Miracle-dz/UniFAR-FTUs huggingface dataset:
Ab initio calculations of neutrinoless $\beta \beta$ decay refine neutrino mass limits

Neutrinos are perhaps the most elusive known particles in the universe. We know they have some nonzero mass, but unlike all other particles, the absolute scale remains unknown. In addition, their fundamental nature is uncertain; they can either be their own antiparticles or exist as distinct neutrinos and antineutrinos. The observation of the hypothetical process of neutrinoless double-beta ($0\nu\beta\beta$) decay would at once resolve both questions, while providing a strong lead in understanding the abundance of matter over antimatter in our universe. In the scenario of light-neutrino exchange, the decay rate is governed by, and thereby linked to the effective mass of the neutrino via, the theoretical nuclear matrix element (NME). In order to extract the neutrino mass, if a discovery is made, or to assess the discovery potential of next-generation searches, it is essential to obtain accurate NMEs for all isotopes of experimental interest. However, two of the most important cases, $^{130}$Te and $^{136}$Xe, lie in the heavy region and have only been accessible to phenomenological nuclear models. In this work we utilize powerful advances in ab initio nuclear theory to compute NMEs from the underlying nuclear and weak forces driving this decay, including the recently discovered short-range component. We find that ab initio NMEs are generally smaller than those from nuclear models, challenging the expected reach of future ton-scale searches as well as claims to probe the inverted hierarchy of neutrino masses. With this step, ab initio calculations with theoretical uncertainties are now feasible for all isotopes relevant for next-generation $0\nu\beta\beta$ decay experiments.
Worrisome Properties of Neural Network Controllers and Their Symbolic Representations

We raise concerns about controllers' robustness in simple reinforcement learning benchmark problems. We focus on neural network controllers and their low neuron and symbolic abstractions. A typical controller reaching high mean return values still generates an abundance of persistent low-return solutions, which is a highly undesirable property, easily exploitable by an adversary. We find that the simpler controllers admit more persistent bad solutions. We provide an algorithm for a systematic robustness study and prove existence of persistent solutions and, in some cases, periodic orbits, using a computer-assisted proof methodology.
From Probabilistic Programming to Complexity-based Programming

The paper presents the main characteristics and a preliminary implementation of a novel computational framework named CompLog. Inspired by probabilistic programming systems like ProbLog, CompLog builds upon the inferential mechanisms proposed by Simplicity Theory, relying on the computation of two Kolmogorov complexities (here implemented as min-path searches via ASP programs) rather than probabilistic inference. The proposed system enables users to compute ex-post and ex-ante measures of unexpectedness of a certain situation, mapping respectively to posterior and prior subjective probabilities. The computation is based on the specification of world and mental models by means of causal and descriptive relations between predicates weighted by complexity. The paper illustrates a few examples of application: generating relevant descriptions, and providing alternative approaches to disjunction and to negation.
Evolution of the Lyman-alpha-emitting fraction and UV properties of lensed star-forming galaxies between 2.9 < z < 6.7

Faint galaxies are theorised to have played a major role in reionising the Universe. Their properties as well as the Lyman-{\alpha} emitter fraction, could provide useful insight into this epoch. We use four galaxy clusters from the Lensed Lyman-alpha MUSE Arcs Sample (LLAMAS) which also have deep HST photometry to select a population of intrinsically faint Lyman Break Galaxies (LBGs) and Lyman-alpha Emitters (LAEs). We study the interrelation of these two populations, their properties, and the fraction of LBGs that display Lyman-alpha emission. The use of lensing clusters allows us to access an intrinsically faint population, the largest sample collected for this purpose: 263 LAEs and 972 LBGs between redshifts of 2.9 and 6.7, Lyman-alpha luminosities between 39.5<log(L)(erg/s)<42 and absolute UV magnitudes between -22<M1500<-12. We find a redshift evolution of the Lyman-alpha emitter fraction in line with past results, with diminished values above z = 6, taken to signify an increasingly neutral intervening IGM. Inspecting this redshift evolution with different limits on Lyman-alpha equivalent width (EW) and M1500 we find that the Lyman-alpha emitter fraction for the UV-brighter half of our sample is higher than the fraction for the UV-fainter half, a difference which increases at higher redshift. This is a surprising result and can be interpreted as a population of low Lyman-alpha EW, UV-bright galaxies situated in reionised bubbles. This result is especially interesting in the context of similar, UV-bright, low Lyman-alpha EW objects recently detected around the epoch of reionisation. We extend to intrinsically fainter objects the previously observed trends of LAEs among LBGs as galaxies with high star-formation rates and low dust content, as well as the strongest LAEs having in general fainter UV magnitudes and steeper UV slopes.
Long-Range Attraction between Graphene and Water/Oil Interfaces

We directly measured the interactions between a hydrophobic solid and a hydrophobic liquid separated by water using force spectroscopy, where colloidal probes were coated with graphene oxide (GO) to interact with immobilized heptane droplets in water. We detected attractions with a long range of ~0.5 microns, which cannot be readily explained by standard Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. When the GO was reduced to become more hydrophobic, these forces increased in strength and ranged up to 1.2 microns, suggesting that the hydrophobic nature of the involved surfaces critically influences the observed long-range forces. Previous studies have addressed such hydrophobic attractions, but were limited to solid/water/solid and solid/water/air scenarios. Here we expand this knowledge to include the solid/water/liquid situation. Based on our results, we propose air bubbles attached to the colloidal probe and molecular rearrangement at the water/oil interface as possible origins of the observed interactions. The proposed mechanism expands insights gained from previous to the solid/water/liquid situation and is universally applicable to describe attractive interactions between hydrophobic bodies of any kind separated by water. Our work will be useful to understand and motivate the formation of many colloid and interface phenomena, including emulsions using 2D materials and other amphiphilic/hydrophobic particles.
On Swan exponents of symmetric and exterior square Galois representations

Let $F$ be a local non-Archimedean field and $E$ a finite Galois extension of $F$, with Galois group $G$. If $\rho$ is a representation of $G$ on a complex vector space $V$, we may compose it with any tensor operation $R$ on $V$, and get another representation $R\circ\rho$. We study the relation between the Swan exponents $\mathrm{Sw}(\rho)$ and $\mathrm{Sw}(R\circ\rho)$, with a particular attention to the cases where $R$ is symmetric square or exterior square. Indeed those cases intervene in the local Langlands correspondence for split classical groups over $F$, via the formal degree conjecture, and we present some applications of our work to the explicit description of the Langlands parameter of simple cuspidal representations. For irreducible $\rho$ our main results determine $\mathrm{Sw}(\mathrm{Sym}^{2}\rho)$ and $\mathrm{Sw}(\wedge^{2}\rho)$ from $\mathrm{Sw}(\rho)$ when the residue characteristic $p$ of $F$ is odd, and bound them in terms of $\mathrm{Sw}(\rho)$ when $p$ is $2$. In that case where $p$ is $2$ we conjecture stronger bounds, for which we provide evidence.
Penrose limit of T-dual of MNa solution and dual orbifold field theory

In this paper we study the Penrose limit of the T-dual of the MNa solution and its field theory dual, in order to better understand the effect of T-duality in this case. We find a matching of string pp wave oscillators and their masses to the field theory modes, that are rearranged after T-duality. The effect of T-duality on the long"annulon-type"operators is found, as a symmetry of the 2+1 dimensional confining theory with spontaneous susy breaking.
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning Implemented on Nvidia Edge GPU Device

Children's safety in the home has become an increasing social concern, and the purpose of this experiment is to use machine learning to detect child abuse scenarios in order to improve children's safety. This experiment uses machine learning to classify and recognize children's voices and to predict whether the current sound the child is making is crying, screaming or laughing. If a crying or screaming sound is detected from a child, an immediate alert is sent to the relevant personnel so that they can sense what the child may be experiencing and respond in a timely manner in the blind spot of the surveillance. Together with the hybrid use of video image classification, the accuracy of child abuse detection can be greatly improved. This greatly reduces the likelihood of children being violently abused in childcare centers and allows staff to stop impending or recent child abuse incidents in a timely manner. The dataset collected for this experiment was derived exclusively from sounds recorded on-site at the children's home, including cries, laughter, screams, and background noise. In this experiment, the short-time Fourier transform is used to transform the sound signals into spectrograms, and the image signals are imported into a convolutional neural network for training and prediction, and the final trained model has an accuracy of about 92% for sound detection.
PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace

Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, these models may only be accessible through paid cloud-based mechanisms, which can prove costly for general public utilization. Moreover, model and data providers require a more streamlined approach to track resource usage and capitalize on subsequent usage by others, both financially and otherwise. An effective mechanism is also lacking to contribute high-quality data for improving model performance. We propose a blockchain-based marketplace called"PredictChain"for predictive machine-learning models to address these issues. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models. Nodes within the blockchain network, equipped with available computing resources, will operate these models, offering a range of archetype machine learning models with varying characteristics, such as cost, speed, simplicity, power, and cost-effectiveness. This decentralized approach empowers users to develop improved models accessible to the public, promotes data sharing, and reduces reliance on centralized cloud providers.
The Initial Screening Order Problem

We investigate the role of the initial screening order (ISO) in candidate screening processes, such as hiring and academic admissions. ISO refers to the order in which the screener sorts the candidate pool before the evaluation. It has been largely overlooked in the literature, despite its potential impact on the optimality and fairness of the chosen set, especially under a human screener. We define two problem formulations: best-$k$, where the screener chooses the $k$ best candidates, and good-$k$, where the screener chooses the first $k$ good-enough candidates. To study the impact of ISO, we introduce a human-like screener and compare to its algorithmic counterpart. The human-like screener is conceived to be inconsistent over time due to fatigue. Our analysis shows that the ISO under a human-like screener hinders individual fairness despite meeting group level fairness. This is due to the position bias, where a candidate's evaluation is affected by its position within ISO. We report extensive simulated experiments exploring the parameters of the problem formulations both for algorithmic and human-like screeners. This work is motivated by a real world candidate screening problem studied in collaboration with a large European company.
Learning Compliant Stiffness by Impedance Control-Aware Task Segmentation and Multi-Objective Bayesian Optimization with Priors

Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a novel stiffness learning method to satisfy both task performance and compliance requirements. The proposed method optimizes the task and compliance objectives ($T/C$ objectives) simultaneously via multi-objective Bayesian optimization. We define the stiffness search space by segmenting a demonstration into task phases, each with constant responsible stiffness. The segmentation is performed by identifying impedance control-aware switching linear dynamics (IC-SLD) from the demonstration. We also utilize the stiffness obtained by proposed IC-SLD as priors for efficient optimization. Experiments on simulated tasks and a real robot demonstrate that IC-SLD-based segmentation and the use of priors improve the optimization efficiency compared to existing baseline methods.
ReMKiT1D -- A framework for building reactive multi-fluid models of the tokamak Scrape-Off Layer with coupled electron kinetics in 1D

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treatment of the electron kinetic equation. As such, the framework allows for flexibility in fluid models of the Scrape-Off Layer while allowing the easy addition of kinetic electron effects. We focus on presenting both the high-level design decisions that allow for model flexibility, as well as the most important implementation aspects. A significant number of verification and performance tests are presented, as well as a step-by-step walkthrough of a simple example for setting up models using the Python interface.
Berezinskii Approach to Disordered Spin Systems with Asymmetric Scattering and Application to the Quantum Boomerang Effect

We extend the Berezinskii diagrammatic technique to one-dimensional disordered spin systems, in which time-reversal invariance is broken due to a spin-orbit coupling term inducing left-right asymmetric scattering. We then use this formalism to theoretically describe the dynamics of the quantum boomerang effect, a recently discovered manifestation of Anderson localization. The theoretical results are confirmed by exact numerical simulations of wave-packet dynamics in a random potential.
Reducing Latency of DAG-based Consensus in the Asynchronous Setting via the UTXO Model

DAG-based consensus has attracted significant interest due to its high throughput in asynchronous network settings. However, existing protocols such as DAG-rider (Keidar et al., PODC 2021) and ``Narwhal and Tusk'' (Danezis et al., Eurosys 2022) face two undesired practical issues: (1) high transaction latency and (2) high cost to verify transaction outcomes. To address (1), this work introduces a novel commit rule based on the Unspent Transaction Output (UTXO) Data Model, which allows a node to predict the transaction results before triggering the commitment. We propose a new consensus algorithm named ``Board and Clerk'', which reduces the transaction latency by half for roughly 50% of transactions. As the tolerance for faults escalates, more transactions can partake in this latency reduction. In addition, we also propose the Hyper-Block Model with two flexible proposing strategies to tackle (2): blocking and non-blocking. Using our proposed strategies, each node first predicts the transaction results if its proposal is committed and packs this result as a commitment in its proposal. The hyper-block packs the signature of the proposal and the outputs of the consensus layer together in order to prove the transaction results.
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition

Multimodal Emotion Recognition in Conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information, rendering it hard to adequately extract complementary and associative information from multimodal data. To cope with this problem, in CFN-ESA, textual modalities are treated as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFN-ESA mainly consists of the unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together the data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture related information, thereby guide the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform the state-of-the-art models.
Emergence and identity of quantum particles

According to classical physics, particles are basic constituents of the physical world. Quantum theory is much less friendly to particles; in particular, relativistic quantum field theory (RQFT) creates serious obstacles for the idea that particles are fundamental. Apparently, when moving from the domain of RQFT to that of classical mechanics (CM), particles have to emerge at some stage. It is standard to assume that this emergence has been completed at the level of quantum mechanics, halfway between RQFT and CM, even though particles of the same kind in many-particle quantum mechanics have the curious feature of being ‘entities without identity’. Against this ‘Received View’ about the nature of quantum particles we outline and defend an Alternative View (AV), in which the emergent character of particles is emphasized. According to this AV, the step to a particle theory has not yet been made in quantum mechanics: conditions have still to be satisfied in order to make the particle concept applicable. If these conditions are met, the quantum particles that emerge are distinguishable individuals possessing physically defined identities, in stark contrast to what the Received View asserts. We will compare and contrast the two Views, both from a physical and a logical/conceptual point of view. This article is part of the theme issue ‘Identity, individuality and indistinguishability in physics and mathematics’.
Nonviral Gene Therapy Approaches to Hemophilia

The goal of hemophilia gene therapy is to obtain long-term therapeutic levels of factor VIII (FVIII) or factor IX (FIX) without stimulating an immune response against the transgene product or the vector. The success of gene therapy is largely dependent on the development of appropriate gene delivery vectors. Both viral vectors and nonviral vectors have been considered for the development of hemophilia gene therapy. In general, viral vectors are far more efﬁcient than nonviral gene delivery approaches and resulted in long-term therapeutic levels of FVIII or FIX in preclinical animal models. However, there are several reasons why a nonviral treatment would still be desirable, particularly because some viral vectors are associated with inﬂammatory reactions, that render transgene expression transient, or with an increased risk of insertional oncogenesis when random integrating vectors are used. Nonviral vectors may obviate some of these concerns. Since nonviral vectors are typically assembled in cell-free systems from well-deﬁned components, they have signiﬁcant manufacturing advantages over viral vectors. The continued development of improved nonviral gene delivery approaches offers new perspectives for gene therapy of chronic diseases including hemophilia
Assessment of Pulmonary Function in COPD

Pulmonary function testing is used in the diagnosis of chronic obstructive pulmonary disease (COPD) and the staging of COPD severity. The current diagnostic criterion for airﬂow obstruction is a ratio of forced expiratory volume in 1 second (FEV 1 ) to forced vital capacity (FVC) < 70%. However this absolute deﬁnition can lead to false-negative determinations in younger patients and false-positive determinations in the elderly. Nevertheless, screening spirometry is advocated and becomes feasible in the physician ofﬁce setting with the availability of compact, relatively affordable apparatus that meets the appropriate technical speciﬁcations. Spirometry should be complemented by measurement of lung volumes using body plethysmography in those with evidence of airﬂow obstruction. Small airways disease can be detected by various techniques that measure airway and total respiratory system resistance. There is renewed interest in the forced oscillation technique and impulse oscillometry because of their noninvasiveness and potential ability to distinguish small from larger airway disease. Finally, pulmonary function testing has an important role in preoperative risk assessment; for example, in patients being considered for lung volume reduction surgery or resection of a lung nodule.
Engineering the Follicle Microenvironment

In vitro ovarian follicle culture provides a tool to investigate folliculogenesis, and may one day provide women with fertility-preservation options. The application of tissue engineering principles to ovarian follicle maturation may enable the creation of controllable microenvironments that will coordinate the growth of the multiple cellular compartments within the follicle. Three-dimensional culture systems can preserve follicle architecture, thereby maintaining critical cell–cell and cell–matrix signaling lost in traditional two-dimensional attached follicle culture systems. Maintaining the follicular structure while manipulating the biochemical and mechanical environment will enable the development of controllable systems to investigate the fundamental biological principles underlying follicle maturation. This review describes recent advances in ovarian follicle culture, and highlights the tissue engineering principles that may be applied to follicle culture, with the ultimate objective of germline preservation for females facing premature infertility.