Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Cognition for Target Tracking in Cognitive Radar Networks

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel. We provide several reward functions which capture different aspects of the dynamic radar scenario and describe the online machine learning algorithms which are applicable to this structure. In addition, we study varying levels of feedback, where central coordinator update rates vary. We compare our algorithms against baselines and demonstrate dramatic improvements in convergence time over the prior art. A network using hybrid cognition is able to use a minimal amount of feedback to achieve much faster convergence times and therefore lower tracking error.
Neural Relation Graph for Identifying Problematic Data

Diagnosing and cleaning datasets are crucial for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions is difﬁcult due to the presence of complex issues, such as label errors or under-representation of certain types. In this paper, we propose a novel approach for identifying problematic data by utilizing a largely ignored source of information: a relational structure of data in the feature-embedded space. We develop an efﬁcient algorithm for detecting label errors and outlier data points based on the relational graph structure of the dataset. We further introduce a visualization tool for contextualizing data points, which can serve as an effective tool for interactively diagnosing datasets. We evaluate label error and out-of-distribution detection performances on large-scale image and language domain tasks, including ImageNet and GLUE benchmarks, and demonstrate the effectiveness of our approach for debugging datasets and building robust machine learning systems.
G-formula for causal inference via multiple imputation

G-formula is a popular approach for estimating treatment or exposure effects from longitudinal data that are subject to time-varying confounding. G-formula estimation is typically performed by Monte-Carlo simulation, with non-parametric bootstrapping used for inference. We show that G-formula can be implemented by exploiting existing methods for multiple imputation (MI) for synthetic data. This involves using an existing modified version of Rubin's variance estimator. In practice missing data is ubiquitous in longitudinal datasets. We show that such missing data can be readily accommodated as part of the MI procedure when using G-formula, and describe how MI software can be used to implement the approach. We explore its performance using a simulation study and an application from cystic fibrosis.
Using Social Cues to Recognize Task Failures for HRI: A Review of Current Research and Future Directions

Robots that carry out tasks and interact in complex environments will inevitably commit errors. Error detection is thus an important ability for robots to master, to work in an efficient and productive way. People leverage social cues from others around them to recognize and repair their own mistakes. With advances in computing and AI, it is increasingly possible for robots to achieve a similar error detection capability. In this work, we review current literature around the topic of how social cues can be used to recognize task failures for human-robot interaction (HRI). This literature review unites insights from behavioral science, human-robot interaction, and machine learning, to focus on three areas: 1) social cues for error detection (from behavioral science), 2) recognizing task failures in robots (from HRI), and 3) approaches for autonomous detection of HRI task failures based on social cues (from machine learning). We propose a taxonomy of error detection based on self-awareness and social feedback. Finally, we leave recommendations for HRI researchers and practitioners interested in developing robots that detect (physical) task errors using social cues from bystanders.
High-pressure order-disorder transition in Mg$_2$SiO$_4$: Implications for super-Earth mineralogy

(Mg, Fe)SiO$_3$ post-perovskite is the highest pressure silicate mineral phase in the Earth's interior. The extreme pressure and temperature conditions inside large extrasolar planets will likely lead to phase transitions beyond pPv. In this work we have explored the high-pressure phase relations in Mg$_2$SiO$_4$ using computations based on density functional theory. We find that a partially disordered I-42d type structure would be stable in the interiors of these super-Earth planets. The discovery of a structure where two very dissimilar cations, Mg$^{2+}$ and Si$^{4+}$ occupy the same crystallographic site opens up a domain of interesting crystal chemistry and provides a foundation for other silicates and oxides with mixed occupancy. We have explored the mechanism of the phase transition from the ordered ground state and the effect of the disordering on electronic properties of the silicate phase.
ForkMerge: Overcoming Negative Transfer in Multi-Task Learning

The goal of multi-task learning is to utilize useful knowledge from multiple related tasks to improve the generalization performance of all tasks. However, learning multiple tasks simultaneously often results in worse performance than learning them independently, which is known as negative transfer. Most previous works attribute negative transfer in multi-task learning to gradient con-ﬂicts between different tasks and propose several heuristics to manipulate the task gradients for mitigating this problem, which mainly considers the optimization difﬁculty and overlooks the generalization problem. To fully understand the root cause of negative transfer, we experimentally analyze negative transfer from the perspectives of optimization, generalization, and hypothesis space. Stemming from our analysis, we introduce Fork-Merge , which periodically forks the model into multiple branches with different task weights, and merges dynamically to ﬁlter out detrimental parameter updates to avoid negative transfer. On a series of multi-task learning tasks, ForkMerge achieves improved performance over state-of-the-art methods and largely avoids negative transfer.
The Survival of One-Dimensional Contact Processes in Random Environments

Consider the inhomogeneous contact process on Z1 with recovery rate 8(k) at site k and infection rates A(k) and p(k) at site k due to the presence of infected neighbors at k 1 and k + 1 respectively. A special case of the main result in this paper is the following: Suppose that the environment is chosen in such a way that the 8(k)'s, A(k)'s and p(k)'s are all mutually independent, with the 8(k)'s having a common distribution, and the A(k)'s and p(k)'s having a common distribution. Then the process survives if
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles’ viewpoints and conversational topics change in the dialogue stream. Thus how to effectively handle the shifting topics and select the most salient utterance becomes one of the major challenges of this task. In this paper, we propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics. The centralities are constructed in both global level and local level. The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic. Specifically, the GLC collects sub-topic based on the utterance representations. And each utterance is aligned with one sub-topic. Based on the sub-topics, the GLC calculates global- and local-level centralities. Finally, we combine the two to guide the model to capture both salient context and sub-topics when generating summaries. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly identify vital contents from sub-topics. ~{footnote{Once our paper is accepted, we will release our code here. }
Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits

Today, artificial neural networks are the state of the art for solving a variety of complex tasks, especially in image classification. Such architectures consist of a sequence of stacked layers with the aim of extracting useful information and having it processed by a classifier to make accurate predictions. However, intermediate information within such models is often left unused. In other cases, such as in edge computing contexts, these architectures are divided into multiple partitions that are made functional by including early exits, i.e. intermediate classifiers, with the goal of reducing the computational and temporal load without extremely compromising the accuracy of the classifications. In this paper, we present Anticipate, Ensemble and Prune (AEP), a new training technique based on weighted ensembles of early exits, which aims at exploiting the information in the structure of networks to maximise their performance. Through a comprehensive set of experiments, we show how the use of this approach can yield average accuracy improvements of up to 15% over traditional training. In its hybrid-weighted configuration, AEP's internal pruning operation also allows reducing the number of parameters by up to 41%, lowering the number of multiplications and additions by 18% and the latency time to make inference by 16%. By using AEP, it is also possible to learn weights that allow early exits to achieve better accuracy values than those obtained from single-output reference models.
A Novel Framework for Policy Mirror Descent with General Parametrization and Linear Convergence

Modern policy optimization methods in applied reinforcement learning, such as Trust Region Policy Optimization and Policy Mirror Descent, are often based on the policy gradient framework. While theoretical guarantees have been established for this class of algorithms, particularly in the tabular setting, the use of a general parametrization scheme remains mostly unjustified. In this work, we introduce a novel framework for policy optimization based on mirror descent that naturally accommodates general parametrizations. The policy class induced by our scheme recovers known classes, e.g. softmax, and it generates new ones, depending on the choice of the mirror map. For a general mirror map and parametrization class, we establish the quasi-monotonicity of the updates in value function, global linear convergence rates, and we bound the total expected Bregman divergence of the algorithm along its path. To showcase the ability of our framework to accommodate general parametrization schemes, we present a case study involving shallow neural networks.
Boundary layer receptivity to free‐stream vorticity

The receptivity to free‐stream vorticity of the boundary layer over a flat plate with an elliptic leading edge is investigated numerically by solving the incompressible Navier–Stokes system in general curvilinear coordinates with the vorticity and streamfunction as dependent variables. A small‐amplitude vortical disturbance is introduced at the upstream boundary and the governing equations solved time accurately to evaluate the spatial and temporal growth of the perturbations leading to instability waves [Tollmien–Schlichting (TS) waves] in the boundary layer. The effect of disturbance amplitude, orientation, and the effect of the leading edge and of surface curvature are investigated for the case of spanwise vorticity. Simulations reveal, for the conditions considered, a linear variation in the TS response with forcing amplitude for perturbations of free‐stream velocity that are either symmetrical or asymmetrical with respect to the basic‐state stagnation streamline. The presence near the leading edge of.
Progressive Prompts: Continual Learning for Language Models

We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement>20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods.
Defining an universal"sign"to strictly probe phase transition

The mystery of the infamous sign problem in quantum Monte Carlo (QMC) simulations mightily restricts applications of the method in fermionic and frustrated systems. A recent work [Science \textbf{375}, 418 (2022)] made a remarkable breakthrough in the sign problem by pointing out that the sign can be used to probe phase transition. In this work, we proposed a general argument based on the definition of the sign that is related to the difference in free energy between the original and reference systems to clarify that the sign problem and phase transition cannot always be strictly related. The sign can exactly probe phase transition only if the free energy in the reference system is flat under variable parameters, which is almost impossible to design. Generally speaking, the conclusion that the sign can probe phase transition is survivorship bias without universality. To solve this problem, we define a modified sign that excludes the influence of the reference system, which can probe the phase transition strictly. The work gives an unbiased solution for detecting phase transition by the new modified sign.
20 ps time resolution with a fully-efficient monolithic silicon pixel detector without internal gain layer

A second monolithic silicon pixel prototype was produced for the MONOLITH project. The ASIC contains a matrix of hexagonal pixels with 100 μm pitch, readout by a low-noise and very fast SiGe HBT frontend electronics. Wafers with 50 μm thick epilayer of 350 Ωcm resistivity were used to produce a fully depleted sensor. Laboratory and testbeam measurements of the analog channels present in the pixel matrix show that the sensor has a 130 V wide bias-voltage operation plateau at which the efficiency is 99.8%. Although this prototype does not include an internal gain layer, the design optimised for timing of the sensor and the front-end electronics provides a time resolutions of 20 ps.
Multilingual Sentence Transformer as A Multilingual Word Aligner

Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer LaBSE is a strong multilingual word aligner. This idea is non-trivial as LaBSE is trained to learn language-agnostic sentence-level embeddings, while the alignment extraction task requires the more fine-grained word-level embeddings to be language-agnostic. We demonstrate that the vanilla LaBSE outperforms other mPLMs currently used in the alignment task, and then propose to finetune LaBSE on parallel corpus for further improvement. Experiment results on seven language pairs show that our best aligner outperforms previous state-of-the-art models of all varieties. In addition, our aligner supports different language pairs in a single model, and even achieves new state-of-the-art on zero-shot language pairs that does not appear in the finetuning process.
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?

We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignment, and only the output layer parameters are trained by loss minimization. Using random weights for a hidden layer is an effective method to avoid non-convex optimization in standard gradient descent learning. It has also been adopted in recent deep learning theories. Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error. In particular, we derive a new nontrivial approximation error lower bound. The proof utilizes the technique of ridgelet analysis, a harmonic analysis method designed for neural networks. This method is inspired by fundamental principles in classical signal processing, specifically the idea that signals with limited bandwidth may not always be able to perfectly recreate the original signal. We corroborate our theoretical results with various simulation studies, and generally, two main take-home messages are offered: (i) Not any distribution for selecting random weights is feasible to build a universal approximator; (ii) A suitable assignment of random weights exists but to some degree is associated with the complexity of the target function.
Optical uniform estimates and rigorous asymptotics beyond all orders for a class of ordinary differential equations

For first-order differential equations of the form y' = ∑pp(x)yp and second-order homogeneous linear differential equations y" + a(x)y' + b(x)y 0 with locally integrable coefficients having asymptotic (possibly divergent) power series when |x| —► oo on a ray arg(x) = const. , under some further assumptions, it is shown that, on the given ray, there is a one-to-one correspondence between true solutions and (complete) formal solutions. The correspondence is based on asymptotic inequalities which are required to be uniform in x and optimal with respect to certain weights.
Federated Learning for Water Consumption Forecasting in Smart Cities

Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly. This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption. The proposed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility. In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity. Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.
PAC-Bayesian Soft Actor-Critic Learning

Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy evaluation and improvement, via two separate function approximators. The practicality of this approach comes at the expense of training instability, caused mainly by the destructive effect of the approximation errors of the critic on the actor. We tackle this bottleneck by employing an existing Probably Approximately Correct (PAC) Bayesian bound for the first time as the critic training objective of the Soft Actor-Critic (SAC) algorithm. We further demonstrate that online learning performance improves significantly when a stochastic actor explores multiple futures by critic-guided random search. We observe our resulting algorithm to compare favorably to the state of the art on multiple classical control and locomotion tasks in terms of both sample efficiency and regret minimization.
Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r. t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.
KalmanNet: A Learnable Kalman Filter for Acoustic Echo Cancellation

The Kalman ﬁlter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive ﬁltering algorithms. In this paper, we integrate the frequency domain Kalman ﬁlter (FDKF) and deep neural networks (DNNs) into a hybrid method, called KalmanNet, to leverage the advantages of deep learning and adaptive ﬁltering algorithms. Speciﬁcally, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed KalmanNet improves the performance of FDKF signiﬁcantly and outperforms strong baseline methods.