Miracle-dz/UniFAR-FTUs huggingface dataset:
In Praise and in Criticism of the Model of Continuous Spontaneous Localization of the Wave-Function

Different attempts to solve the measurement problem of the quantum mechanics (QM) by denying the collapse principle, and replacing it with changes in the quantum formalism, failed because the changes in the formalism lead to contradictions with QM predictions. To the difference, Ghirardi, Rimini and Weber took the collapse as a real phenomenon, and proposed a calculus by which the wave-function should undergo a sudden localization. Later on, Ghirardi, Pearle and Rimini came with a change of this calculus into the CSL (continuous spontaneous localization) model of collapse. Both these proposals rely on the experimental fact that the reduction of the wave-function occurs when the microscopic system encounters a macroscopic object and involves a big amount of its particles. Both of them also change the quantum formalism by introducing in the Schrodinger equation additional terms with noisy behavior. However, these terms have practically no influence as long as the studied system contains only one or a few components. Only when the amount of components is very big, these terms become significant and lead to the reduction of the wave-function to one of its components. The present work has two purposes: 1) proving that the collapse postulate is unavoidable; 2) applying the CSL model to the process in a detector and showing step by step the modification of the wave-function, until reduction. As a side detail, it is argued here that the noise cannot originate in some classical field, contrary to the thought/hope of some physicists, because no classical field is tailored by the wave-functions of entanglements.
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples

Recent work on adversarial learning has focused mainly on neural networks and domains where they excel, such as computer vision. The data in these domains is homogeneous, whereas heterogeneous tabular data domains remain underexplored despite their prevalence. Constructing an attack on models with heterogeneous input spaces is challenging, as they are governed by complex domain-specific validity rules and comprised of nominal, ordinal, and numerical features. We argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. In this paper, we introduce an optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Our approach focuses on an adversary who aims to craft valid perturbations of minimal l_0-norms and apply them in real life. We propose a neural network-based implementation of our approach and demonstrate its effectiveness using three datasets from different content domains. Our results suggest that despite the several constraints heterogeneity imposes on the input space of a machine learning model, the susceptibility to adversarial examples remains unimpaired.
WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization

We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.
GAMES CHARACTERIZING LIMSUP FUNCTIONS AND BAIRE CLASS 1 FUNCTIONS

Abstract We consider a real-valued function f defined on the set of infinite branches X of a countably branching pruned tree T. The function f is said to be a limsup function if there is a function $u \colon T \to \mathbb {R}$ such that $f(x) = \limsup _{t \to \infty } u(x_{0},\dots ,x_{t})$ for each $x \in X$ . We study a game characterization of limsup functions, as well as a novel game characterization of functions of Baire class 1.
Mutual inclinations between giant planets and their debris discs in HD 113337 and HD 38529

HD 113337 and HD 38529 host pairs of giant planets, a debris disc, and wide M-type stellar companions. We measure the disc orientation with resolved images from Herschel and constrain the three-dimensional orbits of the outer planets with Gaia DR2 and Hipparcos astrometry. Resolved disc modelling leaves degeneracy in the disc orientation, so we derive four separate planet-disc mutual inclination ($\Delta I$) solutions. The most aligned solutions give $\Delta I=17-32°$ for HD 113337 and $\Delta I=21-45°$ for HD 38529 (both 1$\sigma$). In both systems, there is a small probability (<0.3 per cent) that the planet and disc are nearly aligned ($\Delta I < 3°$). The stellar and planetary companions cause the orbits of disc material to precess about a plane defined by the forced inclination. We determine this as well as the precession time-scale to interpret the mutual inclination results. We find that the debris discs in both systems could be warped via joint influences of the outer planet and stellar companion, potentially explaining the observed misalignments. However, this requires HD 113337 to be old (0.8-1.7 Gyr), whereas if young (14-21 Myr), the observed misalignment in HD 113337 could be inherited from the protoplanetary disc phase. For both systems, the inclination of the stellar spin axis is consistent with the disc and outer planet inclinations, which instead supports system-wide alignment or near alignment. High-resolution observations of the discs and improved constraints on the planetary orbits would provide firmer conclusions about the (mis)alignment status.
Supervised Seeded Iterated Learning for Interactive Language Learning

Language drift has been one of the major obstacles to train language models through interaction. When word-based conversational agents are trained towards completing a task, they tend to invent their language rather than leveraging natural language. In recent literature, two general methods partially counter this phenomenon: Supervised Selfplay (S2P) and Seeded Iterated Learning (SIL). While S2P jointly trains interactive and supervised losses to counter the drift, SIL changes the training dynamics to prevent language drift from occurring. In this paper, we first highlight their respective weaknesses, i.e., late-stage training collapses and higher negative likelihood when evaluated on human corpus. Given these observations, we introduce Supervised Seeded Iterated Learning to combine both methods to minimize their respective weaknesses. We then show the effectiveness of \algo in the language-drift translation game.
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition

In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model. The model is composed of a stack of transformer layers for audio encoding with no lookahead or right context and an additional stack of transformer layers on top trained with variable right context. In inference time, the context length for the variable context layers can be changed to trade off the latency and the accuracy of the model. We also show that we can run this model in a Y-model architecture with the top layers running in parallel in low latency and high latency modes. This allows us to have streaming speech recognition results with limited latency and delayed speech recognition results with large improvements in accuracy (20% relative improvement for voice-search task). We show that with limited right context (1-2 seconds of audio) and small additional latency (50-100 milliseconds) at the end of decoding, we can achieve similar accuracy with models using unlimited audio right context. We also present optimizations for audio and label encoders to speed up the inference in streaming and non-streaming speech decoding.
Exploration of Hands-free Text Entry Techniques For Virtual Reality

Text entry is a common activity in virtual reality (VR) systems. There is a limited number of available hands-free techniques, which allow users to carry out text entry when users’ hands are busy such as holding items or hand-based devices are not available. The most used hands-free text entry technique is DwellType, where a user selects a letter by dwelling over it for a specific period. However, its performance is limited due to the fixed dwell time for each character selection. In this paper, we explore two other hands-free text entry mechanisms in VR: BlinkType and NeckType, which leverage users’ eye blinks and neck’s forward and backward movements to select letters. With a user study, we compare the performance of the two techniques with DwellType. Results show that users can achieve an average text entry rate of 13.47, 11.18 and 11.65 words per minute with BlinkType, NeckType, and DwellType, respectively. Users’ subjective feedback shows BlinkType as the preferred technique for text entry in VR. Index Terms: Human-centered computing—Human computer interaction (HCI)—Interaction paradigms—Virtual reality; Human-centered computing—Human computer interaction (HCI)—Interaction techniques—Text input
Semiparametric bounds for subdiffraction incoherent optical imaging: a parametric-submodel approach

I derive classical and quantum lower error bounds for moment estimation in subdiffraction incoherent optical imaging. While the end results are similar to earlier ones [Tsang, Phys. Rev. Res. 1, 033006 (2019); Phys. Rev. A 99, 012305 (2019)], the present work avoids the flawed mathematical assumptions in the earlier works by adopting a parametric-submodel approach from semiparametric statistics. With the rigorous bounds at hand and redefining the parameter of interest, I show that an alternative measurement called spatial-mode demultiplexing (SPADE) is superior to ideal direct imaging for a subdiffraction object, even if SPADE can measure only a finite number of modes.
Helicobacter pylori Infection - The Link Between Oxidative Damage, Cell Proliferation, Apoptosis, and Gastric Cancer 82

The demonstration of the link between Helicobacter pylori (H. pylori) infection and gastritis, peptic ulcer, and gastric cancer is recognized as one of medicine’s most important breakthroughs. The complex network of bacterial, environmental, and host factors that modulate the natural history of the infection has been extensively investigated. Nevertheless, several key questions remain unanswered, such as who among the many infected patients will develop gastric cancer, why does the infection eventually lead to a benign or a malignant disease, and whether, apart from eradication, there are yet other methods of modulating risk. This chapter, apart from investigating in depth all the possible factors and mechanisms
Comparison of Cooperative Driving Strategies for CAVs at Signal-Free Intersections

The properties of cooperative driving strategies for planning and controlling Connected and Automated Vehicles (CAVs) at intersections range from some that achieve highly efficient coordination performance to others whose implementation is computationally fast. This paper comprehensively compares the performance of four representative strategies in terms of travel time, energy consumption, computation time, and fairness under different conditions, including the geometric configuration of intersections, asymmetry in traffic arrival rates, and the relative magnitude of these rates. Our simulation-based study has led to the following conclusions: 1) The Monte Carlo Tree Search (MCTS)-based strategy achieves the best traffic efficiency and has great performance in fuel consumption; 2) MCTS and Dynamic Resequencing (DR) strategies both perform well in all metrics of interest. If the computation budget is adequate, the MCTS strategy is recommended; otherwise, the DR strategy is preferable; 3) An asymmetric intersection has a noticeable impact on the strategies, whereas the influence of the arrival rates can be neglected. When the geometric shape is asymmetrical, the modified First-In-First-Out (FIFO) strategy significantly outperforms the FIFO strategy and works well when the traffic demand is moderate, but their performances are similar in other situations; and 4) Improving traffic efficiency sometimes comes at the cost of fairness, but the DR and MCTS strategies can be adjusted to realize a better trade-off between various performance metrics by appropriately designing their objective functions.
A Democratic Emergency After a Health Emergency? Exposure to COVID‐19, Perceived Economic Threat and Support for Anti‐Democratic Political Systems

Objectives The urgency of the COVID‐19 pandemic has led governments to impose restrictions on individual freedom and required citizens to comply with these restrictions. In addition, lockdowns related to COVID‐19 have led to a significant economic crisis. We aimed to study how the pandemic and related economic threats have impacted support for anti‐democratic political systems. Method We analyzed data from a quota panel of the Italian adult population (N = 1,195), surveyed once before and once during the pandemic. Results A hierarchical regression model showed that exposure to COVID‐19 and perceived economic insecurity were associated with support for anti‐democratic political systems, independent of participants’ predispositions toward a strong leader. Conclusion An authoritarian personality is not a necessary precondition for individual anti‐democracy: when facing severe personal threats, anyone could restore a subjective sense of control over the social world by becoming anti‐democratic, independent of their initial predisposition to support anti‐democratic political systems.
Interfacial instabilities in two-dimensional Stokes flow: a weakly nonlinear analysis

Two-dimensional Stokes flow with injection and suction is investigated through a second-order, perturbative mode-coupling approach. We examine the time-dependent disturbance of an initially circular interface separating two viscous fluids, and derive a system of nonlinear differential equations describing the evolution of the interfacial perturbation amplitudes. Linear stability analysis reveals that an injection-induced expanding interface is stable, while a contracting motion driven by suction is unstable. Curiously, at the linear level this suction instability is independent of the viscosity contrast between the fluids. However, second-order results tell a different story, and show that the viscosity contrast plays a key role in determining the shape of the emerging interfacial patterns. By focusing on the onset of nonlinear effects, we have been able to extract valuable information about the most fundamental features of the pattern-forming structures for arbitrary values of viscosity contrast and surface tension.
The effects of inbreeding, crossbreeding and stress on metabolic rate in Drosophila littoralis

Fitness of an individual is commonly measured through the number and quality of offspring. Fitness is, however, the final outcome of all physiological and developmental processes and thus connected also to other features than reproduction, for example to metabolic rate. There are two opposite views about the connection between metabolic rate and quality: according to resource allocation theory low metabolic rate is a sign of high individual quality when the opposite view states low metabolic rate is connected to low ability to obtain resources which further leads to poor quality and fitness. Genetic differences between individuals lead also to differences in fitness. Inbreeding increases these genetic differences by increasing the amount of homozygosity. This leads to decreased fitness when possible deleterious effects of recessive alleles become visible in a homozygous state in fitness-related traits. Because of this effect, commonly known as inbreeding depression, inbred individuals have lower fitness compared to that of outbred individuals. Crossbreeding may cancel out the effects of inbreeding as it results to increased heterozygosity and thus possibly to higher fitness compared to the inbred parents. Added to the internal stress caused by inbreeding, also external factors such as drought and various chemicals can cause stress to the organism. Internal and external stressors may also have interactions, so that inbred individuals suffer more from external stress than outbred individuals. My goal in this study was to specify the connection between fitness and metabolic rate by manipulating the quality of the Drosophila littoralis flies through inbreeding, crossbreeding and salt stress. There were less offspring in inbred families and the body mass of the measured females was lower than in crossbred or base population flies. Thus inbreeding had lowered the fitness of the flies when measured with commonly known fitness traits. Inbred females had lower metabolic rate than that of crossbred females. Based on this, the low metabolic rate of inbred flies observed supports the theory of low total amount of resources indicating poor fitness. Like expected, crossbreeding returned the lowered fitness back to the level of the base population with all fitness measures. Salt stress did not have an effect on metabolic rate alone nor were there interactions between breeding treatment and salt stress in any measured character.
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning

Humans show an innate ability to learn the regularities of the world through interaction. By performing experiments in our environment, we are able to discern the causal factors of variation and infer how they affect the dynamics of our world. Analogously, here we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner. We introduce a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions, and to discover causal factors in the dynamics. The learned behavior allows the agent to infer a binary quantized representation for the ground-truth causal factors in every environment. Additionally, we find that these experimental behaviors are semantically meaningful (e.g., to differentiate between heavy and light blocks, our agents learn to lift them), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners. We show that these behaviors can be re-purposed and fine-tuned (e.g., from lifting to pushing or other downstream tasks). Finally, we show that the knowledge of causal factor representations aids zero-shot learning for more complex tasks.
Place Recognition in Forests With Urquhart Tessellations

In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest. We propose a framework that uses these descriptors to detect previously seen observations and landmark correspondences, even with partial overlap and noise. We run loop closure detection experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness.
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information

We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs? We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model. Our key idea in mRASP is its novel technique of random aligned substitution, which brings words and phrases with similar meanings across multiple languages closer in the representation space. We pre-train a mRASP model on 32 language pairs jointly with only public datasets. The model is then fine-tuned on downstream language pairs to obtain specialized MT models. We carry out extensive experiments on 42 translation directions across a diverse settings, including low, medium, rich resource, and as well as transferring to exotic language pairs. Experimental results demonstrate that mRASP achieves significant performance improvement compared to directly training on those target pairs. It is the first time to verify that multiple low-resource language pairs can be utilized to improve rich resource MT. Surprisingly, mRASP is even able to improve the translation quality on exotic languages that never occur in the pre-training corpus. Code, data, and pre-trained models are available at this https URL.
Reinforcement Learning with Random Delays

Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this principle to derive Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor-Critic with significantly better performance in environments with delays. This is shown theoretically and also demonstrated practically on a delay-augmented version of the MuJoCo continuous control benchmark.
A link among schizophrenia, diabetes, and asthma: Role of Ca2+/cAMP signaling

Asthma has been associated with an increased risk for developing schizophrenia. In addition, schizophrenia has been associated with an increased risk for developing type 2 diabetes mellitus, resulting in an elevated cardiovascular risk and in a limited life expectancy. It is well discussed that dysregulations related to Ca2+ signaling could link these diseases, in addition to cAMP signaling pathways. Thus, revealing this interplay among schizophrenia, diabetes, and asthma may provide novel insights into the pathogenesis of these diseases. Publications involving Ca2+ and cAMP signaling pathways, schizophrenia, diabetes, and asthma (alone or combined) were collected by searching PubMed and EMBASE. Both Ca2+ and cAMP signaling pathways (Ca2+/cAMP signaling) control the release of neurotransmitters and hormones, in addition to airway smooth muscle contractility, then dysregulations of these cellular processes may be involved in these diseases. Taking into consideration, the experience of our group in this field, this narrative review debated the involvement of Ca2+/cAMP signaling in this link among schizophrenia, diabetes, and asthma, including its pharmacological implications.