Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditory cortex anatomy reflects multilingual phonological experience

This study explores the relationship between the anatomy of the auditory cortex and multilingual experience, shedding light on the complex mechanisms of auditory processing in humans. Integrating previous research on auditory information processing and the impact of bi- and multilingualism on brain structure, we investigate how the morphology of auditory brain regions reflects individuals’ language experience and, more specifically, their phonological repertoire. Leveraging two distinct samples comprising over 200 participants, each exposed to between 1 and 7 languages encompassing 36 different languages, we explore whether the morphological variability of auditory brain regions reflects individual language experience, specifically focusing on the phonological repertoire. Additionally, we examine the potential influence of typological distances between languages spoken by multilingual individuals on the neural signatures of multilingualism within the auditory cortex. Our findings revealed a relationship between the average thickness of the left and right second transverse temporal gyrus (TTG) and participants’ language experience. Model comparisons demonstrated that the inclusion of phoneme-level information in the language experience index accounted for the greatest variance in average TTG thickness (bilaterally). The direction of this effect was negative, indicating that a more extensive and more phonologically diverse language experience was associated with thinner cortices in the second TTG. Across two independent datasets, we provide robust evidence linking the degree of language experience and typological distance between languages to cortical thickness in early auditory brain regions. We propose that this phenomenon may reflect experience-driven pruning and neural efficiency, warranting further investigation through longitudinal studies of language acquisition. Moreover, our findings support the notion that experiences with typologically similar languages differ from those with typologically distant languages, offering valuable insights into the intricate relationship between language diversity and brain structure. Our findings also indicate that early auditory regions seem to represent phoneme-level cross-linguistic information, contrary to the most established models of language processing in the brain, which suggest that phonological processing happens in more lateral posterior STG and STS.
HMM-based Anomaly Interpretation for Intelligent Robots in Industry 4.0 (short paper)

We apply an anomaly detection method based on Hidden Markov Models and Hellinger distance to a Kairos mobile robot operating in the ICE lab, a research laboratory for Industry 4.0. Two main contributions are proposed: i) a decomposition of the Hellinger distance which allows to identify the causes of anomalous behaviours detected, ii) a graphical user interface that synchronously shows the robot movements in a map and the evolution of the Hellinger distance components, allowing a quick investigation of the causes of the detected anomalies. The tools are applied to a real-world dataset allowing to discover that an anomalous movement of the Kairos robot is caused by a wrong reading of the lidar from a window in the environment.
From Tamagotchis to Pet Rocks: On Learning to Love Simplicity through the Endings Principles

This article, by two of the technical leads on Project Endings, represents the culmination of all we have learned over the last few years, during which we have rescued over a dozen projects from death by software obsolescence and reconstituted them as entirely static, standalone websites with minimal dependencies. We now know a great deal, mostly from our own mistakes, about how not to build robust, long-lasting digital resources, and we have developed a set of principles, practices, and software tools which we believe provide solid defences against digital extinction. Below, we describe the institutional context within which Project Endings was born, and lay out methodically the guiding principles by which we now develop digital editions and other web resources.
Developments in calibration of EUV and VUV detectors for solar orbiter instrumentation using synchrotron radiation

Space-based missions exploring the spectral ranges of extreme- and vacuum-ultraviolet radiation (EUV, VUV) require on-ground, at-wavelength calibration of their detectors and imaging systems. With the use of monochromatized synchrotron radiation, traceable calibrations regarding the spectral responsivity of the instruments can be provided. A dedicated vacuum chamber is used to house space instruments up to 100 kg weight for calibration measurements. Currently, the development of calibration procedures for the EUI instrument of the Solar Orbiter is still underway.
Towards an Holistic Human Perception System for Close Human-Robot Collaboration (short paper)

When considering close human-robot collaboration, perception plays a central role in order to guarantee a safe and intuitive interaction. In this work, we present an AI-based perception system composed of different modules to understand human activities at multiple levels, namely: human pose estimation, body parts segmentation and human action recognition. Pose estimation and body parts segmentation allow to estimate important information about the worker position within the workcell and the volume occupied, while human action and intention recognition provides information on what the human is doing and how he/she is performing a certain action. The proposed system is demonstrated in a mockup scenario targeting the collaborative assembly of a wooden leg table, highlighting the potential of action recognition and body parts segmentation to enable a safe and natural close human-robot collaboration.
Aiding global function pointer hijacking for post-CET binary exploitation

Most memory corruption exploits are aimed at disrupting the control-flow of a program. State-of-the-art Control-Flow Integrity (CFI) [1] preservation proposals, such as Intel’s ControlFlow Enforcement Technology (CET) [2] make it significantly harder for an attacker to gain arbitrary code execution, drastically shrinking the existing attack surface. With such defenses in place, an attacker cannot directly tamper with saved function return addresses: they must target other control variables. Function pointers constitute a possible exploitation target, as overwriting one allows to redirect the control-flow of a program without triggering CFI defenses. We focus on global function pointers defined in C libraries whose calls are reachable from exported functions, which implies they can possibly also be reached from the code of programs using the library. A memory corruption vulnerability that provides an arbitrary write primitive could be leveraged in such programs to gain arbitrary code execution through the overwrite of the right global library function pointer. This kind of attack is complicated by the need of identifying global function pointers and the constraints to be satisfied in order to trigger a call to them from library code, such as values of function parameters and other global variables, which involves complex and time-consuming manual inspection of library source code. We present an approach based on static analysis and symbolic execution to automate this process. We developed Untangle1, an open-source tool implementing this approach with the goal of aiding global function pointer hijacking in C libraries, using CodeQL, the angr symbolic execution engine [3, 4], and an ad-hoc symbolic memory model specialized in dealing with complex structure pointer parameters. Analyzing several open source C libraries, we identify 57 different global function pointers whose calls are reachable from a total of 1488 unique exported library functions, verifying the validity of the found solution for 484 of them.
Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems

Cooperation and knowledge sharing are of paramount importance in the evolution of an intelligent species. Knowledge sharing requires a set of symbols with a shared interpretation, enabling effective communication supporting cooperation. The engineering of intelligent systems may then benefit from the distribution of knowledge among multiple components capable of cooperation and symbolic knowledge sharing. Accordingly, in this paper, we propose a roadmap for the exploitation of knowledge representation and sharing to foster higher degrees of artificial intelligence. We do so by envisioning intelligent systems as composed by multiple agents, capable of cooperative (transfer) learning —Co(T)L for short. In CoL, agents can improve their local (sub-symbolic) knowledge by exchanging (symbolic) information among each others. In CoTL, agents can also learn new tasks autonomously by sharing information about similar tasks. Along this line, we motivate the introduction of Co(T)L and discuss benefits and feasibility.
An Analysis of Opinions on AI Ethics Based on Network Motifs using the Case Method

The development of artificial intelligence (AI) technologies has led to significant impacts on society. Simultaneously, the ethical issues involved in AI have become increasingly serious, and AI ethics has attracted attention as a topic of active research and discussion. Education in ethics is widely understood to play a crucial role in solving these emerging issues. In this study, we propose a model developed to address some ethical issues of AI based on the case method using network motifs to analyze educational consequences. We evaluated the method in terms of a network analysis of textual data of opinions to understand the changes in participants' perspectives before and after their experience with the case method. The experimental results validated the method we proposed, and the results of the analysis show that the participants’ opinions on the ethics of AI changed to some extent after participating in the case.
I. Apples to apples $A^2$: photometric redshift predictions for next-generation surveys

This is the first of a series of papers where we compare the expected performance of two of the largest stage IV next-generation surveys in the optical and infrared (LSST and Euclid), with a particular focus on cluster surveys. In this first paper, we introduce the mock catalogues we have utilized in this work, an N-body simulation+semi-analytical cone with a posterior modification with PhotReal, a technique which modifies the original photometry to make it more realistic by using an empirical library of spectral templates. We have confirmed the reliability of the mock catalogue by comparing the obtained color-magnitude relation, the luminosity and mass function and the angular correlation function with those of real data. We also analyze the behavior of the expected photometric redshifts for each different survey, in terms of photometric redshift resolution, photometric redshift bias and fraction of outliers. In addition, we discuss the benefits of using the BPZ odds photometric redshift quality parameter to select the best quality data of the sample. We provide consistent comparisons between the different surveys identifying the redshift and magnitude space where the performance of each survey becomes optimum. In particular, we find that very deep near infrared surveys such as Euclid will provide very good performance (∆z/(1+z) ∼ 0.025−0.053) down to H∼24 AB mag and up to redshift ∼ 3 depending on the optical observations available from the ground whereas extremely deep optical surveys such as LSST will obtain an overall lower photometric redshift resolution (∆z/(1 + z) ∼ 0.045) down to i ∼ 27.5 AB mag, being substantially improved (∆z/(1 + z) ∼ 0.035) if we restrict the sample down to i∼24 AB mag. We highlight the fact that those numbers can be improved substantially by selecting a subsample of galaxies with the best quality photometric redshifts. We finally discuss the impact that these surveys will have for the community in terms of photometric redshift legacy once the data is available.
Simple Out of Order Core for GPGPUs

GPU architectures have become popular for executing general-purpose programs which rely on having a large number of threads that run concurrently to hide the latency among dependent instructions. This approach has an important cost/overhead in terms of low data locality due to the increased pressure on the memory hierarchy of the many threads being run concurrently and the extra cost of storing and managing the on-chip state of those many threads. This paper presents SOCGPU (Simple Out-of-order Core for GPU), a simple out-of-order execution mechanism that does not require register renaming nor scoreboards. It uses a small Instruction Buffer and a tiny Dependence matrix to keep track of dependencies among instructions and avoid data hazards. Evaluations for an Nvidia Tesla V100-like GPU show that SOCGPU provides a speed-up of up to 2.3 in some machine learning programs and 1.38 on average for a variety of benchmarks, while it reduces energy consumption by 6.5%, with only 2.4% area overhead.
Comparative Evaluation of Anomaly-Based Controller Area Network IDS

The vulnerability of in-vehicle networks, particularly those based on the Controller Area Network (CAN) protocol, has prompted the development of numerous techniques for intrusion detection on the CAN bus. However, these CAN IDS are often evaluated in disparate experimental settings, with different datasets and evaluation metrics, which hinder direct comparison. This has given rise to efforts at benchmarking and comparative evaluation of CAN IDS under similar experimental conditions to provide an understanding of the relative performance of these CAN IDS. This work contributes to these efforts by reporting results of the comparative evaluation of four statistical and two machine learning-based CAN intrusion detection algorithm, against the Real ORNL Automotive Dynamometer (ROAD) CAN intrusion dataset. The ROAD dataset differs from datasets used in previous work in that it includes the stealthiest possible version of targeted ID fabrication attacks which are more difficult to detect. It also consists of masquerade attacks, which have not been commonly used in comparative evaluation studies. Furthermore, in addition to metrics such as accuracy, precision, recall, and F1-score, we report balanced accuracy, informedness, markedness, and Matthews correlation coefficient, which place equal important on positive and negative classes and are better measures of detection capability, especially for imbalanced datasets. We also report training and testing times for each CAN IDS as an indicator of real-time intrusion detection performance. Results of experiments were found to be generally concordant with previous work, in terms of accuracy, precision, recall, and F1-score. Entropy- and frequency-based CAN IDS were found to be relatively better at detecting attacks, particularly fabrication attacks; while other algorithms did not perform well, as indicated by low MCC scores.
BiRFIA: Selective Binary Rewriting for Function Interception on ARM

Function interception of fully-optimized binaries is widely used for optimization with its ability to accurately collect runtime information and detect inefficiencies at the function level. However, the implementation of function interception with existing binary rewriting techniques still suffers from limited reliability and performance on ARM platform. In this paper, we propose BiRFIA, an efficient selective binary rewriting framework for function interception targeting highly optimized binaries on ARM platforms. BiRFIA performs static binary rewriting of specific functions and intercepts them through well-formed trampoline sections and external instrumentation libraries. Besides, BiRFIA places complex instrumentation code in the trampoline section and jumps to the trampoline section via an adaptive instruction eviction strategy, which significantly reduces the probability of unexpected errors. For evaluation, we develop two function interception tools based on BiRFIA, including a function performance event counter collector and a function parameter tracer. Guided by these tools, we optimize several benchmarks and real-world programs, yielding up to 8% performance speedup. Our evaluation result demonstrates that BiRFIA incurs negligible runtime overhead of 1.006× on average.
Invited Paper: Lessons from HotStuff

This article will take you on a journey to the core of blockchains, their Byzantine consensus engine, where HotStuff emerged as a new algorithmic foundation for the classical Byzantine generals consensus problem. The first part of the article underscores the theoretical advances HotStuff enabled, including several models in which HotStuff-based solutions closed problems which were opened for decades. The second part focuses on HotStuff performance in real life setting, where its simplicity drove adoption of HotStuff as the golden standard for blockchain design, and many variants and improvements built on top of it. Both parts of this document are meant to describe lessons drawn from HotStuff as well as dispel certain myths.
Characterization of tumor-associated reactive astrocytes in gliomas by single-cell and bulk tumor sequencing

Objective Astrocytes constitute approximately 30% of cells in gliomas and play important roles in synapse construction and survival. Recently, JAK/STAT pathway activation associated with a new type of astrocyte was reported. However, the implications of these tumor-associated reactive astrocytes (TARAs) in glioma are not known. Methods We comprehensively assessed TARAs in gliomas, both in single cells and at the bulk tumor level, by analyzing five independent datasets. First, we analyzed two single-cell RNA sequencing datasets of 35,563 cells from 23 patients to estimate the infiltration level of TARAs in gliomas. Second, we collected clinical information and genomic and transcriptomic data of 1,379 diffuse astrocytoma and glioblastoma samples from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas datasets to evaluate the genomic, transcriptomic and clinical characteristics of TARA infiltration. Third, we downloaded expression profiles of recurrent glioblastoma samples from patients receiving PD-1 inhibitors to analyze the predictive value of TARAs for immune checkpoint inhibition. Results Single-cell RNA sequencing data showed TARAs were abundant in the glioma micro-environment (15.7% in the CGGA dataset and 9.1% in the Gene Expression Omnibus GSE141383 dataset, respectively). Bulk tumor sequencing data showed that the extent of TARA infiltration was highly associated with major clinical and molecular features of astrocytic gliomas. Patients with more TARA infiltration were more likely to have MUC16, FLG, and PICK3A mutations, chromosome 9p21.3, 10q23.3, and 13q14.2 deletions and 7p11.2 amplification. Gene Ontology analysis revealed that the high level of astrocyte infiltration was characterized by immune and oncogenic pathways, such as the inflammatory response, positive regulation of the JAK–STAT cascade, positive regulation of NIK/NF-kappa B signaling and the tumor necrosis factor biosynthetic process. Patients with greater TARA infiltration showed inferior prognosis. Meanwhile, the extent of reactive astrocyte infiltration exhibited a predictive value for recurrent glioblastoma patients undergoing anti-PD-1 immune therapy. Conclusion TARA infiltration might promote glioma tumor progression and can be used as a diagnostic, predictive and prognostic marker in gliomas. Prevention of TARA infiltration might be a new therapeutic strategy for glioma.
Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis

Background On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot. Methods In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis. Results Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30–59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population. Conclusion Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.