Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Quantum Group Key Agreement via Tree Key Graphs

Quantum key distribution (QKD) protocols are essential to guarantee information-theoretic security in quantum communication. Although there was some previous work on quantum group key distribution, they still face many challenges under a ``\textit{dynamic}'' group communication scenario. In particular, when the group keys need to be updated in real-time for each user joining or leaving to ensure secure communication properties, i.e., forward confidentiality and backward confidentiality. However, current protocols require a large amount of quantum resources to update the group keys, and this makes them impractical for handling large and dynamic communication groups. In this paper, we apply the notion of ``{\em tree key graph}'' to the quantum key agreement and propose two dynamic Quantum Group Key Agreement (QGKA) protocols for a join or leave request in group communications. In addition, we analyze the quantum resource consumption of our proposed protocols. The number of qubits required per join or leave only increases logarithmically with the group size. As a result, our proposed protocols are more practical and scalable for large and dynamic quantum group communications.
Research on anomaly detection based on network data

With the rapid development of network technology, network-based applications are becoming more and more diversified, and network security issues are becoming more and more significant. It is an urgent task to study methods to protect system security. Log system, as an important part of information system, can record all behaviors generated by the system, and most of the security risks in the system can be found by analyzing log system. To achieve the purpose of improving the system security performance. As an active network security protection measure, anomaly detection technology can detect abnormal behaviors and respond in time to effectively prevent abnormal behaviors and reduce the risk of system destruction. In this paper, public offline logs are used as data sources, common anomaly detection algorithms are studied, and effective information is obtained from massive log data to perceive the situation of network security.
Data stream scheduling algorithm based on link congestion probability optimization

Aiming at the problem of increased network delay due to congestion caused by link collision caused by improper traffic scheduling in the industrial Internet, this paper proposes a flow scheduling strategy based on probability-based data flow path planning within a period of time. On the basis of the software-defined network centralized control architecture, the controller collects the switches and edges’ node information in the topological network to meet the QoS of different services as much as possible while ensuring that more services are responded. In the scheduling period, the congestion probability of each link is introduced, and the relative delay is obtained by combining the delay required by the link. The optimal path under the current strategy is selected to optimize the result of data flow scheduling and reduce data flow transmission delay.
Heuristic deep reinforcement learning for online packing

In order to improve the utilization rate of loading space, a model of online packing problem was established. In this paper, we introduce an advanced technique for incorporating heuristic knowledge distillation into reinforcement learning agents. Our approach utilizes a two-stage learning process, where a teacher model, trained with heuristics, guides a student model in acquiring knowledge directly from the environment through reinforcement learning. To assess the performance of our method, we applied it to a logistics loading scenario, addressing an online packing problem in simulated world environments. Experimental results revealed that our method outperforms both the heuristic and conventional RL models, highlighting the effectiveness of the heuristic knowledge distillation architecture in enhancing RL agent performance across diverse settings.
Research on segmentation algorithm of liver and tumor

Liver cancer is one of the common diseases. It is of great significance to realize the automatic and accurate segmentation of liver and tumors in clinical medicine. The boundary blurring and the difficulty of feature extraction in CT images of liver and tumors are perplex, an improved medical segmentation model SKE-Unet++based on Unet++ was proposed. The SKE-Unet++ model adopts the connection mode of full-scale feature fusion and integrates coarse-grained semantics and fine-grained semantic information extraction features at full scale. In order to extract more important features after convolution, the SE (Squeeze Excitation) module is added to enhance the channel features of the input feature map. The foreground sample in CT image is much smaller than the background sample, cross entropy loss function and dice loss function are combined to solve the class imbalance problem. Compared with the SKE-Unet++ model, the Dice, Jaccard and ASSD evaluation indexes of liver segmentation task in Lits data set increased by 1.03%, 0.29% and 0.2897mm respectively, and the three indexes of liver tumor segmentation task increased by 7.54%, 9.33% and 1.2077mm respectively, which confirmed the validity of the model, and provided reference for automatic segmentation of liver cancer medical images.
Extracellular symbiont colonizes insect during embryo development

Insects typically acquire their beneficial microbes early in development. Endosymbionts housed intracellularly are commonly integrated during oogenesis or embryogenesis, whereas extracellular microbes are only known to be acquired after hatching by immature instars such as larvae or nymphs. Here, however, we report on an extracellular symbiont that colonises its host during embryo development. Tortoise leaf beetles (Chrysomelidae: Cassidinae) host their digestive bacterial symbiont Stammera extracellularly within foregut symbiotic organs, and in ovary-associated glands to ensure its vertical transmission. We outline the initial stages of symbiont colonization and observe that while the foregut symbiotic organs develop three days prior to larval emergence, they remain empty until the final 24 hours of embryo development. Infection by Stammera occurs during that timeframe, and prior to hatching. By experimentally manipulating symbiont availability to embryos in the egg, we describe a 12-hour developmental window governing colonization by Stammera. Symbiotic organs form normally in aposymbiotic larvae, demonstrating that these Stammera-bearing structures develop autonomously. In adults, the foregut symbiotic organs are already colonized following metamorphosis and host a stable Stammera population to facilitate folivory. The ovary-associated glands, however, initially lack Stammera. Symbiont abundance subsequently increases within these transmission organs, thereby ensuring sufficient titers at the onset of oviposition ∼29 days following metamorphosis. Collectively, our findings reveal that Stammera colonization precedes larval emergence, where its proliferation is eventually decoupled in adult beetles to match the nutritional and reproductive requirements of its host.
Progression of the FIB-4 index among patients with chronic HCV infection and early liver disease

Background and aims Historical paired liver biopsy studies are likely to underestimate current progression of disease in patients with chronic hepatitis C virus (HCV) infection. We aimed to assess liver disease progression according to the non-invasive Fibrosis-4 (FIB-4) index in patients with chronic HCV and early disease. Methods and results Patients diagnosed with chronic HCV and FIB-4 <3.25 from four international liver clinics were included in a retrospective cohort study. Follow-up ended at start of antiviral therapy resulting in sustained virological response, at time of liver transplantation or death. Primary outcome of advanced liver disease was defined as FIB-4 >3.25 during follow-up. Survival analyses were used to assess time to FIB-4 >3.25. In total, 4286 patients were followed for a median of 5.0 (IQR 1.7–9.4) years, during which 41 071 FIB-4 measurements were collected. At baseline, median age was 47 (IQR 39–55) years, 2529 (59.0%) were male, and 2787 (65.0%) patients had a FIB-4 <1.45. Advanced liver disease developed in 821 patients. Overall, 10-year cumulative incidence of advanced disease was 32.1% (95% CI 29.9% to 34.3%). Patients who developed advanced disease showed an exponential FIB-4 increase. Among patients with a presumed date of HCV infection, cumulative incidence of advanced disease increased 7.7-fold from 20 to 40 years as opposed to the first 20 years after HCV infection. Conclusions The rate of advanced liver disease is high among chronic HCV-infected patients with early disease at time of diagnosis, among whom liver disease progression accelerated over time. These results emphasise the need to overcome any limitations with respect to diagnosing and treating all patients with chronic HCV across the globe.
Overcoming Observation Bias for Cancer Progression Modeling

Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the “conditioning on a collider” bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects. The data, code, and results are available at https://github.com/cbg-ethz/ObservationMHN.
ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer – study protocol

Introduction Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%–40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy. Methods ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs. Ethics and dissemination The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.
A Benchmark of state-of-the-art Deconvolution Methods in Spatial Transcriptomics: Insights from Cardiovascular Disease and Chronic Kidney Disease

A major challenge in sequencing based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds-to thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. While ST is becoming essential for drug discovery especially in Cardiometabolic diseases, to date no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods Cell2location, RCTD and spatialDWLS have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess best performance when using human data from Cardiovascular Disease (CVD) data and Chronic Kidney Disease (CKD) from patients at different pathological states, evaluated using expert annotation. In this benchmark, we found that all three methods performed comparably well in deconvoluting verifiable cell types including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples while Cell2location on average achieved the highest performance across test experiments. While all three methods had similar accuracies Cell2location need less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.
Mining Information From Legal Sentences in KlonDikE

The “Next Generation UPP” (NGUPP) Italian project aims at devising and implementing new collaborative schemes between universities and judicial offices for improving the efficiency and performance of justice in north-west Italy. The University of Genova was assigned different tasks in NGUPP, including the semantic analysis of sentences and the study of potential bias on a massive scale for legal and analytical purposes. KlonDikE (for Knowledge Driven Extraction engine), with its ability to determine the type of divorce sentence, extract the master data of the parties and children, extract and visualize statistical information, and anonymize the original document, contributed to achievement of the NGUPP semantic analysis objective.
RAPACES OBSERVADAS EN UN ÁREA SELVÁTICA DE SAN PEDRO, MISIONES, ARGENTINA

Raptors observed in a forest area of San Pedro, Misiones, Argentina. - Between January 1994 and September 1998, we visited an area covered with primary and secondary paranaense forest, Department of San Pedro, Misiones, for seven times. The aim of our visits was to assess the composition of the raptor assembly at the study area. During 48 days of fieldwork we obtained observations on the 21 species of diurnal raptors and on the four species of nocturnal raptors listed in this work. The Lesser Yel- low-headed Vulture (Cathartes burrovianus), the Gray-headed Kite (Leptodon cayanensis), the Hook-billed Kite (Chondrohierax uncinatus), the Gray-bellied Hawk (Accipiter poliogaster), the Bicolored Hawk (Accipiter bicolor), and the White-rumped Hawk (Buteo leucorrhous) are cited for the first time for the Department of San Pedro. We found Ornate Hawk-eagles (Spizaetus ornatus) and Rufous-thighed Hawks (Accipiter erythronemius) engaged in aerial sexual displays on September 1994. One pair of Black and White Hawk-eagles (Spizastur melanoleucus) was observed. We believe that the absence of open-country birds of prey known to occur in the Department of San Pedro could indicate that the study area is in good environmental condition. Resumen. - Entre Enero de 1994 y Septiembre de 1998, visitamos en siete oportunidades un area cubierta por selva paranaense primaria y secundaria ubicada en el Departamento de San Pedro, Misiones, con el objeto de conocer la composicion del elenco de aves rapaces presentes en el lugar. Cuarenta y ocho dias de campana nos permitieron obtener observaciones de las 21 especies de aves de presa diurnas y de las cuatro especies de aves rapaces nocturnas listadas en este trabajo. El Jote Cabeza Amarilla (Cathartes burrovianus), el Milano Cabeza Gris (Leptodon cayanensis), el Milano Pico Garfio (Chondrohierax uncinatus), el Esparvero Grande (Accipiter poliogaster), el Esparvero Variado (Accipiter bicolor) y el Taguato Negro (Buteo leu- corrhous), son citados por primera vez para el Departamento de San Pedro. Encontramos Aguilas Crestudas Reales (Spizaetus ornatus) y Esparveros Comunes (Accipiter erythronemius) involucrados en despliegues sexua- les aereos en Septiembre de 1994. Se observo una pareja de Aguilas Viudas (Spizastur melanoleucus). Cree- mos que la ausencia de rapaces de areas abiertas, que se conoce estan presentes en el Departamento de San Pedro, podria indicar que el area de estudio esta en buenas condiciones de conservacion. Aceptado el 16 de Marzo de 2002.
Predicting the dynamic interaction between intrinsically disordered proteins

Interactions between intrinsically disordered proteins (IDPs) are highly dynamic, their interaction interfaces are diverse and affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-LSTM Network to capture the dynamic nature of IDP conformation and predict the behavior of IDP interaction. The training data for our method is obtained from atomistic molecular dynamics (MD) simulations. Our method models multiple conformations of IDP as a dynamic graph which can effectively describe the fluctuation of its flexible conformation. GSALIDP can effectively predict the interaction sites of IDP as well as the contact residue pairs between IDPs. Its performance to predict IDP interaction is on par with or even better than the conventional models to predict the interaction of structural proteins. To the best of our knowledge, this is the first machine-learning method to realize the prediction of interaction behavior of IDPs. Our framework can be exploited to study other IDP-mediated processes such as folding-upon-binding and fuzzy interaction, wherein the dynamic nature of IDP conformation is also essential.
Evaluation der interaktiven NoPhish Präsenzschulung

Phishing Angriffe stellen nach wie vor eine große Bedrohung für Pri-vatpersonen und Unternehmen, Vereine und öffentliche Einrichtun-gen dar. Es gibt bereits viel Forschung zur Effektivität von Security Awareness-Maßnahmen und insbesondere im Kontext von Phishing Angriffen. Die meisten Paper messen den Effekt unmittelbar nach der Durchführung der Maßnahme. Nur wenige Paper unter-suchen, wie lange der Effekt hält, sprich wann eine Auffrischungs-maßnahme durchgeführt werden sollte. Ziel dieses Papers ist es, zu bestätigen, dass der Effekt der NoPhish Präsenzschulung vier Monate anhält. Hierzu wurde eine entsprechende Studie im Rahmen einer freiwilligen Präsenzschulung mit 19 Teilnehmener/innen bei der Polizei durchgeführt und konnte die bisherigen Ergebnisse nicht bestätigen.
Models of Fuzzy Identification of Cyber Incidents in Information and Communication Systems by Intelligent SIEM Systems

The issue of detecting cyber incidents that occur during the functioning of information and communication systems, based on the use of a SIEM system in the cyber protection circuit, as the basis for its design, is considered. This study concludes that the use of the latest information technologies, such as artificial intelligence, intelligent data analysis, big data processing, and machine learning, is necessary to improve cyber incident detection. These technologies make it possible to increase the effectiveness of the SIEM system by minimizing human involvement in solving cyber incident response tasks. The analysis of methods used to detect cyber incidents has revealed their major limitations, particularly their inability to consistently provide the desired outcome in scenarios where information about such incidents is incomplete or inaccurate. To address this issue, a new approach to detect cyber incidents is proposed. This approach is based on using knowledge delivery models within the knowledge base that provide information about the signs of cyber incidents occurring in information and communication systems, as well as the various types of cyber incidents. This knowledge will be used to identify cyber incidents by collecting and processing expert information, utilizing the theory of fuzzy sets. Additionally, the proposed approach includes the formalization of the causal relationships between the variables "signs of cyber incidents" and "types of cyber incidents. " This is achieved by describing these relationships in natural language using the theory of fuzzy sets and linguistic variables. The models proposed in this study can improve the effectiveness of cyber incident detection by addressing the limitations of current methods.
Linguistic category induction and tagging using the paradigmatic context representations with substitute words (Düşey kelime bağlamlarını olası kelimeler ile temsil ederek dil bilimsel sözcük kümeleri ve etikletlerinin bulunması)

This thesis introduces a new paradigmatic representation of word contexts. Paradigmatic representations of word context are constructed from the potential substitutes of a word, in contrast to syntagmatic representations, which are constructed from the properties of neighboring words. The potential substitutes are calculated by using a statistical language model that is trained on raw text without any annotation or supervision. Thus, each context is represented as a distribution of substitute words. This thesis introduces models with different properties that can incorporate the new paradigmatic representation, and discusses the applications of these models to the tagging task in natural language processing (NLP). In a standard NLP tagging task, the goal is to build a model in which the input is a sequence of observed words, and the output, depending on the level of supervision, is a sequence of cluster-ids or predefined tags. We define 5 different models with different properties and supervision requirements. The first model ignores the identity of the word, and clusters the substitute distributions without requiring supervision at any level. The second model represents the co-occurrences of words with their substitute words, and thus incorporates the word identity and context information at the same time. To construct the co-occurrence representation, this model discretizes the substitute distribution. The third model uses probabilistic voting to estimate the distribution of tags in a given context. Unlike the first and second models, this model requires the availability of a word-tag dictionary which can provide all possible tags of each given word. The fourth model proposes two extensions to the standard HMM-based tagging models in which both the word identity