Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitask Learning via Shared Features: Algorithms and Hardness

We investigate the computational efficiency of multitask learning of Boolean functions over the $d$-dimensional hypercube, that are related by means of a feature representation of size $k \ll d$ shared across all tasks. We present a polynomial time multitask learning algorithm for the concept class of halfspaces with margin $\gamma$, which is based on a simultaneous boosting technique and requires only $\textrm{poly}(k/\gamma)$ samples-per-task and $\textrm{poly}(k\log(d)/\gamma)$ samples in total. In addition, we prove a computational separation, showing that assuming there exists a concept class that cannot be learned in the attribute-efficient model, we can construct another concept class such that can be learned in the attribute-efficient model, but cannot be multitask learned efficiently -- multitask learning this concept class either requires super-polynomial time complexity or a much larger total number of samples.
Real-to-Sim: Deep Learning with Auto-Tuning to Predict Residual Errors using Sparse Data

—Achieving highly accurate kinematic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. Thus, the objective of this work is to learn the residual errors between a kinematic and/or simulator model and the real robot. This is achieved using auto-tuning and neural networks, where the parameters of a neural network are updated using an auto-tuning method that applies equations from an Unscented Kalman Filter (UKF) formulation. Using this method, we model these residual errors with only small amounts of data — a necessity as we improve the simulator/kinematic model by learning directly from hardware operation. We demonstrate our method on robotic hardware (e.g., manipulator arm), and show that with the learned residual errors, we can further close the reality gap between kinematic models, simulations, and the real robot.
Driverless road-marking Machines: Ma(r)king the Way towards the Future of Mobility

Driverless road maintenance could potentially be highly beneficial to all its stakeholders, with the key goals being increased safety for all road participants, more efficient traffic management, and reduced road maintenance costs such that the standard of the road infrastructure is sufficient for it to be used in Automated Driving (AD). This paper addresses how the current state of the technology could be expanded to reach those goals. Within the project ‘System for Teleoperated Road-marking’ (SToRM), using the road-marking machine as the system, different operation modes based on teleoperation were discussed and developed. Furthermore, a functional system overview considering both hardware and software elements was experimentally validated with an actual road-marking machine and should serve as a baseline for future efforts in this and similar areas.
Life on Exoplanets in the Habitable Zone of M Dwarfs?

Exoplanets orbiting in the habitable zone around M dwarf stars have been prime targets in the search for life due to the long lifetimes of the host star, the prominence of such stars in the galaxy, and the apparent excess of terrestrial planets found around M dwarfs. However, the heightened stellar activity of M dwarfs and the often tidally locked planets in these systems have raised questions about the habitability of these planets. In this Letter we examine another significant challenge that may exist: these systems seem to lack the architecture necessary to deliver asteroids to the habitable terrestrial planets, and asteroid impacts may play a crucial role in the origin of life. The most widely accepted mechanism for producing a stable asteroid belt and the late-stage delivery of asteroids after gas disk dissipation requires a giant planet exterior to the snow-line radius. We show that none of the observed systems with planets in the habitable zone of their star also contain a giant planet and therefore are unlikely to have stable asteroid belts. We consider the locations of observed giant planets relative to the snow-line radius as a function of stellar mass and find that there is a population of giant planets outside of the snow-line radius around M dwarfs. Therefore, asteroid belt formation around M dwarfs is generally possible. However, we find that multiplanetary system architectures around M dwarfs can be quite different from those around more massive stars.
Bayesian learning of feature spaces for multitasks problems

This paper presents a Bayesian framework to construct non-linear, parsimonious, shallow models for multitask regression. The proposed framework relies on the fact that Random Fourier Features (RFFs) enables the approximation of an RBF kernel by an extreme learning machine whose hidden layer is formed by RFFs. The main idea is to combine both dual views of a same model under a single Bayesian formulation that extends the Sparse Bayesian Extreme Learning Machines to multitask problems. From the kernel methods point of view, the proposed formulation facilitates the introduction of prior domain knowledge through the RBF kernel parameter. From the extreme learning machines perspective, the new formulation helps control overfitting and enables a parsimonious overall model (the models that serve each task share a same set of RFFs selected within the joint Bayesian optimisation). The experimental results show that combining advantages from kernel methods and extreme learning machines within the same framework can lead to significant improvements in the performance achieved by each of these two paradigms independently.
From granular collapses to shallow water waves: A predictive model for tsunami generation

Strongly equatorially asymmetric polar (AP) as well as equatorially symmetric polar (SP) nonlinear rotating waves (RW) are obtained by means of continuation methods at low Prandtl number in a model of thermal convection in thin rotating spherical shells with stress-free boundary conditions. The main goal is to study this new type of convection which is confined at high latitudes in one (AP RW) or both (SP RW) hemispheres, developing a strong axisymmetric component very close to the onset. The AP branch corresponds to the 1st linear instability, which has azimuthal wave number $m=19$, while the SP branch corresponds to the 2nd $m=19$ linear instability. The latter branch becomes stable, demonstrating the relevance of computing several eigenfunctions in linear stability analysis. The low Prandtl number regime is crucial for understanding geophysical and astrophysical convection. We discuss how these waves may be relevant in modelling various geo/planetary/stellar scenarios, in particular that of Jovian convection. Our simulations show equatorially asymmetric zonal winds with a pronounced decrease at the equator (the so-called dimple).
Reconsideration of Conditional Instability Static Stability of a Saturated Air Parcel in an Unsatur

This study examines the stability criterion of a saturated air parcel in an unsaturated environment using the parcel method for parcels of the same virtual temperature as their environment. By considering the lapse rate of virtual temperature in a moist adiabatic process, we get the results that the lapse rate of temperature for the criterion is larger than the usual moist adiabatic temperature lapse rate by about 20% of the difference between dry and moist adiabatic lapse rates. The newly obtained criterion was used to examine the frequency distribution of temperature lapse rates observed by sonde in the tropics. The results show that the maximum frequency of stability occurs at neutral, and that about half of the total cases are unstable if we consider the stability for a saturated air parcel in an unsaturated environment.
Quantum Precision Limits of Displacement Noise-Free Interferometers.

Current laser-interferometric gravitational wave detectors suffer from a fundamental limit to their precision due to the displacement noise of optical elements contributed by various sources. Several schemes for displacement noise-free interferometers (DFI) have been proposed to mitigate their effects. The idea behind these schemes is similar to decoherence-free subspaces in quantum sensing; i.e., certain modes contain information about the gravitational waves but are insensitive to the mirror motion (displacement noise). We derive quantum precision limits for general DFI schemes, including optimal measurement basis and optimal squeezing schemes. We introduce a triangular cavity DFI scheme and apply our general bounds to it. Precision analysis of this scheme with different noise models shows that the DFI property leads to interesting sensitivity profiles and improved precision due to noise mitigation and larger gain from squeezing.
Efficient solution of parameter identification problems with H1 regularization

We consider the identification of spatially distributed parameters under $H^1$ regularization. Solving the associated minimization problem by Gauss-Newton iteration results in linearized problems to be solved in each step that can be cast as boundary value problems involving a low-rank modification of the Laplacian. Using algebraic multigrid as a fast Laplace solver, the Sherman-Morrison-Woodbury formula can be employed to construct a preconditioner for these linear problems which exhibits excellent scaling w.r. t. the relevant problem parameters. We first develop this approach in the functional setting, thus obtaining a consistent methodology for selecting boundary conditions that arise from the $H^1$ regularization. We then construct a method for solving the discrete linear systems based on combining any fast Poisson solver with the Woodbury formula. The efficacy of this method is then demonstrated with scaling experiments. These are carried out for a common nonlinear parameter identification problem arising in electrical resistivity tomography.
Portable solvers for batches of small systems applied to the Landau collision operator

—Many small independent sparse linear system solves occur in many applications, such as the Landau collision operator in plasma physics and astrophysics simulations, chemistry in combustion applications, and subdomain solves in domain decomposition solvers. One can simply stack these systems into a global linear system and use existing general-purpose sparse solvers. However, this “ensemble” approach does not exploit the independent structure of these systems and the theoretical optimality of a Krylov solver is lost. The many independent processing elements (PEs) found in contemporary (GPU) accelerators are well suited to solving each of these systems independently. This “batch” approach maintains the Krylov subspace optimality, signiﬁcantly reduces the number of kernel launches, and elides (unnecessary) global communication. This study develops portable solvers that run an entire linear system solve on a PE in a single kernel launch within the PETSc (Portable Extensible Toolkit for Scientiﬁc Computing) numerical library.
A Data-dependent Approach for High-dimensional (Robust) Wasserstein Alignment

Many real-world problems can be formulated as the alignment between two geometric patterns. Previously, a great amount of research focus on the alignment of two-dimensional (2D) or 3D patterns in the field of computer vision. Recently, the alignment problem in high dimensions finds several novel applications in practice. However, the research is still rather limited in the algorithmic aspect. To the best of our knowledge, most existing approaches are just simple extensions of their counterparts for 2D and 3D cases and often suffer from the issues such as high computational complexities. In this article, we propose an effective framework to compress the high-dimensional geometric patterns. Any existing alignment method can be applied to the compressed geometric patterns and the time complexity can be significantly reduced. Our idea is inspired by the observation that high-dimensional data often has a low intrinsic dimension. Our framework is a “data-dependent” approach that has the complexity depending on the intrinsic dimension of the input data. Our experimental results reveal that running the alignment algorithm on compressed patterns can achieve similar qualities, comparing with the results on the original patterns, but the runtimes (including the times cost for compression) are substantially lower.
Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently con-stitutes a signiﬁcant limitation for several practical applications. Ultra-wideband (UWB) localization technology rep-resents a valuable low-cost solution to the problem. How-ever, non-line-of-sight (NLOS) conditions and complexity of the speciﬁc radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly in-accurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efﬁcient methodology.
Unifying Effects of Direct and Relational Associations for Visual Communication

People have expectations about how colors map to concepts in visualizations, and they are better at interpreting visualizations that match their expectations. Traditionally, studies on these expectations (inferred mappings) distinguished distinct factors relevant for visualizations of categorical vs. continuous information. Studies on categorical information focused on direct associations (e.g., mangos are associated with yellows) whereas studies on continuous information focused on relational associations (e.g., darker colors map to larger quantities; dark-is-more bias). We unite these two areas within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. Observers infer globally optimal assignments by maximizing the “merit,” or “goodness,” of each possible assignment. Previous work on assignment inference focused on visualizations of categorical information. We extend this approach to visualizations of continuous data by (a) broadening the notion of merit to include relational associations and (b) developing a method for combining multiple (sometimes conflicting) sources of merit to predict people's inferred mappings. We developed and tested our model on data from experiments in which participants interpreted colormap data visualizations, representing fictitious data about environmental concepts (sunshine, shade, wild fire, ocean water, glacial ice). We found both direct and relational associations contribute independently to inferred mappings. These results can be used to optimize visualization design to facilitate visual communication.
Improving Choral Music Separation through Expressive Synthesized Data from Sampled Instruments

Choral music separation refers to the task of extracting tracks of voice parts (e.g., soprano, alto, tenor, and bass) from mixed audio. The lack of datasets has impeded research on this topic as previous work has only been able to train and evaluate models on a few minutes of choral music data due to copyright issues and dataset collection difficulties. In this paper, we investigate the use of synthesized training data for the source separation task on real choral music. We make three contributions: first, we provide an automated pipeline for synthesizing choral music data from sampled instrument plugins within controllable options for instrument expressiveness. This produces an 8.2-hour-long choral music dataset from the JSB Chorales Dataset and one can easily synthesize additional data. Second, we conduct an experiment to evaluate multiple separation models on available choral music separation datasets from previous work. To the best of our knowledge, this is the first experiment to comprehensively evaluate choral music separation. Third, experiments demonstrate that the synthesized choral data is of sufficient quality to improve the model's performance on real choral music datasets. This provides additional experimental statistics and data support for the choral music separation study.
Statistical Evidence for Small-scale Interchange Reconnection at a Coronal Hole Boundary

Much of coronal hole (CH) research is focused upon determining the boundary and calculating the open flux as accurately as possible. However, the observed boundary itself is worthy of investigation, and holds important clues to the physics transpiring at the interface between the open and closed fields. This Letter reports a powerful new method, an application of the correlation integral which we call correlation dimension mapping, by which the irregularity of a CH boundary can be objectively quantified. This method highlights the most important spatial scales involved in boundary dynamics, and also allows for easy temporal analysis of the boundary. We apply this method to an equatorial CH bounded on two sides by helmet streamers and on the third by a small pseudostreamer, which we observed at maximum cadence for an hour on 2015 June 4. We argue that the relevant spatial scales are in the range of ∼5–20 Mm, and we find that the boundary complexity depends measurably upon the nature of the neighboring closed structure. The boundary along the pseudostreamer shows signs of highly localized, intermittent-complexity variability, likely associated with abrupt changes in the magnetic topology, which would be elegantly explained by interchange reconnection. By contrast, the helmet streamer boundary supports long-lived, high-complexity regions. These findings support the recent predictions of interchange reconnection occurring at very small scales in the corona.
Output selection for large scale structural systems: a matroid theory approach

The question of how many outputs are needed and where to place them in order to fully observe a linear system has been a fundamental and challenging problem in control theory and applications of control systems. Structural system theory combined with graph theoretic tools has provided an efficient framework to answer this question for an equivalent class of systems, where properties are explored based on the structure of the system that corresponds to the location of zero and nonzero values. This approach deals well with applications to large-scale systems, where the systems’ dimension has to be taken into account. Within this framework, for a given structure of the state matrix of a large scale linear system, a fundamental problem is the design issue of the structure of the output matrix such that overall the system is structurally observable with the restriction that each output be dedicated, i.e., it can only measure directly a single state variable. In the present work, we propose a novel approach to solve that problem based on matroid theory. Further, we make the connection of the obtained results with graph theory and consequently provide an efficient solution to find a minimum number of dedicated outputs that ensures structural observability. Moreover, we demonstrate that if we additionally impose a performance restriction given by the systems’ generic observability index, then the problem falls in the NP-complete class. Several examples are presented that illustrate the derived results.
Cerberus: Exploring Federated Prediction of Security Events

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible. In this paper, we explore the feasibility of using Federated Learning (FL) to predict future security events. To this end, we introduce Cerberus, a system enabling collaborative training of Recurrent Neural Network (RNN) models for participating organizations. The intuition is that FL could potentially offer a middle-ground between the non-private approach where the training data is pooled at a central server and the low-utility alternative of only training local models. We instantiate Cerberus on a dataset obtained from a major security company's intrusion prevention product and evaluate it vis-à-vis utility, robustness, and privacy, as well as how participants contribute to and benefit from the system. Overall, our work sheds light on both the positive aspects and the challenges of using FL for this task and paves the way for deploying federated approaches to predictive security.
VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph

Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimodal relationships like semantic similarity, spatial relations, etc. We first explored this issue by organizing a large-scale scene graph dataset, namely Visual Genome, in the RDF graph database. Based on the proposed RDF-stored multimodal scene graph, we extended SPARQL queries to answer questions containing relational reasoning about color, spatial, etc. Further demo (i.e., VGStore) shows the effectiveness of customized queries and displaying multimodal data.
THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication

The terahertz (0.1-10 THz) band holds enormous potential for supporting unprecedented data rates and millimeter-level accurate sensing thanks to its ultra-broad bandwidth. Terahertz integrated sensing and communication (ISAC) is viewed as a game-changing technology to realize connected intelligence in 6G and beyond systems. In this article, challenges from the THz channel and transceiver perspectives, as well as difficulties of ISAC, are elaborated. Motivated by these challenges, THz ISAC channels are studied in terms of channel types, measurement, and models. Moreover, four key signal processing techniques to unleash the full potential of THz ISAC are investigated, namely, waveform design, receiver processing, narrowbeam management, and localization. Quantitative studies demonstrate the benefits and performance of the state-of-the-art signal processing methods. Finally, open problems and potential solutions are discussed.