Miracle-dz/UniFAR-FTUs huggingface dataset:
Cohesion and segregation in higher-order networks

Looking to overcome the limitations of traditional networks, the network science community has lately given much attention to the so-called higher-order networks, where group interactions are modeled alongside pairwise ones. While degree distribution and clustering are the most important features of traditional network structure, higher-order networks present two additional fundamental properties that are barely addressed: the group size distribution and overlaps. Here, I investigate the impact of these properties on the network structure, focusing on cohesion and segregation (fragmentation and community formation). For that, I create artiﬁcial higher-order networks with a version of the conﬁguration model that assigns degree to nodes and size to groups and forms overlaps with a tuning parameter p . Counter-intuitively, the results show that a high frequency of overlaps favors both network cohesion and segregation—the network becomes more modular and can even break into several components, but with tightly-knit communities.
Generalization-Memorization Machines

Classifying the training data correctly without over-fitting is one of the goals in machine learning. In this paper, we propose a generalization-memorization mechanism, including a generalization-memorization decision and a memory modeling principle. Under this mechanism, error-based learning machines improve their memorization abilities of training data without over-fitting. Specifically, the generalization-memorization machines (GMM) are proposed by applying this mechanism. The optimization problems in GMM are quadratic programming problems and could be solved efficiently. It should be noted that the recently proposed generalization-memorization kernel and the corresponding support vector machines are the special cases of our GMM. Experimental results show the effectiveness of the proposed GMM both on memorization and generalization.
Rotated ansatz for approximate counterdiabatic driving

Approximate counterdiabatic (CD) protocols are a powerful tool to enhance quantum adiabatic processes that allow to reliably manipulate quantum systems on short time scales. However, implementing CD protocols entails the introduction of additional control fields in the Hamiltonian, often associated with highly non-local multi-body interactions. Here, we introduce a novel variational rotated ansatz (RA) to systematically generate experimentally accessible approximate CD protocols. We numerically benchmark our approach on state preparation and adiabatic quantum computing algorithms, and find that using RA protocols significantly enhances their performances.
Central limit theorem for the principal eigenvalue and eigenvector of Chung–Lu random graphs

A Chung–Lu random graph is an inhomogeneous Erdős–Rényi random graph in which vertices are assigned average degrees, and pairs of vertices are connected by an edge with a probability that is proportional to the product of their average degrees, independently for different edges. We derive a central limit theorem for the principal eigenvalue and the components of the principal eigenvector of the adjacency matrix of a Chung–Lu random graph. Our derivation requires certain assumptions on the average degrees that guarantee connectivity, sparsity and bounded inhomogeneity of the graph.
Metal Mixing in the r-process Enhanced Ultrafaint Dwarf Galaxy Reticulum II

The ultrafaint dwarf galaxy Reticulum II was enriched by a single rare and prolific r-process event. The r-process content of Reticulum II thus provides a unique opportunity to study metal mixing in a relic first galaxy. Using multi-object high-resolution spectroscopy with VLT/GIRAFFE and Magellan/M2FS, we identify 32 clear spectroscopic member stars and measure abundances of Mg, Ca, Fe, and Ba where possible. We find 72−12+10% of the stars are r-process-enhanced, with a mean [Ba/H]=−1.68±0.07 and unresolved intrinsic dispersion σ [Ba/H]<0.20. The homogeneous r-process abundances imply that Ret II’s metals are well mixed by the time the r-enhanced stars form, which simulations have shown requires at least 100 Myr of metal mixing in between bursts of star formation to homogenize. This is the first direct evidence of bursty star formation in an ultrafaint dwarf galaxy. The homogeneous dilution prefers a prompt and high-yield r-process site, such as collapsar disk winds or prompt neutron star mergers. We also find evidence from [Ba/H] and [Mg/Ca] that the r-enhanced stars in Ret II formed in the absence of substantial pristine gas accretion, perhaps indicating that ≈70% of Ret II stars formed after reionization.
An AGN with an Ionized Gas Outflow in a Massive Quiescent Galaxy in a Protocluster at z = 3.09

We report the detection of an ionized gas outflow from an X-ray active galactic nucleus hosted in a massive quiescent galaxy in a protocluster at z = 3.09 (J221737.29+001823.4). It is a type-2 QSO with broad (W 80 > 1000 km s−1) and strong ( log(L[OIII] /erg s−1) ≈ 43.4) [O iii]λ λ 4959,5007 emission lines detected by slit spectroscopy in three-position angles using Multi-Object Infra-Red Camera and Spectrograph (MOIRCS) on the Subaru telescope and the Multi-Object Spectrometer For Infra-Red Exploration (MOSFIRE) on the Keck-I telescope. In the all slit directions, [O iii] emission is extended to ∼15 physical kpc and indicates a powerful outflow spreading over the host galaxy. The inferred ionized gas mass outflow rate is 22 ± 3 M ⊙ yr−1. Although it is a radio source, according to the line diagnostics using Hβ, [O ii], and [O iii], photoionization by the central QSO is likely the dominant ionization mechanism rather than shocks caused by radio jets. On the other hand, the spectral energy distribution of the host galaxy is well characterized as a quiescent galaxy that has shut down star formation several hundred Myr ago. Our results suggest a scenario that QSOs are powered after the shutdown of the star formation and help complete the quenching of massive quiescent galaxies at high redshift.
The power of transfer learning in agricultural applications: AgriNet

Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94% and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model. Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir.
Uncertainty-Aware Personal Assistant for Making Personalized Privacy Decisions

Many software systems, such as online social networks, enable users to share information about themselves. Although the action of sharing is simple, it requires an elaborate thought process on privacy: what to share, with whom to share, and for what purposes. Thinking about these for each piece of content to be shared is tedious. Recent approaches to tackle this problem build personal assistants that can help users by learning what is private over time and recommending privacy labels such as private or public to individual content that a user considers sharing. However, privacy is inherently ambiguous and highly personal. Existing approaches to recommend privacy decisions do not address these aspects of privacy sufficiently. Ideally, a personal assistant should be able to adjust its recommendation based on a given user, considering that user’s privacy understanding. Moreover, the personal assistant should be able to assess when its recommendation would be uncertain and let the user make the decision on her own. Accordingly, this article proposes a personal assistant that uses evidential deep learning to classify content based on its privacy label. An important characteristic of the personal assistant is that it can model its uncertainty in its decisions explicitly, determine that it does not know the answer, and delegate from making a recommendation when its uncertainty is high. By factoring in the user’s own understanding of privacy, such as risk factors or own labels, the personal assistant can personalize its recommendations per user. We evaluate our proposed personal assistant using a well-known dataset. Our results show that our personal assistant can accurately identify uncertain cases, personalize them to its user’s needs, and thus helps users preserve their privacy well.
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships

As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable. However, exhaustively collecting and labeling the data necessary to fully test the long tail of rare and challenging scenarios is difficult and expensive. In this work, we construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. Specifically, we conduct an extensive labeling effort to identify causal agents, or agents whose presence influences human drivers' behavior in any format, in the Waymo Open Motion Dataset (WOMD), and we use these labels to perturb the data by deleting non-causal agents from the scene. We evaluate a diverse set of state-of-the-art deep-learning model architectures on our proposed benchmark and find that all models exhibit large shifts under even non-causal perturbation: we observe a 25-38% relative change in minADE as compared to the original. We also investigate techniques to improve model robustness, including increasing the training dataset size and using targeted data augmentations that randomly drop non-causal agents throughout training. Finally, we release the causal agent labels (at https://github.com/google-research/causal-agents) as an additional attribute to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting.
Constrained Training of Neural Networks via Theorem Proving

We introduce a theorem proving approach to the specification and generation of temporal logical constraints for training neural networks. We formalise a deep embedding of linear temporal logic over finite traces (LTL$_f$) and an associated evaluation function characterising its semantics within the higher-order logic of the Isabelle theorem prover. We then proceed to formalise a loss function $\mathcal{L}$ that we formally prove to be sound, and differentiable to a function $d\mathcal{L}$. We subsequently use Isabelle's automatic code generation mechanism to produce OCaml versions of LTL$_f$, $\mathcal{L}$ and $d\mathcal{L}$ that we integrate with PyTorch via OCaml bindings for Python. We show that, when used for training in an existing deep learning framework for dynamic movement, our approach produces expected results for common movement specification patterns such as obstacle avoidance and patrolling. The distinctive benefit of our approach is the fully rigorous method for constrained training, eliminating many of the risks inherent to ad-hoc implementations of logical aspects directly in an"unsafe"programming language such as Python.
Generative Adversarial Networks and Other Generative Models

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be highly successful – though by no means from the first attempt. This chapter gives a basic introduction into the motivation for Generative Adversarial Networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism, and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, and also typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
Berry-dipole photovoltaic demon and the thermodynamics of photocurrent generation within the optical gap of metals

We dismantle the previously held misconception that it is impossible for bulk rectification mechanisms to induce a net DC electric current when the frequency of the impinging radiation lies within the optical gap of a metal in the limit of small carrier relaxation rates. We argue that generically such in-gap rectification mechanisms are irreversible and accompanied by a continuous exchange of energy with a heat bath and must also be necessarily accompanied by a small but finite absorption of radiation in order to guarantee the positivity of the net entropy production and abide by the second law of thermodynamics. We show, however, that the intra-band non-linear Hall effect arising from the Berry curvature is a special kind of in-gap rectification mechanism that behaves as a ``photo-voltaic demon'', namely it can operate as an ideal reversible and dissipationless conveyor of energy between the radiation and an external circuit. Its reversible nature allows for an interesting mode of operation as an amplifier of circularly polarized light, whose efficiency can approach 100%, and which could be technologically promising especially in the infrared frequency range.
Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs’ gas consumption. Contrary to most of the works in the literature, the CAVs’ location uncertainty is accounted for and CAVs controls are re-optimized periodically, allowing for the optimization of system performance while creating safe trajectories. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT), which reaches a better trade-off among performance and computational and communication complexity. In a realistic simulation scenario, AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.
The Compatibility of the Minimalist Foundation with Homotopy Type Theory

The Minimalist Foundation, for short MF, is a two-level foundation for constructive mathematics ideated by Maietti and Sambin in 2005 and then fully formalized by Maietti in 2009. MF serves as a common core among the most relevant foundations for mathematics in the literature by choosing for each of them the appropriate level of MF to be translated in a compatible way, namely by preserving the meaning of logical and set-theoretical constructors. The two-level structure consists of an intensional level, an extensional one, and an interpretation of the latter in the former in order to extract intensional computational contents from mathematical proofs involving extensional constructions used in everyday mathematical practice. In 2013 a completely new foundation for constructive mathematics appeared in the literature, called Homotopy Type Theory, for short HoTT, which is an example of Voevodsky's Univalent Foundations with a computational nature. So far no level of MF has been proved to be compatible with any of the Univalent Foundations in the literature. Here we show that both levels of MF are compatible with HoTT. This result is made possible thanks to the peculiarities of HoTT which combines intensional features of type theory with extensional ones by assuming Voevodsky's Univalence Axiom and higher inductive quotient types. As a relevant consequence, MF inherits entirely new computable models.
Shrouded black holes in Einstein-Gauss-Bonnet gravity

We study black holes in a modiﬁed gravity scenario involving a scalar ﬁeld quadratically coupled to the Gauss-Bonnet invariant. The scalar is assumed to be in a spontaneously broken phase at spatial inﬁnity due to a bare Higgs-like potential. For a proper choice of sign, the non-minimal coupling to gravity leads to symmetry restoration near the black hole horizon, prompting the development of the scalar wall in its vicinity. The wall thickness depends on the bare mass of the scalar and can be much smaller than the Schwarzschild radius. In a weakly coupled regime, the quadratic coupling to the Gauss-Bonnet invariant eﬀectively becomes linear, and no walls are formed. We ﬁnd approximate analytical solutions for the scalar ﬁeld in the test ﬁeld regime, and obtain numerically static black hole solutions within this setup. We discuss cosmological implications of the model and show that it is fully consistent with the existence of an inﬂationary stage, unlike the spontaneous scalarization scenario assuming the opposite sign of the non-minimal coupling to gravity.
Hidden Schema Networks

Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly. In this work we introduce a novel neural language model that enforces, via inductive biases, explicit relational structures which allow for compositionality onto the output representations of pretrained language models. Specifically, the model encodes sentences into sequences of symbols (composed representations), which correspond to the nodes visited by biased random walkers on a global latent graph, and infers the posterior distribution of the latter. We first demonstrate that the model is able to uncover ground-truth graphs from artificially generated datasets of random token sequences. Next, we leverage pretrained BERT and GPT-2 language models as encoder and decoder, respectively, to infer networks of symbols (schemata) from natural language datasets. Our experiments show that (i) the inferred symbols can be interpreted as encoding different aspects of language, as e.g. topics or sentiments, and that (ii) GPT-2-like models can effectively be conditioned on symbolic representations. Finally, we explore training autoregressive, random walk “reasoning” models on schema networks inferred from commonsense knowledge databases, and using the sampled paths to enhance the performance of pretrained language models on commonsense If-Then reasoning tasks.
Big Learning: A Universal Machine Learning Paradigm?

Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learning. Specifically, the big learning exhaustively exploits the information inherent in its large-scale complete/incomplete training data, by simultaneously modeling many/all joint/conditional/marginal data distributions across potentially diverse domains, with one universal foundation model. We reveal that big learning ($i$) underlies most existing foundation models, ($ii$) is equipped with extraordinary flexibilities for complete/incomplete training data and trustworthy data tasks, ($iii$) is capable of delivering all joint/conditional/marginal data capabilities with one universal model, and ($iv$) unifies conventional machine learning paradigms and enables their flexible cooperations, manifested as a universal learning paradigm. Diverse experiments are carried out to validate the effectiveness of the presented big learning.
A new perspective on the fictitious space lemma

In the present contribution we propose a new proof of the so-called fictitious space lemma. For the proof, we exhibit an explicit expression for the inverse of additive Schwarz preconditionners in terms of Moore-Penrose pseudo inverse of the map associated to the decomposition over the subdomain partition. We will first briefly recall the definition of the pseudo-inverse of a matrix and some of its remarkable properties. We will then explain how this concept can be used to reformulate the fictitious space lemma in a very compact form. We will then give an aletrnative proof of the fictious space lemma. As a remarkable feature, this proof does not rely on Cauchy-Schwarz inequality, as opposed to previous proofs provided by Nepomnyaschikh [5, 4], Griebel and Oswald [2] or Dolean, Jolivet and Nataf [1], see also [6]. The present proof applies directly in the infinite dimensional case.
Pruning Early Exit Networks

Deep learning models that perform well often have high computational costs. In this paper, we combine two approaches that try to reduce the computational cost while keeping the model performance high: pruning and early exit networks. We evaluate two approaches of pruning early exit networks: (1) pruning the entire network at once, (2) pruning the base network and additional linear classifiers in an ordered fashion. Experimental results show that pruning the entire network at once is a better strategy in general. However, at high accuracy rates, the two approaches have a similar performance, which implies that the processes of pruning and early exit can be separated without loss of optimality.
Nuclear geometry at high energy from exclusive vector meson production

We show that when saturation effects are included one obtains a good description of the exclusive $\mathrm{J}/\psi$ production spectra in ultra peripheral lead-lead collisions as recently measured by the ALICE Collaboration at the LHC. As exclusive spectra are sensitive to the spatial distribution of nuclear matter at small Bjorken-$x$, this implies that gluon saturation effects modify the impact parameter profile of the target as we move towards small $x$. In addition to saturation effects, we find a preference for larger nuclear strong-interaction radii compared to the typical charge radius. We demonstrate the role of finite photon transverse momentum and the interference between the cases for which the role of photon emitter and target are switched between the nuclei. We show that these effects are comparable to the experimental precision for $p_T$-differential cross sections and as such need to be included when comparing to LHC data. Finally, the integrated $\mathrm{J}/\psi$ production cross sections from the LHC and preliminary transverse momentum spectra from RHIC are shown to prefer calculations with fluctuating nucleon substructure, although these datasets would require even stronger saturation effects than predicted from our framework.