Miracle-dz/UniFAR-FTUs huggingface dataset:
Positional games on graphs

The theory of Positional Games is an exciting and relatively young area of Combinatorics, though its origins can be traced back to Classical Game Theory. Its goal is to give a mathematical framework for analyzing games of thought. Positional games are strongly related to several other branches of Combinatorics such as Ramsey Theory, Extremal Graph and Set Theory and the Probabilistic Method. The subject has proven to be quite instrumental in deriving important results in Theoretical Computer Science, in particular in derandomization and algorithmization of important probabilistic tools. Although sporadic papers on positional games appeared much earlier, the beginning of their systematic study can be attributed to two, by now classical, papers: that of Hales and Jewett from 1963 [41], and that of Erdős and Selfridge from 1973 [36]. The significance of these two papers certainly cannot be overestimated and goes far beyond the realm of Positional Games: the Hales-Jewett theorem is one of the cornerstones of modern Ramsey Theory, the Erdős-Selfridge argument was essentially the first derandomization procedure, a central concept in the theory of algorithms. This thesis contains several novel contributions to the theory of Positional Games such as new instances of the Erdős paradigm, a general criterion for Avoider’s win in AvoiderEnforcer games, surprising results regarding the monotonicity of such games, a study of the duration of play, game theoretic strengthening of results in Random Graphs Theory, a new class of hybrid games that we call Bart-Moe games and more. This thesis also includes a result which is not directly connected to games; it is a novel sufficient condition for Hamiltonicity of relatively sparse graphs, based on expansion and high connectivity. It is included because its proof was found through research in Positional Game Theory, and later on, this criterion had found many other applications, in Positional Game Theory as well as in other branches of Combinatorics.
ISMS in small public sector organisations: requirements and design of a procedural approach

At a time when information technology is growing faster than ever before, information security management system (ISMS) assessment has become one of the most important aspects of most public sector organisations. The dependency on technology for almost every single process in an organisation has put ISMS at the top of the corporate agenda of public sector organisations. For public organisations in particular, the NIS 2 Directive describes abstract requirements for the development of an ISMS. On the other hand, only a few public administrations operate an ISMS. In this context, this paper analyses the requirements of the NIS-2 Directive and complements them with the obstacles and reasons for success in the introduction of ISMS in small public sector organisations (SPSO). At the same time, minimum requirements should be defined that help municipal administration set up an information security management system quickly and easily. This paper summarizes the different requirements and generates a foundation for a rough procedural model, for implementing the upcoming requirements of the NIS 2 Directive quickly and easily in local governments.
Emotion Recognition in Adaptive Virtual Reality Settings: Challenges and Opportunities

Recently, there has been a notable surge of interest in Emotion Recognition (ER) systems, primarily due to their potential in improving interactions between humans and computers. Meanwhile, Virtual Reality (VR) has emerged as a groundbreaking technology that is also capable of transforming human-computer interaction through the simulation of immersive and flexible environments. The integration of ER into VR holds great promise for further advancing human-computer interaction by allowing the virtual environment to adapt to the user’s emotional state. This adaptive VR setting is particularly relevant in fields such as education and gaming, where there is often the need to adapt the content to a person’s emotions. However, applying traditional ER systems to adaptive VR settings comes with several challenges. In this paper, we identify the key differences between traditional ER and ER performed in VR environments. Specifically, we argue that the two scenarios primarily differ in terms of data collection methodologies and handling multimodality. After reviewing the main modalities considered in ER, and describing existing datasets, we delve into the challenges associated with these factors, highlighting the limitations of using traditional datasets in adaptive VR settings, and the fact that traditional ER models are not designed to effectively handle the multiple modalities arising from the VR setting. In addition to discussing these challenges, we also explore unique opportunities that arise from overcoming them. These opportunities include acquiring diverse datasets, eliciting genuine emotional responses, and exploiting multiple data modalities.
BulkECexplorer: a bulk RNAseq compendium of five endothelial subtypes that predicts whether genes are active or leaky

Transcriptomic data obtained by single cell (sc) RNAseq or bulk RNAseq can be mined to understand the molecular activity of cell types. Yet, lowly expressed but functional genes may remain undetected in RNAseq experiments for technical reasons, such as insufficient read depth or gene drop out in scRNAseq assays. By contrast, bulk RNAseq assays may detect lowly expressed mRNA transcripts thought to be the biologically irrelevant products of leaky transcription. To more accurately represent a cell’s functional transcriptome, we propose compiling many bulk RNAseq datasets into a compendium and applying established classification models to predict whether the detected genes are likely active or leaky in that cell type. Here, we have created such a compendium for vascular endothelial cells from several mouse and human organs, termed the BulkECexplorer.
Signature Informed Sampling for Transcriptomic Data

Working with transcriptomic data is challenging in deep learning applications due to its high dimensionality and low patient numbers. Deep learning models tend to overfit this data and do not generalize well on out-of-distribution samples and new cohorts. Data augmentation strategies help alleviate this problem by introducing synthetic data points and acting as regularisers. However, existing approaches are either computationally intensive or require parametric estimates. We introduce a new solution to an old problem - a simple, non-parametric, and novel data augmentation approach inspired by the phenomenon of chromosomal crossover. Based on the assumption that there exist non-overlapping gene signatures describing each phenotype of interest, we demonstrate how new synthetic data points can be generated by sampling gene signatures from different patients under certain phenotypic constraints. As a case study, we apply our method to transcriptomic data of colorectal cancer. Through discriminative and generative experiments on two different datasets, we show that our method improves patient stratification by generating samples that mirror biological variability as well as the models’ robustness to overfitting and distribution shift. Our approach requires little to no computation, and outperforms, or at the very least matches, the performance of established augmentation methods.
Adaptive divergence in the eyes of Heliconius butterflies likely contributes to pre- and post-mating isolation

When populations experience different sensory conditions, natural selection may favor whole sensory system divergence, from the peripheral structures to the brain. We characterized the outer eye morphology of sympatric Heliconius species from different forest types, and their first-generation reciprocal hybrids to test for adaptive visual system divergence and hybrid disruption. In Panama, Heliconius cydno occurs in closed forests, whereas Heliconius melpomene resides in more open areas. Previous work has shown that, among wild individuals, H. cydno has larger eyes than H. melpomene, and there are heritable, habitat-associated differences in the visual brain structures that exceed neutral divergence expectations. Notably, hybrids have intermediate neural phenotypes, suggesting disruption. To test for similar effects in the visual periphery, we reared both species and their hybrids in common garden conditions. We confirm that H. cydno has larger eyes and provide new evidence that this is driven by selection. Hybrid eye morphology is more H. melpomene-like despite body size being intermediate, contrasting with neural trait intermediacy. Thus, eye morphology differences between H. cydno and H. melpomene are consistent with adaptive divergence, and when combined with previous neuroanatomy data, suggest hybrid visual system disruption due to mismatched patterns of intermediacy and dominance in the visual pathway.
Multiplicative frequency and angular speckle reduction in ultrasound imaging

Speckle is the major artifact in ultrasound imaging, and it is well-known that speckle can be reduced by compounding (averaging) images taken either at different frequencies or from different angles. By averaging images of a phantom taken over a frequency range and a 90° span of angles, the combined speckle reduction is demonstrated to be ~ 9× compared to non-compounded images, while the reduction with frequency or angle averaging resulted in reductions of ~ 3× individually. The rf input to the transducer is altered to vary the sound frequency and the phantom is rotated with respect to the transducer to obtain different imaging angles. Numerical simulations of sound scattered by randomly distributed point scatterers showed quantitative agreement with the experiment. Using a commercial system, a 6× reduction in speckle is demonstrated imaging a human wrist. A robot arm is used to move the transducer along a circular path to acquire images at 9 angles separated by 10°. The commercial system does not allow direct control of the input to the transducer, so the broadband signal detected is Fourier filtered to obtain images at different frequencies with ~ 2× reduced frequency range. Images taken at different angles contain distortions from speed of sound variations and pressure induced by the probe. Two forms of non-rigid image registration are applied to correct for the distortions and create a higher resolution composite image. A design for achieving ~10× speckle reduction with essentially no loss in imaging speed is described.
Cross-modal interactions and movement-related tactile gating: the role of vision

When engaging with the environment, multisensory cues interact and are integrated to create a coherent representation of the world around us, process that has been suggested to be affected by the lack of visual feedback in blind individuals. In addition, the presence of voluntary movement seems to be responsible for suppressing somatosensory information that is processed by the cortex, which can lead to a worse encoding of tactile information. In this work, we aim to explore how multisensory processing can be affected by active movements and the role of vision in this process. To this end, we measure the precision of 18 sighted controls and 18 blind individuals in a velocity discrimination task. The participants were instructed to detect the stimulus that contained the faster speed, between a sequence of two, in both passive and active touch conditions. The sensory stimulation could be either just tactile or audio-tactile, where a non-informative sound occurred simultaneously with the tactile stimulation. The measure of precision was obtained by computing the Just Noticeable Difference (JND) of each participant. The results show worse precision in the bimodal audio-tactile sensory stimulation in the active condition for the sighted group but not for the blind one. In the sighted group, the noise of the tactile feedback might make them more vulnerable to the noisy interference of the audio modality; however, this is not the case for the blind one, which seems to be only affected by the movement itself. Our work should be considered when developing next-generation haptic devices; moreover, it supports the need for action in the blind population.
Segmental Duplications Drive the Evolution of Accessory Regions in a Major Crop Pathogen

Many pathogens evolved compartmentalized genomes with conserved core and variable accessory regions which carry effector genes mediating virulence. The fungal plant pathogen Fusarium oxysporum has such accessory regions often spanning entire chromosomes. The presence of specific accessory regions influences the host range, and horizontal transfer of some accessory regions can modify the pathogenicity of the receiving strain. However, understanding how these accessory regions evolve in strains that infect the same host remains limited. Here, we define the pan-genome of 69 diverse Fusarium strains that cause Fusarium wilt of banana, a significant constraint to global banana production. In this diverse panel of Fusarium strains infecting banana, we analyzed the diversity and evolution of the accessory regions. Accessory regions in Fusarium strains infecting the same banana cultivar are highly diverse, and we could not identify any shared genomic regions and in planta induced effectors. We demonstrate that segmental duplications drive the evolution of accessory regions. Furthermore, we show that recent segmental duplications and aneuploidy occur specifically in accessory chromosomes and cause the expansion of accessory regions in F. oxysporum. Taken together we conclude that extensive recent duplications drive the evolution of accessory regions in Fusarium, which contribute to the evolution of virulence.
Relationship between Protein Conformational Stability and Its Immunogenicity When Administering Antigens to Mice Using Adjuvants

Antigen-presenting cells (APCs) are crucial in the immune system by breaking down antigens into peptide fragments that bind to major histocompatibility complex molecules. Previous research suggests that stable proteins may hinder CD4+ T cell stimulation by impeding antigen processing and presentation. Conversely, some proteins require stabilization to activate the immune response. This discrepancy may be influenced by various factors, including protein characteristics and the use of different adjuvants in animal experiments. Here, we investigated the effects of adjuvants on antigen administration, specifically focusing on the stability of the CH2 domain. Together with the previous study, we show that protein stability is also crucial in triggering an immune response in mice by binding protein antigens to B cell receptors on APCs. Together with the study so far, we propose that intrinsic protein stability is crucial for binding to B cell receptors on APCs in mice when administering antigens with adjuvants.
A Fast Self-supervised Dual Correlation Window based Stereo Matching

We propose a real-time dual-window-based dense Stereo Correspondence method. Although there have been a large number of related research in decades, Compared with deep learning and global optimization algorithms, correlation window based methods still have the advantages of speed, low memory requirements, and high robustness. The challenge in window-based method is to choose a matching window size that maintains detail and objects’ boundaries at the same time coverage of areas with low or repeated textures. In the face of this trade-off, the classical method prefers to choose a larger window in order to achieve higher robustness. We tackle this issue from a novel direction by proposing a Fast-Marching sampling algorithm to capture global features from a large window. Then aggregating the result with a small window with higher performance on details and objects’ boundaries to generate costs and perform WTA through a proposed continuous energy function. To verify the performance and feasibility of our method, Middlebury’s Stereo Matching benchmark is used. The experimental results demonstrated that our method can not only capture well the non-textured areas but also the details in the scene.
Querying monitoring and integrating web applications

As the number and variety of Web applications grow, more business opportunities emerge for providing new services based upon existing applications (e.g. virtual malls, comparative shopping). In particular, many enterprises nowadays use business processes for inter and cross-organizational applications. A business process (BP for short) consists of a group of business activities undertaken by one or more organizations in pursuit of some particular goal. It usually operates in a distributed environment and the software implementing it is fairly complex. Standards facilitate the design, deployment, and execution of BPs. In particular, the recent BPEL standard (Business Process Execution Language), provides an XML-based language to describe the interface between the participants in a process, as well as the full operational logic of the process and its execution flow. BPEL specifications are automatically compiled into executable code that implements the described BP and runs on a BPEL application server. Processes execution is traced, and their run-time behavior can be recorded in standard XML formats. In this thesis we argue that these new standards not only simplify software development, but, more interestingly from an information management perspective, they also provide an important new mine of information. Queries about the BPs, that were extremely hard (if not impossible) to evaluate when the BP logic was coded in complex programs are now potentially much easier given a declarative specification. Furthermore, sophisticated querying, that interleaves static analysis of the BP specification with run-time process monitoring, can now be used for a variety of critical
Bond Graph Modeling and LQR Based Neuro-Fuzzy Control of Spatial Inverted Pendulum

The dynamics of inverted pendulum systems have an inherit property of instability, nonlinearity and underactuation. Therefore, the inverted pendulum systems have been recognized as a benchmark problem to validate emerging controllers. In addition, the dynamics of many real-world system resembles the inverted pendulum systems. To broaden the diversity of this classical system, this work focuses on a new type of inverted pendulum i.e., spatial inverted pendulum. In contrast to classical inverted pendulum systems, this system is highly complex to model and control due to its large degrees of freedom as well as underactuation. This paper proposes the bond graph model of this complex system to derive its dynamic equations. The bond graph technique only requires kinematics of the model and derives the complex dynamics by itself. Furthermore, the robust neuro-fuzzy controller based on linear quadratic regulator (LQR) controller is designed and simulated for the stabilization of the system. The proposed controller provides an advantage of improved robustness compared to classical LQR controller. The robustness of the proposed controller is verified by simulation results for various pendulum masses. The results demonstrate that when the mass variation is significant, proposed controller outperforms the LQR controller by achieving superior performance and robustness to parameter uncertainties.
This work was carried out under the supervision of Dr. Ido Dagan

This thesis introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. We define the task and describe the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. Challenge. We further describe how textual entailment can be approximated and modeled at the lexical level and propose a lexical reference subtask and a correspondingly derived dataset. The thesis further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. We suggest that the proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. In addition, we describe two lexical models demonstrating the applicability of the probabilistic setting. Although our proposed models are relatively simple, as they do not rely on syntactic or other deeper analysis, they nevertheless achieved competitive results on the pascal rte challenge. Finally, the thesis presents a novel acquisition algorithm to identify lexical entail-ment relations from a single corpus focusing on the extraction of verb paraphrases. Most previous approaches detect individual paraphrase instances within a pair (or iii set) of comparable corpora, each of them containing roughly the same information, and rely on the given substantial level of correspondence of such corpora. We present a novel method that successfully detects isolated paraphrase instances within a single corpus without relying on any a-priori structure and information. Our instance based approach seems to address some of the drawbacks of distributional similarity based methods, in particular by providing a consistent scoring scale across different words. iv Preface Portions of this thesis are joint work and have appeared elsewhere.
Research Characteristics and Future Trends of Entrepreneurship Study In China: Based on the Perspective of Literature Analysis

Using the combination of Citespace and CNKI database literature analysis, this paper analyzes the core journals of entrepreneurship research and CSSCI source journals in the CNKI database, and finds that domestic entrepreneurship research is significantly affected by national policies; The scope and theme range are constantly expanding, and the perspectives are gradually diversified, but they have not formed scales; the research shows a distinct transitional and phased nature, which has not become a prominent research center; the authoritative research results of domestic academic circles are few and so on. The main reason is that current research is based on mainstream economic theory, and this is limiting the way entrepreneurship research is full of changes and uncertainties. Therefore, in the future, entrepreneurship will still maintain a high degree of attention, but without the breakthrough of entrepreneurial theory, it is difficult to form widely accepted research conclusions in the short term.This is also the bottleneck of entrepreneurial research.
A modeling method for strategic design of induced semantic digital nudging

There’s a growing interest in optimizing User Interface (UI) design to better align with organizational strategies and facilitate improved guidance of users towards specific options in a choice architecture, a kind of user behavior influencing known as "digital nudging". The paper reports on a Design Science Research project introducing a domain-specific modeling method to integrate digital nudging strategies with User Experience (UX) and feature portfolio design. This paper proposes Semantic Digital Nudging as a modeling method that semantically enriches Digital Nudges implemented in UX workflows by linking to target product features. This is deployed as a visual tool that embeds properties which can be queried to assess the friction certain nudges cause on User Experience and to maintain an inventory of by-design nudges. Linking the procedural knowledge in UI usage flows with product features and the nudging patterns makes this a multi-perspective domain-specific modeling approach that can help businesses to plan the layout of user choices, or help the user make more sustainable choices by increasing salience of healthier alternatives.