Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence Learning using Equilibrium Propagation

Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fact that it relies only on local computations and requires solely one kind of computational unit during both of its training phases, thereby enabling greater applicability in domains such as bio-inspired neuromorphic computing. The dynamics of the model in EP is governed by an energy function and the internal states of the model consequently converge to a steady state following the state transition rules defined by the same. However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training. Thus it is not possible to design a model for sequence classification using EP with an LSTM or GRU like architecture. In this paper, we leverage recent developments in modern hopfield networks to further understand energy based models and develop solutions for complex sequence classification tasks using EP while satisfying its convergence criteria and maintaining its theoretical similarities with recurrent backpropagation. We explore the possibility of integrating modern hopfield networks as an attention mechanism with convergent RNN models used in EP, thereby extending its applicability for the first time on two different sequence classification tasks in natural language processing viz. sentiment analysis (IMDB dataset) and natural language inference (SNLI dataset). Our implementation source code is available at https://github.com/NeuroCompLab-psu/EqProp-SeqLearning.
Young Open Clusters as Probes of the Star Formation Process

This paper is the second in a series devoted to the use of young, optically revealed open clusters as probes of the star-formation process. The first paper in the series [Phelps & Janes, ApJS (1993) (in press)] presented the dataset used for the study, and discussed some of the results from the survey of 23 open clusters in the Cassipoeia region of the Perseus spiral arm of our Galaxy. This paper presents an analysis of the stellar content of a subset of the clusters, including determinations of the luminosity and mass functions of 8 of the clusters to ∼1M ○. The slope of the average mass function Γ is found to be −1.40±0.13 over the mass range 1.4<M/M ○. <7.9. Significant variations from this average value are found for two of the clusters, NGC 581 and NGC 663
Measurement of decay-time dependent CP violation in B 0 → K 0 S K 0 S K 0 S using 2019–2021 Belle II data The Belle II Collaboration

We report a measurement of decay-time dependent CP -violating parameters in B 0 → K 0 S K 0 S K 0 S decays. We use (198 . 0 ± 3 . 0) × 10 6 BB pairs collected at the Υ(4 S ) resonance with the Belle II detector at the SuperKEKB asymmetric-energy e + e − collider. The observed mixing-induced and direct CP violation parameters are S = − 1 . 86 +0 . 91 − 0 . 46 (stat) ± 0 . 09 (syst) and A = − 0 . 22 +0 . 30 − 0 . 27 (stat) ± 0 . 04 (syst), respectively.
Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility

Responsible artificial intelligence guidelines ask engineers to consider how their systems might harm. However, contemporary artificial intelligence systems are built by composing many preexisting software modules that pass through many hands before becoming a finished product or service. How does this shape responsible artificial intelligence practice? In interviews with 27 artificial intelligence engineers across industry, open source, and academia, our participants often did not see the questions posed in responsible artificial intelligence guidelines to be within their agency, capability, or responsibility to address. We use Suchman's “located accountability” to show how responsible artificial intelligence labor is currently organized and to explore how it could be done differently. We identify cross-cutting social logics, like modularizability, scale, reputation, and customer orientation, that organize which responsible artificial intelligence actions do take place and which are relegated to low status staff or believed to be the work of the next or previous person in the imagined “supply chain. ” We argue that current responsible artificial intelligence interventions, like ethics checklists and guidelines that assume panoptical knowledge and control over systems, could be improved by taking a located accountability approach, recognizing where relations and obligations might intertwine inside and outside of this supply chain.
Reflections and Considerations on Running Creative Visualization Learning Activities

This paper draws together nine strategies for creative visualization activities. Teaching visualization often involves running learning activities where students perform tasks that directly support one or more topics that the teacher wishes to address in the lesson. As a group of educators and researchers in visualization, we reflect on our learning experiences. Our activities and experiences range from dividing the tasks into smaller parts, considering different learning materials, to encouraging debate. With this paper, our hope is that we can encourage, inspire, and guide other educators with visualization activities. Our reflections provide an initial starting point of methods and strategies to craft creative visualisation learning activities, and provide a foundation for developing best practices in visualization education.
Twitter Topic Classification

Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.
Boundary conditions for the quantum Hall effect

We formulate a self-consistent model of the integer quantum Hall effect on an infinite strip, using boundary conditions to investigate the influence of finite-size effects on the Hall conductivity. By exploiting the translation symmetry along the strip, we determine both the general spectral properties of the system for a large class of boundary conditions respecting such symmetry, and the full spectrum for (fibered) Robin boundary conditions. In particular, we find that the latter introduce a new kind of states with no classical analogues, and add a finer structure to the quantization pattern of the Hall conductivity. Moreover, our model also predicts the breakdown of the quantum Hall effect at high values of the applied electric field.
Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA

In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation non-orthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of sub-carriers for transmitting their data modulated by the sub-carrier index modulation technique. More particularly, users of SC-IM-NOMA simultaneously transmit their SC-IM data at different power levels which are then exploited by their receivers to perform successive interference cancellation (SIC) multi-user detection. The existing detectors designed for SC-IM-NOMA, such as the joint maximum-likelihood (JML) detector and the maximum likelihood SIC-based (ML-SIC) detector, suffer from high computational complexity. To address this issue, we propose a DNN-based detector whose structure relies on the model-based SIC for jointly detecting both M-ary symbols and index bits of all users after trained with sufficient simulated data. The simulation results demonstrate that the proposed DNN-based detector attains near-optimal error performance and significantly reduced runtime complexity in comparison with the existing hand-crafted detectors.
Finitary models of language users

been proposed to describe talkers and listeners to describe the users of language rather than the language itself. As m was pointed out at the beginning of Chapter 12, our language is not merely the collection of our linguistic responses, habits, or dispositions, just as our knowledge of arithmetic is not merely the collection of our arithmetic responses, habits, or dispositions. We must respect this distinction between the person's knowledge and his actual or even potential behavior; a formal characterization of some language is not simultaneously a model of the users of that
The rank 2 classification problem I: scale invariant geometries

: In this ﬁrst of a series of three papers we outline an approach to classifying 4d N =2 superconformal ﬁeld theories at rank 2. The classiﬁcation of allowed scale invariant N = 2 Coulomb branch geometries of dimension (or rank) greater than one is a famous open problem whose solution will greatly constrain the space of N =2 superconformal ﬁeld theories. At rank 2 the problem is equivalent to ﬁnding all possible genus 2 Seiberg-Witten curves and 1-forms satisfying a special K¨ahler condition. This is tractable because regular genus 2 Riemann surfaces can be uniformly described as binary-sextic plane curves, and the Seiberg-Witten curves are families of such curves varying meromorphically over the two-dimensional base. There are also solutions con-sisting of families of degenerate genus-2 Riemann surfaces given by a bouquet of two elliptic curves which are described by a diﬀerent set of curves. In this paper we set up and carry out the analysis of the generic case, i.e. , those whose typical ﬁber is a regular genus-2 Riemann surface with no extended automorphism, and ﬁnd the complete answer for polynomial coeﬃcients.
Machine Learning Models Evaluation and Feature Importance Analysis on NPL Dataset

Predicting the probability of non-performing loans for individuals has a vital and beneﬁcial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore different feature selection methods to state the important features for the bank. Our ﬁndings show that XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data. We also found that the most important features are the age of the applicant, years of employment, and total income of the applicant rather than collateral-related features in evaluating credit risk.
Frictionless Motion of Diffuse Interfaces by Sharp Phase-Field Modeling

Diffuse interface descriptions offer many advantages for the modeling of microstructure evolution. However, the numerical representation of moving diffuse interfaces on discrete numerical grids involves spurious grid friction, which limits the overall performance of the model in many respects. Interestingly, this intricate and detrimental effect can be overcome in finite difference (FD) and fast Fourier transformation (FFT)-based implementations by employing the so-called sharp phase-field method (SPFM). The key idea is to restore the discretization-induced broken translational invariance (TI) in the discrete phase-field equation by using analytic properties of the equilibrium interface profile. We prove that this method can indeed eliminate spurious grid friction in the three-dimensional space. Focusing on homogeneous driving forces, we quantitatively evaluate the impact of spurious grid friction on the overall operational performance of different phase-field models. We show that the SPFM provides superior degrees of interface isotropy with respect to energy and kinetics. The latter property enables the frictionless motion of arbitrarily oriented diffuse interfaces on a fixed 3D grid.
Iso-Dream: Isolating Noncontrollable Visual Dynamics in World Models

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollable dynamics independent of the action signals, making it difﬁcult to learn effective world models. To tackle this problem, we present a novel reinforcement learning approach named Iso-Dream, which improves the Dream-to-Control framework [22] in two aspects. First, by optimizing the inverse dynamics , we encourage the world model to learn controllable and noncontrollable sources of spatiotemporal changes on isolated state transition branches. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model. Speciﬁcally, to estimate state values, we roll-out the noncontrollable states into the future and associate them with the current controllable state . In this way, the isolation of dynamics sources can greatly beneﬁt long-horizon decision-making of the agent, such as a self-driving car that can avoid potential risks by anticipating the movement of other vehicles. Experiments show that Iso-Dream is effective in decoupling the mixed dynamics and remarkably outperforms existing approaches in a wide range of visual control and prediction domains.
Maximizing spectral flux from self-seeding hard x-ray free electron lasers

Fully coherent x-rays can be generated by self-seeding x-ray free-electron lasers (XFELs). Self-seeding by a forward Bragg diffraction (FBD) monochromator has been recently proposed [1] and demonstrated [2]. Characteristic time To of FBD determines the power, spectral, and time characteristics of the FBD seed [3]. Here we show that for a given electron bunch with duration sigma_e the spectral flux of the self-seeding XFEL can be maximized, and the spectral bandwidth can be respectively minimized by choosing To ~ sigma_e/pi and by optimizing the electron bunch delay tau_e. The choices of To and tau_e are not unique. In all cases, the maximum value of the spectral flux and the minimum bandwidth are primarily determined by sigma_e. Two-color seeding takes place To >> sigma_e/\pi. The studies are performed, for a Gaussian electron bunch distribution with the parameters, close to those used in the short-bunch (sigma_e ~ 5 fs) and long-bunch (sigma_e ~ 20 fs) operation modes of the LCLS XFEL.
Hubble Space Telescope and Chandra Monitoring of the Crab Synchrotron Nebula

We report the results of coordinated Hubble Space Telescope and Chandra observations of the Crab synchrotron nebula. Similar dynamical structures, including equatorial wisps moving outward at ~0.5c, are seen in both passbands. The inner X-ray ring is a variable, irregular structure from which wisps and diffuse emission emerge. The X-ray/visible jet to the southeast of the pulsar is a true jet. The data support the interpretation that the inner ring and a highly dynamical feature at the base of the jet are unstable quasi-stationary shocks in the "cold" equatorial wind and polar jet from the pulsar.
Particle with spin S=3/2 in Riemannian space-time

Equations for 16-component vector-bispinor field, originated from Rarita-Schwinger Lagrangian for spin 3/2 field extended to Riemannian space-time are investigated. Additional general covariant constrains for the field are produced, which for some space-time models greatly simplify original wave equation. Peculiarities in description of the massless spin 3/2 field are specified. In the flat Minkowski space for massless case there exist gauge invariance of the main wave equation, which reduces to possibility to produce a whole class of trivial solutions in the the form of 4-gradient of arbitrary (gauge) bispinor function, \Psi ^{0}_{c} = \partial_{c} \psi. Generalization of that property for Riemannian model is performed; it is shown that in general covariant case solutions of the gradient type \Psi^{0}_{\beta} = (\nabla_{\beta} + \Gamma_{\beta})\Psi exist in space-time regions where the Ricci tensor obeys an identity R_{\alpha \beta} - {1 \over 2} R g_{\alpha \beta} = 0.
Towards a Modular Data Management System Framework

Today’s data management systems (DMSes) are implemented using monolithic architectures. This is explained by the fact that, historically, the DMS market was dominated by commercial solutions. However, with the advent of open source, lots of alternative DMSes and DMS components have emerged. Yet, they cannot be easily integrated because DMSes are still designed as monoliths. We pro-pose PolyDMS, our vision towards composable DMSes. The core idea of PolyDMS is to split up and wrap individual components with standardized interfaces, such that a Component Orchestrator can flexibly construct DMSes out of them. Users can compose new DMSes by writing small programs in our System Definition Language. PolyDMS allows them to quickly instantiate new DMSes according to their needs while giving them the opportunity to reuse existing components. Our proof-of-concept implementation shows that composing multiple components into a new DMS can yield additional functionality and better performance.
Degenerate pathway for processing smile and other emotional expressions in congenital facial palsy: an hdEEG investigation

Influential theoretical models argue that an internal simulation mechanism (motor or sensorimotor simulation) supports the recognition of facial expressions. However, despite numerous converging sources of evidence, recent studies testing patients with congenital facial palsy (i.e. Moebius syndrome) seem to refute these theoretical models. However, these results do not consider the principles of neuroplasticity and degeneracy that could support the involvement of an alternative neural processing pathway in these patients. In the present study, we tested healthy participants and participants with Moebius syndrome in a highly sensitive facial expression discrimination task and concomitant high-density electroencephalographic recording. The results, both at the scalp and source levels, indicate the activation of two different pathways of facial expression processing in healthy participants and participants with Moebius syndrome, compatible, respectively, with a dorsal pathway that includes premotor areas and a ventral pathway. Therefore, these results support the reactivation of sensorimotor representations of facial expressions (i.e. simulation) in healthy subjects, in the place of an alternative processing pathway in subjects with congenital facial palsy. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.
Reconstructing radial stem size changes of trees with machine learning

Like many scientists, ecologists depend heavily on continuous uninterrupted data in order to understand better the object of their study. Although this might be straightforward to achieve under controlled laboratory conditions, the situation is easily complicated under field conditions where sensors and data transmission are affected by harsh weather, living organisms, changes in atmospheric conditions etc. This often results in parts of the data being corrupted or missing altogether. We propose the use of the most recent machine-learning techniques to reverse such data losses in multi-channel time series. In particular, we focus on tree stem growth data obtained from the TreeNet project, which monitors the changes in stem radius and environmental conditions of a few hundred trees across Switzerland. In the first part of the study, we test the performance of five architectures based on encoders and recurrent and convolutional neural networks, and we show that a deep neural network combining long short-term memory with one-dimensional convolutional layers performs the best. In the second part, we adopt this model to reconstruct the original TreeNet dataset, which we then use in a separate classification problem to show the effect of the proposed gap-filling procedure.
A minimal model of elastic instabilities in biological filament bundles

We present a model of a system of elastic fibres which exhibits complex, coupled, nonlinear deformations via a connecting elastic spring network. This model can capture physically observed deformations such as global buckling, pinching and internal collapse. We explore the transitions between these deformation modes numerically, using an energy minimization approach, highlighting how supported environments, or stiff outer sheath structures, favour internal structural collapse over global deformation. We then derive a novel analytic buckling criterion for the internal collapse of the system, a mode of structural collapse pertinent in many biological filament bundles such as the optic nerve bundle and microtubule bundles involved in cell abscission.
Risk-taking neonates do not pay a survival cost in a free-ranging large mammal, the fallow deer (Dama dama)

Recent debate has focused on whether variation in personality primarily reflects variation in resource allocation or resource acquisition of individuals. These two mechanisms predict different relationships between personality and survival. If personality mainly reflects variation in resource allocation, then bold (i.e. risk-taking) individuals are expected to live shorter lives, whereas the opposite pattern is expected with resource acquisition. Here we studied the relationship between neonate personality and early-life survival in 269 juveniles of a population of fallow deer (Dama dama). We found that bolder individuals paid no apparent survival cost. Interestingly, among-individual differences in the physiological response at capture (heart rates, which covary with the behavioural response, i.e. latency to leave) were linked to survival, where individuals with lower heart rates when handled by humans had a higher probability of early-life survival. This suggests that bolder individuals may be of higher state than their shyer counterparts. As the first study linking neonate personality to survival in a free-ranging mammal, we provide novel insights into drivers behind early-life individual variation.
A general swimming response in exhausted obligate swimming fish

Marine organisms normally swim at elevated speeds relative to cruising speeds only during strenuous activity, such as predation or escape. We measured swimming speeds of 29 ram ventilating sharks from 10 species and of three Atlantic bluefin tunas immediately after exhaustive exercise (fighting a capture by hook-and-line) and unexpectedly found all individuals exhibited a uniform mechanical response, with swimming speed initially two times higher than the cruising speeds reached approximately 6 h later. We hypothesized that elevated swimming behaviour is a means to increase energetic demand and drive the removal of lactate accumulated during capture via oxidation. To explore this hypothesis, we estimated the mechanical work that must have been spent by an animal to elevate its swim speed and then showed that the amount of lactate that could have been oxidized to fuel it comprises a significant portion of the amount of lactate normally observed in fishes after exhaustive exercise. An estimate for the full energetic cost of the catch-and-release event ensued.