Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Incompatibilities and Evolutionary Rescue by Wild Relatives Shaped Grain Amaranth Domestication

Crop domestication and the subsequent expansion of crops have long been thought of as a linear process from a wild ancestor to a domesticate. However, evidence of gene flow from locally adapted wild relatives that provided adaptive alleles into crops has been identified in multiple species. Yet, little is known about the evolutionary consequences of gene flow during domestication and the interaction of gene flow and genetic load in crop populations. We study the pseudo-cereal grain amaranth that has been domesticated three times in different geographic regions of the Americas. We quantify the amount and distribution of gene flow and genetic load along the genome of the three grain amaranth species and their two wild relatives. Our results show ample gene flow between crop species and between crops and their wild relatives. Gene flow from wild relatives decreased genetic load in the three crop species. This suggests that wild relatives could provide evolutionary rescue by replacing deleterious alleles in crops. We assess experimental hybrids between the three crop species and found genetic incompatibilities between one Central American grain amaranth and the other two crop species. These incompatibilities might have created recent reproductive barriers and maintained species integrity today. Together, our results show that gene flow played an important role in the domestication and expansion of grain amaranth, despite genetic species barriers. The domestication of plants was likely not linear and created a genomic mosaic by multiple contributors with varying fitness effects for today’s crops.
Simultaneous Spectral Differentiation of Multiple Fluorophores in Super-resolution Imaging Using a Glass Phase Plate

Multicolor localization microscopy typically relies on sequential imaging and bandpass filters to distinguish fluorescent tags, which introduces temporal delays during live imaging, and decreases photon yield. By engineering the point-spread function (PSF), different fluors can be imaged simultaneously and distinguished by their unique patterns, without discarding photons. Here, we insert a silicon-dioxide phase plate at the Fourier plane of the detection path of a wide-field fluorescence microscope to produce distinguishable PSFs (X-PSFs) at different wavelengths. We demonstrate that the resulting PSFs can be localized spatially and spectrally using a statistics-based computational algorithm and can be utilized for hyper-spectral super-resolution microscopy of biological samples. Single PSFs in fixed U2OS cells were acquired using dSTORM with simultaneous illumination of fluors without emission filters. The modified PSF achieves ∼21 nm lateral localization precision (FWHM), ∼17 nm axial precision (FWHM) with an average of 1,800 - 3,500 photons per PSF and a background as high as 130 - 400 photons per pixel. The modified PSF can distinguish up to three fluorescent probes with ∼80 nm peak-to-peak separation between consecutive spectra.
Pontine Waves Accompanied by Short Hippocampal Sharp Wave-Ripples During Non-rapid Eye Movement Sleep

Ponto-geniculo-occipital (PGO) or pontine (P) waves have long been recognized as an electrophysiological signature of rapid eye movement (REM) sleep. However, P-waves can be observed not just during REM sleep, but also during non-REM (NREM) sleep. Recent studies have uncovered that P-waves are functionally coupled with hippocampal sharp wave-ripples (SWRs) during NREM sleep. However, it remains unclear to what extent P-waves during NREM sleep share their characteristics with P-waves during REM sleep and how the functional coupling to P-waves modulates SWRs. Here, we address these issues by performing multiple types of electrophysiological recordings and fiber photometry in both sexes of mice. P-waves during NREM sleep share their waveform shapes and local neural ensemble dynamics at a short (∼100 ms) timescale with their REM sleep counterparts. However, the dynamics of mesopontine cholinergic neurons are distinct at a longer (∼10 s) timescale: although P-waves are accompanied by cholinergic transients, the cholinergic tone gradually reduces before P-wave genesis during NREM sleep. While P-waves are coupled to hippocampal theta rhythms during REM sleep, P-waves during NREM sleep are accompanied by a rapid reduction in hippocampal ripple power. SWRs coupled with P-waves are short-lived and hippocampal neural firing is also reduced after P-waves. These results demonstrate that P-waves are part of coordinated sleep-related activity by functionally coupling with hippocampal ensembles in a state-dependent manner.
Sur la restriction des faisceaux semi-stables

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Anti-windup disturbance suppression control of feedback linearizable systems with application to hypersonic flight vehicles

This paper deals with the anti-windup control problem for a class of feedback linearizable systems subject to external disturbances and constrained actuators. The original system is approximately feedback linearized into a tractable form with additive mismatched disturbances. A baseline disturbance suppression control is built on the combination of a disturbance observer and a compensation law. Moreover, this baseline control is strengthened by two anti-windup mechanisms to accommodate the possible input saturation, leading to the anti-windup disturbance suppression control. The resulting control schemes guarantee output tracking errors to be bounded in the presence of input saturation, while the zero steady-state tracking is finally achieved due to the recovery property. The proposed methods are applied to the longitudinal tracking control of hypersonic flight vehicles to highlight their efficiency and superiority.
Interactive simulation and free-field auralization of acoustic space with the rtSOFE

The Simulated Open Field Environment (SOFE), a loudspeaker setup in an anechoic chamber to render sound sources along with their simulated, spatialized reflections, has been used for more than two decades in free-field hearing research. In 2004, the concept was revised to incorporate room-acoustic simulation software that computes sound reflections in arbitrarily-shaped rooms and auralizes them via many loudspeakers—the principle of various systems used today (Hafter and Seeber, ICA 2004). For a complete redesign of the system, an anechoic chamber has been purpose-built at TUM and I will talk about its specifications. The anechoic chamber hosts the real-time SOFE (rtSOFE), a setup with 61 loudspeakers to create a spatial sound field in a 5 m x 5 m area along with 360° of visual 3D projection. New room-acoustic simulation software for interactive computation of reflections computes room impulse responses in sub-millisecond intervals and updates a convolution system capable of convolving seconds-long impuls.
Fabrication of silicon-on-diamond substrate with an ultrathin SiO2 bonding layer

We proposed and demonstrated a sputter etching method to prepare both a flat surface (root-mean-square surface roughness of approximately 0.2–0.3 nm) and an ultrathin SiO2 bonding layer at an accuracy of approximately 5 nm in thickness to fabricate a silicon-on-diamond substrate (SOD). We also investigated a plasma activation method on a SiO2 surface using various gases. We found that O2 plasma activation is more suitable for the bonding between SiO2 and Si than N2 or Ar plasma activation. We speculate that the concentration of hydroxyl groups on the SiO2 surface was increased by O2 plasma activation. We fabricated the SOD substrate with an ultrathin (15 nm in thickness) SiO2 bonding layer using the sputter etching and O2 plasma activation methods.
Solving Raven's Progressive Matrices via a Neuro-vector-symbolic Architecture

Figure 1: A neuro-vector-symbolic architecture (NVSA): combining neural nets and VSA by leveraging the common language between them namely, the holographic vector representations shown as a 3D hologram projection. Human fluid intelligence is the ability to think and reason abstractly, and make inferences in a novel domain. The Raven’s progressive matrices (RPM) test has been a widely-used assessment of fluid intelligence and visual abstract reasoning. Neuro-symbolic AI approaches display both perception and reasoning capabilities, but inherit the limitations of their individual deep learning and symbolic AI components, namely the the so-called neural binding problem and exhaustive symbolic searches explained in the following. The binding problem in neural networks refers to their inability to recover distinct objects from their joint representation. This inability prevents the neural networks from providing an adequate description of real-world objects or situations that can be represented by hierarchical and nested compositional structures. As an alternative to local representations, distributed representations can provide enough capacity to represent a combinatorially growing number of compositional items; however, they face another issue known as the “superposition catastrophe”. The second problem is exhaustive searches in symbolic reasoning. When solving the RPM tests, a probabilistic abduction reasoning approach searches for a solution in a space defined by prior background knowledge which is represented in symbolic form by describing all possible rule realizations that could govern the RPM tests. In effect, the computational complexity of the exhaustive search rapidly increases with the number of objects in the RPM panels, which hinders its utilization for large-scale problems, end-to-end training, and real-time inference. To address these problems, one viable solution is to exploit vector-symbolic architectures (VSAs), which are computational models where all representations—from atomic to composite structures—are high-dimensional distributed vectors of the same, fixed dimensionality. The
A Brief Discussion about the Credal Semantics for Probabilistic Answer Set Programs

Among the different logic-based programming languages, Answer Set Programming has emerged as an effective paradigm to solve complex combinatorial tasks. Since most of the real-world data are uncertain, several semantics have been proposed to extend Answer Set Programming to manage uncertainty, where rules are associated with a weight, or a probability, expressing a degree of belief about the truth value of certain atoms. In this paper, we focus on one of these semantics, the Credal Semantics, highlight some of the differences with other proposals, and discuss some possible future works.
PhysWM: Physical World Models for Robot Learning

Within the last decade machine learning methods have shown remarkable results in pattern recognition tasks and behavior learning. However, when applied to real-world robotics tasks, these approaches have limitations, such as sample inefficiency and limited generalization to out-of-distribution samples. Despite the availability of precise physics in simulation engines, model-based reinforcement learning (RL) resorts to learning an approximation of these dynamics. On the other hand, optimal control approaches often assume a static, complete model of the world, addressing the simulation-reality gap by adding low level controllers. In order to handle these issues, we propose a hybrid simulator consisting of differentiable physics and rendering modules, which employ symbolic representations and reduce the model complexity of neural policies, while retaining gradient computation for model and behavior optimization. Moreover, this reduced parametric representation enables the use of Bayesian inference to estimate the uncertainty over physical parameters. This uncertainty quantification allows us to generate a curriculum of exploration behaviors for continuously improving the world model.
Circularity in Case-Based Reasoning

We attempt to bring to the forefront the often-overlooked circularity in a wide variety of tasks linked to Case-Based Reasoning (CBR). This motivates the need for a methodical examination of circularity to arrive at new paradigms in CBR. In our previous work on RelCBR , we have illustrated that the current CBR paradigm can benefit from the consideration of circularity. We propose several other directions of research wherein the state-of-the-art approaches are still agnostic to circularity and argue why the incorporation of circularity can yield improvement in performance. Approaches proposed for resolving the diverse variety of circularities can serve as templates for circularities of similar nature that emerges in different contexts.
Investigating the duality of CBR: Performance and Interpretability

Case-based reasoning (CBR) is an artificial intelligence (AI) methodology with applications across different domains. Its reasoning is rooted in human intuition making this methodology universally accepted as interpretable. In contrast, black box models like neural networks have a widely accepted notion with high-performance accuracy and low interpretability. Despite low interpretability, there is a ready acceptance of black box models in academia and industry for automated decision-making. The focus of this research is investigating the relationship between performance and interpretability for CBR by exploring a variety of ways to note the impact of enhancing performance on CBR interpretability.
Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval Time

Temporal Knowledge Graphs (TKGs) are an extension of Knowledge Graphs where facts are temporally scoped. They have recently received increasing attention in knowledge management, mirroring an increased interest in temporal graph learning within the graph learning community. While there have been many systems proposed for TKG learning, there are many settings to be considered, and not all of them are yet fully explored. In this position paper we identify a problem not yet approached, inductive future time prediction on interval-based TKGs, and formalise it as a machine learning task. We then outline several promising approaches for solving it, focusing on a neurosymbolic framework connecting TKG learning with the temporal reasoning formalism DatalogMTL.
Feature Deletion and Case Discovery in Case-Base Maintenance

Case-based reasoning solves a problem by adapting the solution to a similar problem already solved. Motivated in part by the swamping utility problem, case-base maintenance strategies support a compact, competent case base by deleting, modifying, or discovering cases. This research summary briefly presents four case-base maintenance strategies developed by the author: flexible feature deletion, adaptation-guided feature deletion, expansion-contraction compression, and predictive case discovery. Flexible feature deletion deletes components of cases instead of whole cases. Adaptation-guided feature deletion prioritizes components for deletion according to their recoverability via adaptation knowledge. Expansion-contraction compression, in addition to deleting cases, also adds cases in unexplored regions of the problem space. And predictive case discovery anticipates and acquires cases expected to be useful for solving future problems.
A Dataset of Contractual Events in Court Decisions

The promise of automation of legal reasoning is developing technology that reduces human time required for legal tasks or that improves human performance on such tasks. In order to do so, different methods and systems based on logic programming were developed. However, in order to apply such methods on legal data, it is necessary to provide an interface between human users and the legal reasoning system, and the most natural interface in the legal domain is natural language, in particular, written text. In order to perform reasoning in written text using logic programming methods, it is then necessary to map expressions in text to atoms and predicates in the formal language, a task referred generally as information extraction. In this work, we propose a new dataset for the task of information extraction, in particular event extraction, in court decisions, focusing on contracts. Our dataset captures contractual relations and events that affect them in some way, such as negotiations preceding a (possible) contract, the execution of a contract, or its termination. We conducted text annotation with law students and graduates, resulting in a dataset with 207 documents, 3934 sentences, 4440 entities, and 1794 events. We describe here this resource, the annotation process, its evaluation with inter-annotator agreement metrics, and discuss challenges during the development of this resource and for the future.
Mass/Count Distinction and Japanese Semantics ∗

The recent development of plural logic has been a good news for a student of the semantics of a language like Japanese which has no systematic distinction between singular and plural. But plural logic is applicable only to countable predicates; it is not applicable to non-countable predicates. Thus, the first question that must be settled before we may apply plural logic to Japanese is to make sure that it has countable predicates. I argue that Japanese has indeed countable predicates and that they can be recognized by a kind of numeral suffixes which can modify them. Japanese numeral suffixes are divided into three classes, namely, (1) sortal suffixes, or classifiers, (2) unit-forming suffixes, and (3) measure suffixes; they can be distinguished from each other by a certain simple test. I argue that a sortal suffix’s contribution to the meaning of a sentence in which it occurs is not to its truth-conditional content but to its conventional implicature only, and hence that a noun which typically occurs with a sortal suffix has an individuating force by itself. On the basis of the above, we can single out a class of count nouns. The resulting distinction between count nouns and non-count nouns largely coincides with the count/mass distinction, and we argue that, in contrast to what is commonly thought, this distinction is more robust in a classifier language like Japanese than in a number-sensitive language like English.
Predictive Analytics for Re-Hospitalization and Disease Prediction

Hospital readmissions can have serious consequences for both patients and healthcare providers. They can lead to increased healthcare costs, lower patient satisfaction, and negative health outcomes. When patients are readmitted to the hospital soon after being discharged, their risk of morbidity and mortality increases. Additionally, hospitals may face penalties for having too many readmissions. To address this issue, researchers are investigating the potential of using machine learning techniques to predict a patient’s risk for readmission and illness progression. A large dataset of electronic health records from a major hospital was analyzed, containing patient information on medical histories, diagnoses, treatments, and personal characteristics. Several machine learning techniques, such as logistic regression, decision trees, random forests, and neural networks, were utilized to detect important risk factors and develop predictive models for re-hospitalization and disease progression. The performance of each machine learning model was assessed by the researchers using diverse metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The study showed that machine learning techniques have the potential to improve patient outcomes by identifying high-risk individuals who may benefit from targeted interventions. The paper presents a range of approaches to explore the factors that contribute to hospital readmissions and introduces a predictive model that employs machine learning algorithms to identify patients who are at a higher risk of readmission. The research involved patients who were admitted to a tertiary hospital, and the model’s effectiveness was assessed using several metrics. The article discusses the possible implications of the model for clinical practice and future research, emphasizing the potential of machine learning algorithms to predict hospital readmissions and enhance patient outcomes. [1]
TRACEM - Towards a Standard Metamodel for Execution Traces in Model-Driven Reverse Engineering

Reverse engineering is a crucial stage in the software modernization process. The current techniques available in existing CASE tools provide forward engineering and limited facilities for reverse engineering, dynamic analysis in particular. The Architecture-Driven Modernization initiative has defined standards to support the modernization process in the model-driven engineering (MDE) context. Standardization increases interoperability between different tools enabling a new generation of solutions to benefit the whole industry and encourage collaboration among complementary vendors. In this paper, we present TRACEM, a metamodel to represent trace information under a standard representation. This metamodel complements a MDE framework for software modernization that aims to integrate static and dynamic analysis techniques during the reverse engineering process. This paper includes a case study that exemplifies how dynamic information combined with static information allows improving the whole reverse engineering process.
The Kilimanjaro hypothesis: sexual reproduction as a conserver of replicative fidelity and an adaptation to unstable environments

This review focuses on three critical questions in evolutionary biology: (1) why do virtually all multicellular and many unicellular organisms reproduce sexually? (2) Why have some animals, such as peacocks and birds of paradise, evolved extravagant ornaments and complex mating rituals that appear detrimental? (3) How does natural selection maintain high-fidelity genetic replication? I present a simple “ Kilimanjaro hypothesis” that attempts to answer all three questions. First, I note that the proteins that replicate DNA are themselves specified by genes that must, of course, be subject to mutation. This means that replicative fidelity must vary between individuals. Moreover, many organisms choose their mates by responding to various biochemical mechanisms, physical displays, and complex behaviors, often generated by the interaction of many gene products acting together. I propose that natural selection consistently adds unnecessary complexity to the mechanisms that transfer genetic material between individuals. Since most mutations are either harmful or neutral, mutator mutations are likely to disrupt these mechanisms - which must reduce the chance that individuals with error-prone replisomes will mate successfully. Therefore, sex, with its many diverse and intricate variations, may have evolved partly to provide an effective filter to reduce the dissemination of mutator mutations. Sex may, in particular, have allowed ancient organisms to colonize unstable environments where intense selective pressures would apply. Many puzzling biological phenomena among sexual organisms can be explained along these lines. The migration and spawning of Atlantic salmon and the complex displays of birds of paradise may, for example, be best understood as “tests” to esta blish that potential sexual partners possess genes for high-fidelity genetic replication. Animals that have developed ornaments that appear harmful, such as peacocks, and animals that undertake remarkable migrations, such as arctic terns and monarch butterflies, may provide conspicuous examples of such tests. I also make suggestions for experiments to test the hypothesis.