Miracle-dz/UniFAR-FTUs huggingface dataset:
The Power of Noise: Redefining Retrieval for RAG Systems

Retrieval-Augmented Generation (RAG) systems represent a significant advancement over traditional Large Language Models (LLMs). RAG systems enhance their generation ability by incorporating external data retrieved through an Information Retrieval (IR) phase, overcoming the limitations of standard LLMs, which are restricted to their pre-trained knowledge and limited context window. Most research in this area has predominantly concentrated on the generative aspect of LLMs within RAG systems. Our study fills this gap by thoroughly and critically analyzing the influence of IR components on RAG systems. This paper analyzes which characteristics a retriever should possess for an effective RAG's prompt formulation, focusing on the type of documents that should be retrieved. We evaluate various elements, such as the relevance of the documents to the prompt, their position, and the number included in the context. Our findings reveal, among other insights, that including irrelevant documents can unexpectedly enhance performance by more than 30% in accuracy, contradicting our initial assumption of diminished quality. These results underscore the need for developing specialized strategies to integrate retrieval with language generation models, thereby laying the groundwork for future research in this field.
Understanding Domain Generalization: A Noise Robustness Perspective

Despite the rapid development of machine learning algorithms for domain generalization (DG), there is no clear empirical evidence that the existing DG algorithms outperform the classic empirical risk minimization (ERM) across standard benchmarks. To better understand this phenomenon, we investigate whether there are benefits of DG algorithms over ERM through the lens of label noise. Specifically, our finite-sample analysis reveals that label noise exacerbates the effect of spurious correlations for ERM, undermining generalization. Conversely, we illustrate that DG algorithms exhibit implicit label-noise robustness during finite-sample training even when spurious correlation is present. Such desirable property helps mitigate spurious correlations and improve generalization in synthetic experiments. However, additional comprehensive experiments on real-world benchmark datasets indicate that label-noise robustness does not necessarily translate to better performance compared to ERM. We conjecture that the failure mode of ERM arising from spurious correlations may be less pronounced in practice.
ISP pricing and Platform pricing interaction under net neutrality

We analyze the effects of enforcing vs. exempting access ISP from net neutrality regulations when platforms are present and operate two-sided pricing in their business models. This study is conducted in a scenario where users and Content Providers (CPs) have access to the internet by means of their serving ISPs and to a platform that intermediates and matches users and CPs, among other service offerings. Our hypothesis is that platform two-sided pricing interacts in a relevant manner with the access ISP, which may be allowed (an hypothetical non-neutrality scenario) or not (the current neutrality regulation status) to apply two-sided pricing on its service business model. We preliminarily conclude that the platforms are extracting surplus from the CPs under the current net neutrality regime for the ISP, and that the platforms would not be able to do so under the counter-factual situation where the ISPs could apply two-sided prices.
The Borel Ramsey properties for countable Borel equivalence relations

We define some natural notions of strong and weak Borel Ramsey properties for countable Borel equivalence relations and show that they hold for a countable Borel equivalence relation if and only if the equivalence relation is smooth. We also consider some variation of the notion for hyperfinite non-smooth Borel equivalence relations.
Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent Users

Advances in natural language processing and understanding have led to a rapid growth in the popularity of conversational user interfaces (CUIs). While CUIs introduce novel benefits, they also yield risks that may exploit people's trust. Although research looking at unethical design deployed through graphical user interfaces (GUIs) established a thorough understanding of so-called dark patterns, there is a need to continue this discourse within the CUI community to understand potentially problematic interactions. Addressing this gap, we interviewed 27 participants from three cohorts: researchers, practitioners, and frequent users of CUIs. Applying thematic analysis, we construct five themes reflecting each cohort's insights about ethical design challenges and introduce the CUI Expectation Cycle, bridging system capabilities and user expectations while considering each theme's ethical caveats. This research aims to inform future development of CUIs to consider ethical constraints while adopting a human-centred approach.
RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning

Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dexterous hands – especially on real robotic platforms – due to the high dimensionality of the problem. In this letter, we focus on the multi-fingered grasping task with the anthropomorphic hand of the iCub humanoid. We propose the RESidual learning with PREtrained CriTics (RESPRECT) method that, starting from a policy pre-trained on a large set of objects, can learn a residual policy to grasp a novel object in a fraction ($\sim 5 \times$ faster) of the timesteps required to train a policy from scratch, without requiring any task demonstration. To our knowledge, this is the first Residual Reinforcement Learning (RRL) approach that learns a residual policy on top of another policy pre-trained with DRL. We exploit some components of the pre-trained policy during residual learning that further speed-up the training. We benchmark our results in the iCub simulated environment, and we show that RESPRECT can be effectively used to learn a multi-fingered grasping policy on the real iCub robot.
Hierarchical binary black hole mergers in globular clusters: mass function and evolution with redshift

Hierarchical black hole (BH) mergers are one of the most straightforward mechanisms to produce BHs inside and above the pair-instability mass gap. Here, we investigate the impact of globular cluster (GC) evolution on hierarchical mergers, and we account for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio and spin distribution. We find that the evolution of the host GC quenches the hierarchical BH assembly already at the third generation, mainly due to cluster expansion powered by a central BH sub-system. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1>50 \, \mathrm{M_{\odot}}$, regardless of the assumed BH pairing function. At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger rate density. We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \mathrm{M_{\odot}}$ for $z\geq{}1$. Finally, we calculate the mixing fraction of BBHs from GCs and isolated binary systems. Our predictions are very sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs, in order to reproduce misaligned spin observations.
An explicit-implicit Generalized Finite Difference scheme for a parabolic-elliptic density-suppressed motility system

In this work, a Generalized Finite Difference (GFD) scheme is presented for effectively computing the numerical solution of a parabolic-elliptic system modelling a bacterial strain with density-suppressed motility. The GFD method is a meshless method known for its simplicity for solving non-linear boundary value problems over irregular geometries. The paper first introduces the basic elements of the GFD method, and then an explicit-implicit scheme is derived. The convergence of the method is proven under a bound for the time step, and an algorithm is provided for its computational implementation. Finally, some examples are considered comparing the results obtained with a regular mesh and an irregular cloud of points.
Emulating Complex Synapses Using Interlinked Proton Conductors

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.
Polyploid genome assembly of Cardamine chenopodiifolia

Background Cardamine chenopodiifolia is an amphicarpic plant that develops two fruit morphs, one above and the other below ground. Above-ground fruit disperse their seeds by explosive coiling of the fruit valves, while below-ground fruit are non-explosive. Amphicarpy is a rare trait that is associated with polyploidy in C. chenopodiifolia. Studies into the development and evolution of this trait are currently limited by the absence of genomic data for C. chenopodiifolia. Results We produced a chromosome-scale assembly of the octoploid C. chenopodiifolia genome using high-fidelity long read sequencing with the Pacific Biosciences platform. We successfully assembled 32 chromosomes and two organelle genomes with a total length of 597.2 Mbp and an N50 of 18.8 kbp (estimated genome size from flow cytometry: 626 Mbp). We assessed the quality of this assembly using genome-wide chromosome conformation capture (Omni-C) and BUSCO analysis (97.1% genome completeness). Additionally, we conducted synteny analysis to infer that C. chenopodiifolia likely originated via allo-rather than auto-polyploidy and phased one of the four sub-genomes. Conclusions This study provides a draft genome assembly for C. chenopodiifolia, which is a polyploid, amphicarpic species within the Brassicaceae family. This genome offers a valuable resource to investigate the under-studied trait of amphicarpy and the origin of new traits by allopolyploidy.
Genetic network analysis uncovers spatial variation in diversity and connectivity of a species presenting a continuous distribution

The conservation of genetic diversity and connectivity is essential for the long-term persistence and adaptive ability of a species. Recent calls have been made for the inclusion of genetic diversity and differentiation measures in the assessment, management, and conservation of species. However, the literature often lacks direction on how to do so for species with continuous distributions or no distinct breaks in genetic connectivity. There are many considerations to overcome when investigating genetic diversity and connectivity of such species. We combine multiple genetic network methodologies with more traditional population genetic analyses within a single framework to address the challenges of investigating population structure and quantifying variation in genetic diversity and connectivity of wide-ranging species with continuous distributions. We demonstrate the efficacy and applicability of our framework through a study on woodland caribou (Rangifer tarandus) occupying the boreal forest of Canada; a species of significant conservation concern. The dataset consisted of 4911 unique individuals genotyped at 9 microsatellite loci, which were subsequently partitioned into 103 spatial nodes to create a population-based genetic network. The Walktrap community detection algorithm was used to detect hierarchical population genetic structure within the study area and node-based network metrics such as mean inverse edge weight and clustering coefficient were used to quantify the variation in genetic connectivity across the range. Lastly, genetic diversity was assessed by calculating allelic richness and heterozygosity of the nodes making up the network. The community detection analysis identified two communities at the coarsest scale to nine communities at the optimal partition. A strong pattern of Isolation by Distance (IBD) was found across the range at multiple scales. Furthermore, signs of genetic erosion along the study area’s southern boundaries were depicted by nodes presenting low genetic diversity and low centrality values. These results are important to the species status assessments in providing previously unavailable information on connectivity and diversity within and beyond the current local population units used in management. Our approach to quantify the patterns and extent of connectivity across the boreal range is comprehensive and could easily be adapted to other species. The results are robust and provide a solid foundation for the continued monitoring and recovery of the species.
A specialized inhibitory function sharpens somatosensory hand representation and enhances the production and perception of fast multifinger movements in pianists

Accurate control of fast, coordinated movements across multiple body parts characterizes experts’ skills, such as playing musical instruments. While performing such skillful movements, the somatosensory system is challenged to successively and in parallel process a large amount of somatosensory information originating from different body parts within a short period. Over decades, it has been posited that the cortical representations of distinct body parts are more isolated from each other in trained than untrained individuals. Several recent studies, however, have re-examined and failed to replicate it. Here, we provide compelling evidence that expert pianists possess a unique inhibitory function that isolates the somatosensory processing of different body parts in the somatosensory cortex (S1). A behavioural experiment demonstrated a superior ability to perceive fast multifinger movements in pianists than musically untrained individuals, suggesting the specialized neural process of somatosensory information originating from multiple fingers within a short period in pianists. A series of neurophysiological experiments demonstrated that pianists have a unique inhibitory function in the S1, which was activated by weak electrical stimulation to the ulnar nerve. This stimulation also increased the representational distance between fingers, which was assessed based on cortical activation patterns elicited by the passive finger movements. This indicates the strengthened independence of the individual finger representation in the somatosensory processes specifically in pianists. This stimulation also augmented both the perception and execution of the fast and complex multifinger sequential movements. In nonmusicians, neither the inhibitory effects on the somatosensory process nor enhancement of the perception of multifinger movements was induced by this stimulation. Together, these findings provide the first evidence of the experience-dependent plasticity of inhibition of the somatosensory system, which highlights its pivotal role in the isolated somatosensory processing of multiple body parts in trained individuals and enables them to control fast and complex multifinger movements.
The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.
Constitutive sodium permeability in a C. elegans two-pore domain potassium channel

Two-pore domain potassium (K2P) channels play a central role in modulating cellular excitability and neuronal function. The unique structure of the selectivity filter in K2P and other potassium channels determines their ability to allow the selective passage of potassium ions across cell membranes. The nematode C. elegans has one of the largest K2P families, with 47 subunit-coding genes. This remarkable expansion has been accompanied by the evolution of atypical selectivity filter sequences that diverge from the canonical TxGYG motif. Whether and how this sequence variation may impact the function of K2P channels has not been investigated so far. Here we show that the UNC-58 K2P channel is constitutively permeable to sodium ions and that a cysteine residue in its selectivity filter is responsible for this atypical behavior. Indeed, by performing in vivo electrophysiological recordings and Ca2+ imaging experiments, we demonstrate that UNC-58 has a depolarizing effect in muscles and sensory neurons. Consistently, unc-58 gain-of-function mutants are hypercontracted, unlike the relaxed phenotype observed in hyperactive mutants of many neuromuscular K2P channels. Finally, by combining molecular dynamics simulations with functional studies in Xenopus laevis oocytes, we show that the atypical cysteine residue plays a key role in the unconventional sodium permeability of UNC-58. As predicting the consequences of selectivity filter sequence variations in silico remains a major challenge, our study illustrates how functional experiments are essential to determine the contribution of such unusual potassium channels to the electrical profile of excitable cells. SIGNIFICANCE Potassium channels play a central role in modulating cellular excitability, particularly of neuronal cells. Their unique structure determines their ability to let ions pass selectively through cell membranes. The impact of pathological or evolutionary variations in this selectivity filter remains difficult to predict. Here, we reveal that UNC-58, a member of the two-pore domain potassium channel family of C. elegans, exhibits an unusual sodium permeability due to a unique cysteine residue in its selectivity filter. Our findings underscore the importance of functional studies to determine how sequence variation in potassium channel selectivity filters can shape the electrical profiles of excitable cells.
The Increasing Authorship trend in Neuroscience: A scientometric analysis across 11 countries

Previous studies have demonstrated an increasing trend of the number of authors across various fields over the years. This trend has been attributed to the necessity for larger collaborations and, at times, to ethical issues regarding authorship attribution. Our study focuses on the evolution of authorship trends in the field of Neuroscience. We conducted our analysis based on a dataset containing 576,647 neuroscience publications produced from 2000 to 2022, focusing on the publications within the Group of ten (G10) countries. Using a matrix-based methodology, we extracted and analyzed the average number of authors per country. Our findings reveal a consistent rise in authorship across all G10 countries over the past two decades. Italy emerged with the highest average number of authors, while the Netherlands stood out for experiencing the most significant increase, particularly in the last decade. The countries with the lowest number of authors per publication were the USA, UK, and Canada.
Bayesian evidence for the neural dissociation between finger and hand imitation skills

Introduction For limb apraxia ‒ a heterogeneous disorder of higher motor cognition following stroke ‒ an enduring debate has arisen regarding the existence of dissociating neural correlates for finger and hand gestures in the left hemisphere. We re-assessed this question asking whether previous attempts analysing pooled samples of patients with deficits in only one and patients with deficits in both imitation types might have led to systematically biased results. Methods We conducted frequentist and Bayesian, voxelwise and regionwise lesion symptom mappings on a pooled sample (N=96) and subsamples containing only shared and only isolated hand and finger imitation deficits and respective controls. Results Anatomical analyses on the isolated sample reinforced a cortical dissociation of finger deficits (located more anteriorly) and hand deficits (located more posteriorly). The presence of patients with shared deficits did indeed dilute associations that appeared stronger in the respective isolated samples. Also, brain regions truly associated with hand imitation deficits showed a positive bias for finger imitation deficits, when the sample contained patients with shared deficits. In addition, our frequentist parameters uncovered that some of our Bayesian evidence supported reverse associations (damage protecting from rather than increasing the deficit). Discussion Joint analyses of patients with shared and isolated imitation deficits do indeed lead to biases, which may explain why some previous studies have failed to detect the actual neural dissociation between hand and finger imitation deficits.