Miracle-dz/UniFAR-FTUs huggingface dataset:
Standing genetic variation as a potential mechanism of novel cave phenotype evolution in the freshwater isopod, Asellus aquaticus

Novel phenotypes can come about through a variety of mechanisms including standing genetic variation from a founding population. Cave animals are an excellent system in which to study the evolution of novel phenotypes such as loss of pigmentation and eyes. Asellus aquaticus is a freshwater isopod crustacean found in Europe and has both a surface and a cave ecomorph which vary in multiple phenotypic traits. An orange eye phenotype was previously revealed by F2 crosses and backcrosses to the cave parent within two examined Slovenian cave populations. Complete loss of pigmentation, both in eye and body, is epistatic to the orange eye phenotype and therefore the orange eye phenotype is hidden within the cave populations. Our goal was to investigate the origin of the orange eye alleles within the Slovenian cave populations by examining A. aquaticus individuals from Slovenian and Romanian surface populations and Asellus aquaticus infernus individuals from a Romanian cave population. We found orange eye individuals present in lab raised surface populations of A. aquaticus from both Slovenia and Romania. Using a mapping approach with crosses between individuals of two surface populations, we found that the region known to be responsible for the orange eye phenotype within the two previously examined Slovenian cave populations was also responsible within both the Slovenian and the Romanian surface populations. Complementation crosses between orange eye Slovenian and orange eye Romanian surface individuals suggest that the same gene is responsible for the orange eye phenotype in both surface populations. Additionally, we observed a low frequency phenotype of eye loss in crosses generated between the two surface populations and also in the Romanian surface population. Finally, in a cave population from Romania, A. aquaticus infernus, we found that the same region is also responsible for the orange eye phenotype as the Slovenian cave populations and the Slovenian and Romanian surface populations. Therefore, we present evidence that variation present in the cave populations could originate from standing variation present in the surface populations and/or transgressive hybridization of different surface phylogenetic lineages rather than de novo mutations.
Cortical microtubules contribute to division plane positioning during telophase in maize

Abstract Cell divisions are accurately positioned to generate cells of the correct size and shape. In plant cells, the new cell wall is built in the middle of the cell by vesicles trafficked along an antiparallel microtubule and a microfilament array called the phragmoplast. The phragmoplast expands toward a specific location at the cell cortex called the division site, but how it accurately reaches the division site is unclear. We observed microtubule arrays that accumulate at the cell cortex during the telophase transition in maize (Zea mays) leaf epidermal cells. Before the phragmoplast reaches the cell cortex, these cortical-telophase microtubules transiently interact with the division site. Increased microtubule plus end capture and pausing occur when microtubules contact the division site-localized protein TANGLED1 or other closely associated proteins. Microtubule capture and pausing align the cortical microtubules perpendicular to the division site during telophase. Once the phragmoplast reaches the cell cortex, cortical-telophase microtubules are incorporated into the phragmoplast primarily by parallel bundling. The addition of microtubules into the phragmoplast promotes fine-tuning of the positioning at the division site. Our hypothesis is that division site-localized proteins such as TANGLED1 organize cortical microtubules during telophase to mediate phragmoplast positioning at the final division plane.
Entropy and Entanglement of the Nonlinear Jaynes-Cummings Model

We have studied the influences of the Stark shifts and a Kerr-like medium on the degree of entanglement and the spectrum, for a system in which a two-level atom interaction occurs with a single mode of an electromagnetic field two-photon processes. The analytical expression for the spectrum is obtained from the finite double-Fourier transform of the two-time field correlation function. It is shown that the Stark shift effect changes the quasiperiod of the field entropy and, hence, the entanglement between the particle and the field.
Marked intrafamilial variability of clinical and neuroimaging manifestations in NFIB‐related developmental disorder

NFIB belongs to the nuclear factor I (NFI) family of transcription factors that, by activating or repressing gene expression during embryogenesis, has a relevant role in the development of several organs including the brain. Heterozygous pathogenic variants of NFIB have recently been associated with developmental delay and mild‐to‐moderate intellectual disability, macrocephaly, nonspecific facial dysmorphisms, and corpus callosum dysgenesis. We identified a heterozygous missense variant in the NFIB gene in a 15‐year‐old boy with neurodevelopmental disorder and brain malformations, who inherited the variant from his substantially healthy mother presenting only minor physical and neuroanatomical defects.
Computers as Bad Social Actors: Dark Patterns and Anti-Patterns in Interfaces that Act Socially

Technologies increasingly mimic human-like social behaviours. Beyond prototypical conversational agents like chatbots, this also applies to basic automated systems like app notifications or self-checkout machines that address or 'talk to' users in everyday situations. Whilst early evidence suggests social cues may enhance user experience, we lack a good understanding of when, and why, their use may be inappropriate. Building on a survey of English-speaking smartphone users (n=80), we conducted experience sampling, interview, and workshop studies (n=11) to elicit people's attitudes and preferences regarding how automated systems talk to them. We thematically analysed examples of phrasings/conduct participants disliked, the reasons they gave, and what they would prefer instead. One category of inappropriate behaviour we identified regards the use of social cues as tools for manipulation. We describe four unwanted tactics interfaces use: agents playing on users' emotions (e.g., guilt-tripping or coaxing them), being pushy, `mothering' users, or being passive-aggressive. Another category regards pragmatics: personal or situational factors that can make a seemingly friendly or helpful utterance come across as rude, tactless, or invasive. These include failing to account for relevant contextual particulars (e.g., embarrassing users in public); expressing obviously false personalised care; or treating a user in ways that they find inappropriate for the system's role or the nature of their relationship. We discuss these behaviours in terms of an emerging 'social' class of dark and anti-patterns. Drawing from participant recommendations, we offer suggestions for improving how interfaces treat people in interactions, including broader normative reflections on treating users respectfully.
Hubbard–Stratonovich Detector for Simple Trainable MIMO Signal Detection

Recently, various multiple-input multiple-output (MIMO) signal detectors based on deep learning have been proposed. Especially, deep unfolding (DU), which involves unrolling of an existing iterative algorithm and embedding of trainable parameters, has been applied with remarkable detection performance. In this letter, we attempted to construct a simple DU-based trainable MIMO detector with the simplest structure without using matrix inversion. The proposed detector based on the Hubbard–Stratonovich (HS) transformation and DU is called the trainable HS (THS) detector. It requires a constant number of trainable parameters and only quadratic training and execution costs per iteration. Numerical results show that the detection performance of the THS detector is better than that of existing algorithms of the same complexity and close to that of a DU-based detector, which has higher training and execution costs than the THS detector.
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks

In this paper, we explore Annotation Artifacts - the phenomena wherein large pre-trained NLP models achieve high performance on benchmark datasets but do not actually"solve"the underlying task and instead rely on some dataset artifacts (same across train, validation, and test sets) to figure out the right answer. We explore this phenomenon on the well-known Natural Language Inference task by first using contrast and adversarial examples to understand limitations to the model's performance and show one of the biases arising from annotation artifacts (the way training data was constructed by the annotators). We then propose a data augmentation technique to fix this bias and measure its effectiveness.
Partial Optimality in Cubic Correlation Clustering

The higher-order correlation clustering problem is an expressive model, and recently, local search heuristics have been proposed for several applications. Certifying optimality, however, is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optimality conditions for the special case of complete graphs and cubic objective functions. In addition, we define and implement algorithms for testing these conditions and examine their effect numerically, on two datasets.
Performative Recommendation: Diversifying Content via Strategic Incentives

The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking improve diversity by presenting more diverse items. Here we argue that to promote inherent and prolonged diversity, the system must encourage its creation. Towards this, we harness the performative nature of recommendation, and show how learning can incentivize strategic content creators to create diverse content. Our approach relies on a novel form of regularization that anticipates strategic changes to content, and penalizes for content homogeneity. We provide analytic and empirical results that demonstrate when and how diversity can be incentivized, and experimentally demonstrate the utility of our approach on synthetic and semi-synthetic data.
Better Diffusion Models Further Improve Adversarial Training

It has been recognized that the data generated by the denoising diffusion probabilistic model (DDPM) improves adversarial training. After two years of rapid development in diffusion models, a question naturally arises: can better diffusion models further improve adversarial training? This paper gives an affirmative answer by employing the most recent diffusion model which has higher efficiency ($\sim 20$ sampling steps) and image quality (lower FID score) compared with DDPM. Our adversarially trained models achieve state-of-the-art performance on RobustBench using only generated data (no external datasets). Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70.69\%$ and $42.67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i.e. improving upon previous state-of-the-art models by $+4.58\%$ and $+8.03\%$. Under the $\ell_2$-norm threat model with $\epsilon=128/255$, our models achieve $84.86\%$ on CIFAR-10 ($+4.44\%$). These results also beat previous works that use external data. We also provide compelling results on the SVHN and TinyImageNet datasets. Our code is available at https://github.com/wzekai99/DM-Improves-AT.
Weakly supervised human skin segmentation using guidance attention mechanisms

Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin colour, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.
Robot Synesthesia: A Sound and Emotion Guided AI Painter

If a picture paints a thousand words, sound may voice a million. While recent robotic painting and image synthesis methods have achieved progress in generating visuals from text inputs, the translation of sound into images is vastly unexplored. Generally, sound-based interfaces and sonic interactions have the potential to expand accessibility and control for the user and provide a means to convey complex emotions and the dynamic aspects of the real world. In this paper, we propose an approach for using sound and speech to guide a robotic painting process, known here as robot synesthesia. For general sound, we encode the simulated paintings and input sounds into the same latent space. For speech, we decouple speech into its transcribed text and the tone of the speech. Whereas we use the text to control the content, we estimate the emotions from the tone to guide the mood of the painting. Our approach has been fully integrated with FRIDA, a robotic painting framework, adding sound and speech to FRIDA's existing input modalities, such as text and style. In two surveys, participants were able to correctly guess the emotion or natural sound used to generate a given painting more than twice as likely as random chance. On our sound-guided image manipulation and music-guided paintings, we discuss the results qualitatively.
Benchmarks for Automated Commonsense Reasoning: A Survey

More than one hundred benchmarks have been developed to test the commonsense knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems. However, these benchmarks are often flawed, and many aspects of common sense remain untested. Consequently, there is currently no reliable way of measuring to what extent existing AI systems have achieved these abilities. This article surveys the development and uses of AI commonsense benchmarks. It enumerates 139 commonsense benchmarks that have been developed: 102 text-based, 18 image-based, 12 video-based, and 7 based in simulated physical environments. It gives more detailed descriptions of twelve of these, three from each category. It surveys the various methods used to construct commonsense benchmarks. It discusses the nature of common sense, the role of common sense in AI, the goals served by constructing commonsense benchmarks, desirable features of commonsense benchmarks, and flaws and gap in existing benchmarks. It concludes with a number of recommendations for future development of commonsense AI benchmarks; most importantly, that the creators of benchmarks invest the work needed to ensure that benchmark examples are consistently high quality.
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be useful for tracking the movement of the liver over time. This research explores this possibility by training the multi-scale attention UNet of Turbolift learning at its third stage on the TRVs and shows the robustness of Turbolift learning since it can even work efficiently with the TRVs, resulting in a Dice score of $0.864\pm 0.004$.
ESPRESSO: design and analysis of a Coudé-train for a stable and efficient simultaneous optical feeding from the four VLT unit telescopes

ESPRESSO is a fiber-fed, cross-dispersed, high-resolution, echelle spectrograph. Being the first purpose of ESPRESSO to develop a competitive and innovative high-resolution spectrograph to fully exploit the VLT (Very Large Telescope), and allow new science, it is important to develop the VLT array concept bearing in mind the need to obtain the highest stability, while preserving its best efficiency. This high-resolution ultra-stable spectrograph will be installed in the VLT at the Combined Coudé Laboratory (CCL), fed by four Coudé Trains, which brings the light from the Nasmyth platforms of the four VLT Unit Telescopes to the CCL. A previous trade-off analysis, considering the use of mirrors, prisms, lenses or fibers and several possible combinations of them, pointed towards a Full Optics solution, using only conventional optics to launch the light from the telescope into the front-end unit. In this case, the system is composed of a set of prisms and lenses to deliver a pupil and an image in the CCL, including an Atmospheric Dispersion Compensator. In this paper, we present the optical design of the Coudé Trains, the opto-mechanical concept, the main characteristics and expected performances.
NLP-based Decision Support System for Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank

As part of its digitization initiative, the German Central Bank (Deutsche Bundesbank) wants to examine the extent to which natural Language Processing (NLP) can be used to make independent decisions upon the eligibility criteria of securities prospectuses. Every month, the Directorate General Markets at the German Central Bank receives hundreds of scanned prospectuses in PDF format, which must be manually processed to decide upon their eligibility. We found that this tedious and time-consuming process can be (semi-)automated by employing modern NLP model architectures, which learn the linguistic feature representation in text to identify the present eligible and ineligible criteria. The proposed Decision Support System provides decisions of document-level eligibility criteria accompanied by human-understandable explanations of the decisions. The aim of this project is to model the described use case and to evaluate the extent to which current research results from the field of NLP can be applied to this problem. After creating a heterogeneous domain-specific dataset containing annotations of eligible and non-eligible mentions of relevant criteria, we were able to successfully build, train and deploy a semi-automatic decider model. This model is based on transformer-based language models and decision trees, which integrate the established rule-based parts of the decision processes. Results suggest that it is possible to efficiently model the problem and automate decision making to more than 90% for many of the considered eligibility criteria.
A 1.1-/0.9-nA Temperature-Independent 213-/565-ppm/°C Self-Biased CMOS-Only Current Reference in 65-nm Bulk and 22-nm FDSOI

In many applications, the ability of current references to cope with process, voltage, and temperature (PVT) variations is critical to maintaining system-level performance. However, temperature-independent current references operating in the nA range are rarely area-efficient due to the use of large resistors which occupy a significant silicon area at this current level. In this article, we introduce a nA-range constant-with-temperature (CWT) current reference relying on a self-cascode MOSFET (SCM), biased by a proportional-to-absolute-temperature (PTAT) voltage with a CWT offset. On the one hand, the proposed reference has been simulated post-layout in 65-nm bulk. This design consumes 5.4 nW at 0.7 V and achieves a 1.1-nA current with a line sensitivity (LS) of 0.69 %/V and a temperature coefficient (TC) of 213 ppm/°C. On the other hand, the proposed reference has been simulated and fabricated in 22-nm fully depleted silicon-on-insulator (FDSOI). This second design requires additional features to mitigate the impact of parasitic diode leakage at high temperature. In measurement, it consumes 5.8 nW at 0.9 V and achieves a 0.9-nA current with a 0.39-%/V LS and a 565-ppm/°C TC. As a result of using an SCM, the proposed references occupy a silicon area of 0.0021 mm<sup>2</sup> in 65 nm (respectively, 0.0132 mm<sup>2</sup> in 22 nm) at least 25<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (respectively, 4<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>) smaller than state-of-the-art CWT references operating in the same current range.
Gaze-based intention estimation: principles, methodologies, and applications in HRI

Intention prediction has become a relevant field of research in Human-Machine and Human-Robot Interaction. Indeed, any artificial system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would benefit from first inferring the human's current intention. To spare the user the cognitive burden of explicitly uttering their goals, this inference relies mostly on behavioral cues deemed indicative of the current action. It has been long known that eye movements are highly anticipatory of the single steps unfolding during a task, hence they can serve as a very early and reliable behavioural cue for intention recognition. This review aims to draw a line between insights in the psychological literature on visuomotor control and relevant applications of gaze-based intention recognition in technical domains, with a focus on teleoperated and assistive robotic systems. Starting from the cognitive principles underlying the relationship between intentions, eye movements, and action, the use of eye tracking and gaze-based models for intent recognition in Human-Robot Interaction is considered, with prevalent methodologies and their diverse applications. Finally, special consideration is given to relevant human factors issues and current limitations to be factored in when designing such systems.