Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.
Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks

Childhood overweight and obesity is one of the main health problems in the world since it is related to the early appearance of different diseases, in addition to being a risk factor for later developing obesity in adulthood with its health and economic consequences. Visceral abdominal tissue (VAT) is strongly related to the development of metabolic and cardiovascular diseases compared to abdominal subcutaneous adipose tissue (ASAT). Therefore, precise and automatic VAT and ASAT quantification methods would allow better diagnosis, monitoring and prevention of diseases caused by obesity at any stage of life. Currently, magnetic resonance imaging is the standard for fat quantification, with Dixon sequences being the most useful. Different semiautomatic and automatic ASAT and VAT quantification methodologies have been proposed. In particular, the semi-automated quantification methodology used commercially through the cloud-based service AMRA R Researcher stands out due to its extensive validation in different studies. In the present work, a database made up of Dixon MRI sequences, obtained from children between 7 and 9 years of age, was studied. Applying a preprocessing to obtain what we call total intensity maps, a convolutional neural network (CNN) was proposed for the automatic quantification of ASAT and VAT. The quantifications obtained from the proposed methodology were compared with quantifications previously made through AMRA R Researcher. For the comparison, correlation analysis, Bland-Altman graphs and non-parametric statistical tests were used. The results indicated a high correlation and similar precisions between the quantifications of this work and those of AMRA R Researcher. The final objective is that the proposed methodology can serve as an accessible and free tool for the diagnosis, monitoring and prevention of diseases related to childhood obesity.
Dynamique des figures de tavelures en milieux désordonnés

Ce travail resume mes activites de recherche portant sur la diffusion multiple des ondes en milieux desordonnes et, en particulier, sur la dynamique des ondes multiplement diffusees. La spectroscopie, l'imagerie et la microscopie par ondes diffusees, la diffusion multiple dans les milieux non lineaires, la localisation d'Anderson, et l'optique quantique des milieux desordonnes sont discutes dans 4 chapitres.
Probing the state of hydrogen in 
δ−AlOOH
 at mantle conditions with machine learning potential

Hydrous and nominally anhydrous minerals (NAMs) are a fundamental class of solids of enormous significance to geophysics. They are the water carriers in the deep geological water cycle and impact structural, elastic, plastic, and thermodynamic properties and phase relations in Earth's forming aggregates (rocks). They play a critical role in the geochemical and geophysical processes that shape the planet. Their complexity has prevented predictive calculations of their properties, but progress in materials simulations ushered by machine learning potentials is transforming this state of affairs. Here, we adopt a hybrid approach that combines deep learning potentials (DP) with the SCAN meta-GGA functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate $\delta$-AlOOH ($\delta$), a phase capable of transporting water down to near the core-mantle boundary of the Earth (~2,900 km depth and ~135 GPa) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DP-potentials sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions. These simulations provide a pathway for a deeper understanding of these crucial components that shape Earth's internal state.
Explicit Riemann-Roch spaces in the Hilbert class field

Let $\mathbf K$ be a finite field, $X$ and $Y$ two curves over $\mathbf K$, and $Y\rightarrow X$ an unramified abelian cover with Galois group $G$. Let $D$ be a divisor on $X$ and $E$ its pullback on $Y$. Under mild conditions the linear space associated with $E$ is a free ${\mathbf K}[G]$-module. We study the algorithmic aspects and applications of these modules.
Fault-Tolerant Spanners against Bounded-Degree Edge Failures: Linearly More Faults, Almost For Free

We study a new and stronger notion of fault-tolerant graph structures whose size bounds depend on the degree of the failing edge set, rather than the total number of faults. For a subset of faulty edges $F \subseteq G$, the faulty-degree $\deg(F)$ is the largest number of faults in $F$ incident to any given vertex. We design new fault-tolerant structures with size comparable to previous constructions, but which tolerate every fault set of small faulty-degree $\deg(F)$, rather than only fault sets of small size $|F|$. Our main results are: - New FT-Certificates: For every $n$-vertex graph $G$ and degree threshold $f$, one can compute a connectivity certificate $H \subseteq G$ with $|E(H)| = \widetilde{O}(fn)$ edges that has the following guarantee: for any edge set $F$ with faulty-degree $\deg(F)\leq f$ and every vertex pair $u,v$, it holds that $u$ and $v$ are connected in $H \setminus F$ iff they are connected in $G \setminus F$. This bound on $|E(H)|$ is nearly tight. Since our certificates handle some fault sets of size up to $|F|=O(fn)$, prior work did not imply any nontrivial upper bound for this problem, even when $f=1$. - New FT-Spanners: We show that every $n$-vertex graph $G$ admits a $(2k-1)$-spanner $H$ with $|E(H)| = O_k(f^{1-1/k} n^{1+1/k})$ edges, which tolerates any fault set $F$ of faulty-degree at most $f$. This bound on $|E(H)|$ optimal up to its hidden dependence on $k$, and it is close to the bound of $O_k(|F|^{1/2} n^{1+1/k} + |F|n)$ that is known for the case where the total number of faults is $|F|$ [Bodwin, Dinitz, Robelle SODA '22]. Our proof of this theorem is non-constructive, but by following a proof strategy of Dinitz and Robelle [PODC '20], we show that the runtime can be made polynomial by paying an additional $\text{polylog } n$ factor in spanner size.
TTD Configurations for Near-Field Beamforming: Parallel, Serial, or Hybrid?

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. In this paper, a serial and a hybrid serial-parallel TTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay caused by multiple TTDs, which potentially alleviates the maximum delay requirements on the individual TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. Therefore, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. The wideband near-field beamforming design for different configurations is studied to maximize the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for the considered TTD configurations. In addition, a hybrid-forward-and-backward (HFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the individual TTDs and ii) the hybrid configuration excels in single-user systems, while the HFB serial configuration is preferred in multi-user systems.
CalClim: An accessible data archive to monitor California climate

Climate change and variability have crucial impacts on water supply, energy use, agriculture, biodiversity and numerous other aspects of California’s environment and society. Because California’s population is projected to double over the next several decades and at the same time the global climate is likely to undergo considerable changes, these impacts will probably be even greater than they are today. A number of studies (e.g., Roos (1993) and Dettinger and Cayan (1995), Stewart et al (2004)) have described reduced snowmelt runoff in Sierra Nevada watersheds and several others (e.g., Knowles and Cayan (2002) Dettinger et al. (2004), Hayhoe et al. (2004)) have shown how increasing temperatures will significantly change California’s hydrological characteristics. Basic questions such as “Is California warming or cooling? ” and “Is California getting wetter or drier? ” are currently not answered with precision or certainty. As a result, the California Energy Commission (CEC) has established the California Climate Change Center to investigate and plan for potential climate impacts. The CalClim project was established to be the data warehouse to support the research of the California Climate Change Center and other climate scientists, and to develop, archive and disseminate products for monitoring climate in California. California’s physical and organizational landscape is extremely complex, and at the time when CalClim was conceived in 2003, climate data and information were spread over various agencies and sources. A time-consuming search for the necessary and relevant data and information was required. CalClim’s California Climate Data Archive (CCDA) was designed to streamline the data access process, simplifying the hunt for appropriate climate data and information for the researcher by consolidating several data sources. The Western Regional Climate Center (WRCC) at the Desert Research Institute, one of six NOAA Regional Climate Centers, was selected to build the CCDA. WRCC has established itself as a reliable and comprehensive resource for national and regional data sets, including NOAA data via the National Weather Service (NWS) Cooperative Network and the National
Résumé Parsing as Hierarchical Sequence Labeling: An Empirical Study

Extracting information from r\'esum\'es is typically formulated as a two-stage problem, where the document is first segmented into sections and then each section is processed individually to extract the target entities. Instead, we cast the whole problem as sequence labeling in two levels -- lines and tokens -- and study model architectures for solving both tasks simultaneously. We build high-quality r\'esum\'e parsing corpora in English, French, Chinese, Spanish, German, Portuguese, and Swedish. Based on these corpora, we present experimental results that demonstrate the effectiveness of the proposed models for the information extraction task, outperforming approaches introduced in previous work. We conduct an ablation study of the proposed architectures. We also analyze both model performance and resource efficiency, and describe the trade-offs for model deployment in the context of a production environment.
Global becomes local: Efficient many-body dynamics for global master equations

This work makes progress on the issue of global- vs. local- master equations. Global master equations like the Redfield master equation (following from standard Born- and Markov- approximation) require a full diagonalization of the system Hamiltonian. This is especially challenging for interacting quantum many-body systems. We discuss a short-bath-correlation-time expansion in reciprocal (energy) space, leading to a series expansion of the jump operator, which avoids a diagonalization of the Hamiltonian. For a bath that is coupled locally to one site, this typically leads to an expansion of the global Redfield jump operator in terms of local operators. We additionally map the local Redfield master equation to an approximate Lindblad form, giving an equation which has the same conceptual advantages of traditional local Lindblad approaches, while being applicable in a much broader class of systems. Our ideas give rise to a non-heuristic foundation of local master equations, which can be combined with established many-body methods.
Minimum Bitrate Neuromorphic Encoding for Continuous-Time Gauss-Markov Processes

In this work, we study minimum data rate tracking of a dynamical system under a neuromorphic event-based sensing paradigm. We begin by bridging the gap between continuous-time (CT) system dynamics and information theory's causal rate distortion theory. We motivate the use of non-singular source codes to quantify bitrates in event-based sampling schemes. This permits an analysis of minimum bitrate event-based tracking using tools already established in the control and information theory literature. We derive novel, nontrivial lower bounds to event-based sensing, and compare the lower bound with the performance of well-known schemes in the established literature.
Human-Robot Co-creativity: A Scoping Review : Informing a Research Agenda for Human-Robot Co-Creativity with Older Adults

This review is the first step in a long-term research project exploring how social robotics and AI-generated content can contribute to the creative experiences of older adults, with a focus on collaborative drawing and painting. We systematically searched and selected literature on human-robot co-creativity, and analyzed articles to identify methods and strategies for researching co-creative robotics. We found that none of the studies involved older adults, which shows the gap in the literature for this often involved participant group in robotics research. The analyzed literature provides valuable insights into the design of human-robot co-creativity and informs a research agenda to further investigate the topic with older adults. We argue that future research should focus on ecological and developmental perspectives on creativity, on how system behavior can be aligned with the values of older adults, and on the system structures that support this best.
On the probability of down-crossing and up-crossing rogue waves

By means of the direct numerical simulation of directional waves on the surface of deep water, it is shown that extreme waves can exhibit such asymmetry that the occurrence of deeper troughs is several times more likely on the wave rear slopes. This effect becomes most pronounced in the case of steep short-crested waves. It is not related to the Benjamin–Feir instability but is a result of complex contribution from nonlinear combination harmonics, mainly cubic in nonlinearity. The discovered asymmetry can lead to remarkably different estimates of the rogue wave probability based on either down- or up-zero-crossing methods for individual wave selection, commonly used in the oceanography.
Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays

The layout of multi-dimensional data can have a significant impact on the efficacy of hardware caches and, by extension, the performance of applications. Common multi-dimensional layouts include the canonical row-major and column-major layouts as well as the Morton curve layout. In this paper, we describe how the Morton layout can be generalized to a very large family of multi-dimensional data layouts with widely varying performance characteristics. We posit that this design space can be efficiently explored using a combinatorial evolutionary methodology based on genetic algorithms. To this end, we propose a chromosomal representation for such layouts as well as a methodology for estimating the fitness of array layouts using cache simulation. We show that our fitness function correlates to kernel running time in real hardware, and that our evolutionary strategy allows us to find candidates with favorable simulated cache properties in four out of the eight real-world applications under consideration in a small number of generations. Finally, we demonstrate that the array layouts found using our evolutionary method perform well not only in simulated environments but that they can effect significant performance gains -- up to a factor ten in extreme cases -- in real hardware.
RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design

Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At first glance, this is a reasonable design by following the rack-scale hierarchical design principles. However, it suffers from suboptimal end-to-end performance, due to the lack of coordination between SDN and SDF. In this paper, we co-design the SDN and SDF stack by redefining the functions of their control plane and data plane, and splitting up them within a new architecture named RackBlox. RackBlox decouples the storage management functions of flash-based solid-state drives (SSDs), and allow the SDN to track and manage the states of SSDs in a rack. Therefore, we can enable the state sharing between SDN and SDF, and facilitate global storage resource management. RackBlox has three major components: (1) coordinated I/O scheduling, in which it dynamically adjusts the I/O scheduling in the storage stack with the measured and predicted network latency, such that it can coordinate the effort of I/O scheduling across the network and storage stack for achieving predictable end-to-end performance; (2) coordinated garbage collection (GC), in which it will coordinate the GC activities across the SSDs in a rack to minimize their impact on incoming I/O requests; (3) rack-scale wear leveling, in which it enables global wear leveling among SSDs in a rack by periodically swapping data, for achieving improved device lifetime for the entire rack. We implement RackBlox using programmable SSDs and switch. Our experiments demonstrate that RackBlox can reduce the tail latency of I/O requests by up to 5.8× over state-of-the-art rack-scale storage systems.
Collaborative Dynamic 3D Scene Graphs for Automated Driving

Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes and processing information from multiple agents are still challenging problems. In this work, we present Collaborative URBan Scene Graphs (CURB-SG) that enable higher-order reasoning and efficient querying for many functions of automated driving. CURB-SG leverages panoptic LiDAR data from multiple agents to build large-scale maps using an effective graph-based collaborative SLAM approach that detects inter-agent loop closures. To semantically decompose the obtained 3D map, we build a lane graph from the paths of ego agents and their panoptic observations of other vehicles. Based on the connectivity of the lane graph, we segregate the environment into intersecting and non-intersecting road areas. Subsequently, we construct a multi-layered scene graph that includes lane information, the position of static landmarks and their assignment to certain map sections, other vehicles observed by the ego agents, and the pose graph from SLAM including 3D panoptic point clouds. We extensively evaluate CURB-SG in urban scenarios using a photorealistic simulator. We release our code at http://curb.cs. uni-freiburg.de.
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers

For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy. In this paper, we explore the benefits of combining these two aspects together in the scenario of transfer learning -- differentially private pre-training and fine-tuning of TabTransformers with a variety of parameter-efficient fine-tuning (PEFT) methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experiments on the ACSIncome dataset show that these PEFT methods outperform traditional approaches in terms of the accuracy of the downstream task and the number of trainable parameters, thus achieving an improved trade-off among parameter efficiency, privacy, and accuracy. Our code is available at github.com/IBM/DP-TabTransformer.
Games and Argumentation: Time for a Family Reunion!

The rule"defeated(X) $\leftarrow$ attacks(Y,X), $\neg$ defeated(Y)"states that an argument is defeated if it is attacked by an argument that is not defeated. The rule"win(X) $\leftarrow$ move(X,Y), $\neg$ win(Y)"states that in a game a position is won if there is a move to a position that is not won. Both logic rules can be seen as close relatives (even identical twins) and both rules have been at the center of attention at various times in different communities: The first rule lies at the core of argumentation frameworks and has spawned a large family of models and semantics of abstract argumentation. The second rule has played a key role in the quest to find the"right"semantics for logic programs with recursion through negation, and has given rise to the stable and well-founded semantics. Both semantics have been widely studied by the logic programming and nonmonotonic reasoning community. The second rule has also received much attention by the database and finite model theory community, e.g., when studying the expressive power of query languages and fixpoint logics. Although close connections between argumentation frameworks, logic programming, and dialogue games have been known for a long time, the overlap and cross-fertilization between the communities appears to be smaller than one might expect. To this end, we recall some of the key results from database theory in which the win-move query has played a central role, e.g., on normal forms and expressive power of query languages. We introduce some notions that naturally emerge from games and that may provide new perspectives and research opportunities for argumentation frameworks. We discuss how solved query evaluation games reveal how- and why-not provenance of query answers. These techniques can be used to explain how results were derived via the given query, game, or argumentation framework.