bandits as a powerful framework for personalized recommendations. We delve into the challenges, advanced algorithms and theories, collaborative strategies, and open challenges and future prospects within this field. Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i.e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.
Quantum Dynamics Happens Only on Paper: QBism's Account of Decoherence

QBism has long recognized quantum states, POVM elements, Kraus operators, and even unitary operations to be cut from the same cloth: They all express aspects of an agent's personal belief system concerning the consequences (for her) of actions she might take on her external world. Such action-consequence pairs have conventionally been called"quantum measurements. "The calculus of quantum theory is then viewed as an empirically motivated addition to Bayesian decision theory when brought to this notion of measurement. This radical approach has allowed QBism to eliminate the conceptual problems that plague other interpretations of quantum mechanics. However, one issue has remained elusive: If a QBist agent does not believe in the existence of an ontic (agent-independent) dynamical variable evolving over time, why would there be any constraints on her quantum state assignment in the absence of performing a measurement? Why would she introduce unitary or open-system quantum dynamics at all? Here, we present a representation theorem based on van Fraassen's reflection principle to answer these questions. Simply put, an agent's assignment of quantum dynamics represents her belief that a measurement action she is contemplating would not change her current odds for future gambles. A corollary to this approach is that one can make sense of"open-system dynamics"without ever introducing an"environment with a measurement record"as is common in decoherence accounts of quantum measurement. Instead, the QBist understanding of decoherence rests entirely on an agent's beliefs about the system of interest (not system plus environment) and her judgments about measurements she might perform on that system.
Enabling Secure Wireless Communications via Movable Antennas

A pioneering secure transmission scheme is proposed, which harnesses movable antennas (MAs) to optimize antenna positions for augmenting the physical layer security. Particularly, an MA-enabled secure wireless system is considered, where a multi-antenna transmitter communicates with a single-antenna receiver in the presence of an eavesdropper. The beamformer and antenna positions at the transmitter are jointly optimized under two criteria: power consumption minimization and secrecy rate maximization. For each scenario, a novel suboptimal algorithm was proposed to tackle the resulting nonconvex optimization problem, capitalizing on the approaches of alternating optimization and gradient descent. Numerical results demonstrate that the proposed MA systems significantly improve physical layer security compared to various benchmark schemes relying on conventional fixed-position antennas (FPAs).
Parameter dependence of entanglement spectra in quantum field theories

In this paper, we explore the characteristics of reduced density matrix spectra in quantum field theories. Previous studies mainly focus on the function $\mathcal{P}(\lambda):=\sum_i \delta(\lambda-\lambda_i)$, where $\lambda_i$ denote the eigenvalues of the reduced density matirx. We introduce a series of functions designed to capture the parameter dependencies of these spectra. These functions encompass information regarding the derivatives of eigenvalues concerning the parameters, notably including the function $\mathcal{P}_{\alpha_J}(\lambda):=\sum_i \frac{\partial \lambda_i }{\partial \alpha_J}\delta(\lambda-\lambda_i)$, where $\alpha_J$ denotes the specific parameter. Computation of these functions is achievable through the utilization of R\'enyi entropy. Intriguingly, we uncover compelling relationships among these functions and demonstrate their utility in constructing the eigenvalues of reduced density matrices for select cases. We perform computations of these functions across several illustrative examples. Specially, we conducted a detailed study of the variations of $\mathcal{P}(\lambda)$ and $\mathcal{P}_{\alpha_J}(\lambda)$ under general perturbation, elucidating their physical implications. In the context of holographic theory, we ascertain that the zero point of the function $\mathcal{P}_{\alpha_J}(\lambda)$ possesses universality, determined as $\lambda_0=e^{-S}$, where $S$ denotes the entanglement entropy of the reduced density matrix. Furthermore, we exhibit potential applications of these functions in analyzing the properties of entanglement entropy.
Market-Adaptive Ratio for Portfolio Management

This paper explores the limitations of existing risk-adjusted returns in portfolio management and introduces a novel metric, the Market-adaptive ratio, to address these shortcomings. Existing risk-adjusted returns neglect the differences between bear and bull markets. Acknowledging that these market conditions demand distinct strategies, the Market-adaptive ratio incorporates the unique attributes of each, enhancing the portfolio performance. By emphasizing the significance of market type in impacting investment outcomes, this novel metric empowers investors to refine their strategies accordingly.
A new criterion for interpreting acoustic emission damage signals in condition monitoring based on the distribution of cluster onsets

Structural Health Monitoring (SHM) relies on non-destructive techniques such as Acoustic Emission (AE) which provide a large amount of data over the life of the systems. The analysis of these data is often based on clustering in order to get insights about damage evolution. In order to evaluate clustering results, current approaches include Clustering Validity Indices (CVI) which favor compact and separable clusters. However, these shape-based criteria are not specific to AE data and SHM. This paper proposes a new approach based on the sequentiality of clusters onsets. For monitoring purposes, onsets indicate when potential damage occurs for the first time and allows to detect the inititation of the defects. The proposed CVI relies on the Kullback-Leibler divergence and enables to incorporate prior on damage onsets when available. Three experiments on real-world data sets demonstrate the relevance of the proposed approach. The first benchmark concerns the detection of the loosening of bolted plates under vibration. The proposed onset-based CVI outperforms the standard approach in terms of both cluster quality and accuracy in detecting changes in loosening. The second application involves micro-drilling of hard materials using Electrical Discharge Machining. In this industrial application, it is demonstrated that the proposed CVI can be used to evaluate the electrode progression until the reference depth which is essential to ensure structural integrity. Lastly, the third application is about the damage monitoring in a composite/metal hybrid joint structure. As an important result, the timeline of clusters generated by the proposed CVI is used to draw a scenario that accounts for the occurrence of slippage leading to a critical failure.
Identification of novel genes involved in phosphate accumulation in Lotus japonicus through Genome Wide Association mapping of root system architecture and anion content

Phosphate is a key nutrient for plants and as it is needed in high quantities. It is highly immobile in the soil and represents a major limiting factor for plant productivity. Plants have evolved different solutions to forage the soil for phosphate and to adapt to phosphate limitation ranging from a profound tuning of their root system architecture and metabolic profile to the evolution of widespread mutualistic interactions, such as those with arbuscular mycorrhizal fungi (AM symbiosis). Despite the prevalence of AM symbiosis throughout land plants, most studies aimed at identifying genes that regulate plant responses to phosphate have been conducted in species incapable of AM symbiosis, such as Arabidopsis. Here we elucidated plant responses and their genetic basis to different phosphate levels in a plant species that is widely used as a model for AM symbiosis: Lotus japonicus. Rather than focusing on a single model strain, we measured root growth and anion content in response to different levels of phosphate in a large panel of Lotus japonicus natural accessions. This allowed us not only to uncover common as well as divergent responses within this species, but also enabled Genome Wide Association Studies by which we identified new genes regulating phosphate homeostasis in Lotus. Under low phosphate conditions, we uncovered a correlation between plant biomass and the decrease of plant phosphate concentration in plant tissues, suggesting a dilution effect. Altogether our data of the genetic and phenotypic variation within a species capable of AM complements studies that have been conducted in Arabidopsis, and advances our understanding of the continuum of genotype by phosphate level interaction that exists throughout dicot plants. Author Summary Phosphate represents a major limiting factor for plant productivity. Plants have evolved different solutions to adapt to phosphate limitation ranging from a profound tuning of their root system architecture and metabolic profile to the evolution of widespread mutualistic interactions, such as arbuscular mycorrhizal symbiosis. Here we elucidated plant responses and their genetic basis to different phosphate levels in model legume plant species, Lotus japonicus, a plant commonly used for studying arbuscular mycorhizal symbiosis. We investigated Lotus responses to phosphate levels by combining high throughput root system architecture phenotyping and nutrient measurements with a natural variation approach. We investigated relations between root phenotypic responses and nutrient accumulation and we uncovered, under low phosphate conditions, a correlation between plant biomass and the decrease of plant phosphate concentration in plant tissues, suggesting a dilution effect. By means of Genome Wide Association mapping and integration of multiple traits, we identified new genes regulating phosphate homeostasis in Lotus.
In-Context Reinforcement Learning for Variable Action Spaces

Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action space often requires data re-collection and model re-training, which can be costly for some applications. In our work, we show that it is possible to mitigate this issue by proposing the Headless-AD model that, despite being trained only once, is capable of generalizing to discrete action spaces of variable size, semantic content and order. By experimenting with Bernoulli and contextual bandits, as well as a gridworld environment, we show that Headless-AD exhibits significant capability to generalize to action spaces it has never encountered, even outperforming specialized models trained for a specific set of actions on several environment configurations.
Time Lower Bounds for the Metropolis Process and Simulated Annealing

The Metropolis process (MP) and Simulated Annealing (SA) are stochastic local search heuristics that are often used in solving combinatorial optimization problems. Despite significant interest, there are very few theoretical results regarding the quality of approximation obtained by MP and SA (with polynomially many iterations) for NP-hard optimization problems. We provide rigorous lower bounds for MP and SA with respect to the classical maximum independent set problem when the algorithms are initialized from the empty set. We establish the existence of a family of graphs for which both MP and SA fail to find approximate solutions in polynomial time. More specifically, we show that for any $\varepsilon \in (0,1)$ there are $n$-vertex graphs for which the probability SA (when limited to polynomially many iterations) will approximate the optimal solution within ratio $\Omega\left(\frac{1}{n^{1-\varepsilon}}\right)$ is exponentially small. Our lower bounds extend to graphs of constant average degree $d$, illustrating the failure of MP to achieve an approximation ratio of $\Omega\left(\frac{\log (d)}{d}\right)$ in polynomial time. In some cases, our impossibility results also go beyond Simulated Annealing and apply even when the temperature is chosen adaptively. Finally, we prove time lower bounds when the inputs to these algorithms are bipartite graphs, and even trees, which are known to admit polynomial-time algorithms for the independent set problem.
Finite Temperature Minimal Entangled Typical Thermal States Impurity Solver

We present a minimally entangled typical thermal state (METTS) quantum impurity solver for general multi-orbital systems at finite temperatures. We introduce an improved estimator for the single-particle Green's function that strongly reduces the large fluctuations at long imaginary time and low temperature, which were a severe limitation of the original algorithm. In combination with the fork tensor product states ansatz, we obtain a dynamical mean field theory (DMFT) quantum impurity solver, which we benchmark for single and three-band models down to low temperatures, including the effect of spin-orbit coupling in a realistic DMFT computation for the Hund's metal Sr$_2$RuO$_4$ down to low temperatures.
Typhoon: Thai Large Language Models

Typhoon is a series of Thai large language models (LLMs) developed specifically for the Thai language. This technical report presents challenges and insights in developing Thai LLMs, including data preparation, pretraining, instruction-tuning, and evaluation. As one of the challenges of low-resource languages is the amount of pretraining data, we apply continual training to transfer existing world knowledge from a strong LLM. To evaluate the Thai knowledge encapsulated in each model from the pretraining stage, we develop ThaiExam, a benchmark based on examinations for high-school students and investment professionals in Thailand. In addition, we fine-tune Typhoon to follow Thai instructions, and we evaluate instruction-tuned models on Thai instruction datasets as well as translation, summarization, and question-answering tasks. Experimental results on a suite of Thai benchmarks show that Typhoon outperforms all open-source Thai language models, and its performance is on par with GPT-3.5 in Thai while having only 7 billion parameters and being 2.62 times more efficient in tokenizing Thai text.