Malaria is caused by infection with protozoan parasites belonging to the genus Plasmodium transmitted by female Anopheles species mosquitoes. Our understanding of the malaria parasites begins in 1880 with the discovery of the parasites in the blood of malaria patients by Alphonse Laveran. The sexual stages in the blood were discovered by William MacCallum in birds infected with a related haematozoan, Haemoproteus columbae, in 1897 and the whole of the transmission cycle in culicine mosquitoes and birds infected with Plasmodium relictum was elucidated by Ronald Ross in 1897. In 1898 the Italian malariologists, Giovanni Battista Grassi, Amico Bignami, Giuseppe Bastianelli, Angelo Celli, Camillo Golgi and Ettore Marchiafava demonstrated conclusively that human malaria was also transmitted by mosquitoes, in this case anophelines. The discovery that malaria parasites developed in the liver before entering the blood stream was made by Henry Shortt and Cyril Garnham in 1948 and the final stage in the life cycle, the presence of dormant stages in the liver, was conclusively demonstrated in 1982 by Wojciech Krotoski. This article traces the main events and stresses the importance of comparative studies in that, apart from the initial discovery of parasites in the blood, every subsequent discovery has been based on studies on non-human malaria parasites and related organisms.
While Multimodal Large Language Models (MLLMs) have advanced GUI navigation agents, current approaches face limitations in cross-domain generalization and effective history utilization. We present a reasoning-enhanced framework that systematically integrates structured reasoning, action prediction, and history summarization. The structured reasoning component generates coherent Chain-of-Thought analyses combining progress estimation and decision reasoning, which inform both immediate action predictions and compact history summaries for future steps. Based on this framework, we train a GUI agent, \textbf{GUI-Rise}, through supervised fine-tuning on pseudo-labeled trajectories and reinforcement learning with Group Relative Policy Optimization (GRPO). This framework employs specialized rewards, including a history-aware objective, directly linking summary quality to subsequent action performance. Comprehensive evaluations on standard benchmarks demonstrate state-of-the-art results under identical training data conditions, with particularly strong performance in out-of-domain scenarios. These findings validate our framework's ability to maintain robust reasoning and generalization across diverse GUI navigation tasks. Code is available at https://leon022.github.io/GUI-Rise.
Guy Tennenholtz, Jihwan Jeong, Chih-Wei Hsu
et al.
Effective decision making in partially observable environments requires compressing long interaction histories into informative representations. We introduce Descriptive History Representations (DHRs): sufficient statistics characterized by their capacity to answer relevant questions about past interactions and potential future outcomes. DHRs focus on capturing the information necessary to address task-relevant queries, providing a structured way to summarize a history for optimal control. We propose a multi-agent learning framework, involving representation, decision, and question-asking components, optimized using a joint objective that balances reward maximization with the representation's ability to answer informative questions. This yields representations that capture the salient historical details and predictive structures needed for effective decision making. We validate our approach on user modeling tasks with public movie and shopping datasets, generating interpretable textual user profiles which serve as sufficient statistics for predicting preference-driven behavior of users.
Modern out-of-order CPUs heavily rely on speculative execution for performance optimization, with branch prediction serving as a cornerstone to minimize stalls and maximize efficiency. Whenever shared branch prediction resources lack proper isolation and sanitization methods, they may originate security vulnerabilities that expose sensitive data across different software contexts. This paper examines the fundamental components of modern Branch Prediction Units (BPUs) and investigates how resource sharing and contention affect two widely implemented but underdocumented features: Bias-Free Branch Prediction and Branch History Speculation. Our analysis demonstrates that these BPU features, while designed to enhance speculative execution efficiency through more accurate branch histories, can also introduce significant security risks. We show that these features can inadvertently modify the Branch History Buffer (BHB) update behavior and create new primitives that trigger malicious mis-speculations. This discovery exposes previously unknown cross-privilege attack surfaces for Branch History Injection (BHI). Based on these findings, we present three novel attack primitives: two Spectre attacks, namely Spectre-BSE and Spectre-BHS, and a cross-privilege control flow side-channel attack called BiasScope. Our research identifies corresponding patterns of vulnerable control flows and demonstrates exploitation on multiple processors. Finally, Chimera is presented: an attack demonstrator based on eBPF for a variant of Spectre-BHS that is capable of leaking kernel memory contents at 24,628 bit/s.
Marcin Śniadecki, Anna Malitowska, Oliwia Musielak
et al.
Medicine is struggling with the constantly rising incidence of breast cancer. The key to this fight is to be able to speed up diagnosis, as rapid diagnosis reduces the number of aggressive or advanced cases. For this process to be effective, it is necessary to have the right attitude toward diagnosis as a research practice. Our critical analysis of diagnosis, as a methodology of medical science, reflects on it as a research practice that is regulated in a socio-subjective way by a methodological culture. This position allows us to contrast critical methodological culture with the habitual–practical, or methodical, culture of practicing diagnosis. We point to the interpretative status of medical analyses performed by medical historians by referring to Italian Renaissance paintings and historical–artistic interpretations. In this field, analyzing disputes between researchers as a clash of methodologies in the ways interpretation transforms signs into meaning is a critical methodological reflection. Medicine is a diverse scientific discourse with a paradigmatic structure in which new ways of conducting diagnostic tests may appear. It is only possible to see this from the methodological level. In addition, passive respect for existing patterns of conduct hinders an exchange of views between researchers, which limits the possibility of correcting research procedures. The ultimate consequence of such passivity is an inability to improve diagnosis, which, in turn, harms the interests of patients. In this regard, it is worth remembering that the paramount objective of diagnosis is not the disease, but the patient.
Measurements of jet substructure in Pb+Pb collisions provide key insights into the mechanism of jet quenching in the hot and dense QCD medium created in these collisions.This Letter presents a measurement of the suppression of large-radius jets with a radius parameter of R=1.0 and its dependence on the jet substructure. The measurement uses 1.72 nb−1 of Pb+Pb data and 255 pb−1 of pp data, both at sNN=5.02 TeV, recorded with the ATLAS detector at the Large Hadron Collider. Large-radius jets are reconstructed by reclustering R=0.2 calorimetric jets and are measured for transverse momentum above 200 GeV. Jet substructure is evaluated using charged-particle tracks, and the overall level of jet suppression is quantified using the jet nuclear modification factor (RAA). The jet RAA is measured as a function of jet pT, the charged kt splitting scale (d12), and the angular separation (ΔR12) of two leading sub-jets. The jet RAA gradually decreases with increasing d12, implying significantly stronger suppression of large-radius jets with larger kt splitting scale. The jet RAA gradually decreases for ΔR12 in the range 0.01−0.2 and then remains consistent with a constant for ΔR12 ≳ 0.2. The observed significant dependence of jet suppression on the jet substructure will provide new insights into its role in the quenching process.
We overview the history of primordial black hole (PBH) research from the first papers around 50 years ago to the present epoch. The history may be divided into four periods, the dividing lines being marked by three key developments: inflation on the theoretical front and the detection of microlensing events by the MACHO project and gravitational waves by the LIGO/Virgo/KAGRA project on the observation front. However, they are also characterised by somewhat different focuses of research. The period 1967-1980 covered the groundbreaking work on PBH formation and evaporation. The period 1980-1996 mainly focussed on their formation, while the period 1996-2016 consolidated the work on formation but also collated the constraints on the PBH abundance. In the period 2016-2024 there was a shift of emphasis to the search for evidence for PBHs and - while opinions about the strength of the purported evidence vary - this has motivated more careful studies of some aspects of the subject. Certainly the soaring number of papers on PBHs in this last period indicates a growing interest in the topic.
Federico Grazzini, Joshua Dorrington, Christian M. Grams
et al.
The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user-oriented post-processing. Here we describe a specific chain, based on a random forest pipeline, specialised in recognizing favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on Northern and Central Italy, taken as a testbed region, but is seamlessly extensible to other regions and timescales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded high-resolution precipitation dataset as the target truth, using the last 20 years of the ECMWF re-forecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger-scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.
Huy Nguyen, Christoph Treude, Patanamon Thongtanunam
With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit code representation for deep learning. Recent papers incorporate contextual data (e.g. call hierarchy) into vector representation to address program comprehension problems. This motivates further studies to explore additional contexts, such as version history, to enhance models' understanding of programs. That is, insights from version history enable recognition of patterns in code evolution over time, recurring issues, and the effectiveness of past solutions. Our paper presents preliminary evidence of the potential benefit of encoding contextual information from the version history to predict code clones and perform code classification. We experiment with two representative deep learning models, ASTNN and CodeBERT, to investigate whether combining additional contexts with different aggregations may benefit downstream activities. The experimental result affirms the positive impact of combining version history into source code representation in all scenarios; however, to ensure the technique performs consistently, we need to conduct a holistic investigation on a larger code base using different combinations of contexts, aggregation, and models. Therefore, we propose a research agenda aimed at exploring various aspects of encoding additional context to improve code representation and its optimal utilisation in specific situations.
Modeling policies for sequential clinical decision-making based on observational data is useful for describing treatment practices, standardizing frequent patterns in treatment, and evaluating alternative policies. For each task, it is essential that the policy model is interpretable. Learning accurate models requires effectively capturing the state of a patient, either through sequence representation learning or carefully crafted summaries of their medical history. While recent work has favored the former, it remains a question as to how histories should best be represented for interpretable policy modeling. Focused on model fit, we systematically compare diverse approaches to summarizing patient history for interpretable modeling of clinical policies across four sequential decision-making tasks. We illustrate differences in the policies learned using various representations by breaking down evaluations by patient subgroups, critical states, and stages of treatment, highlighting challenges specific to common use cases. We find that interpretable sequence models using learned representations perform on par with black-box models across all tasks. Interpretable models using hand-crafted representations perform substantially worse when ignoring history entirely, but are made competitive by incorporating only a few aggregated and recent elements of patient history. The added benefits of using a richer representation are pronounced for subgroups and in specific use cases. This underscores the importance of evaluating policy models in the context of their intended use.
Muhammad Shihab Rashid, Jannat Ara Meem, Vagelis Hristidis
Open Retrieval Conversational Question Answering (OrConvQA) answers a question given a conversation as context and a document collection. A typical OrConvQA pipeline consists of three modules: a Retriever to retrieve relevant documents from the collection, a Reranker to rerank them given the question and the context, and a Reader to extract an answer span. The conversational turns can provide valuable context to answer the final query. State-of-the-art OrConvQA systems use the same history modeling for all three modules of the pipeline. We hypothesize this as suboptimal. Specifically, we argue that a broader context is needed in the first modules of the pipeline to not miss relevant documents, while a narrower context is needed in the last modules to identify the exact answer span. We propose NORMY, the first unsupervised non-uniform history modeling pipeline which generates the best conversational history for each module. We further propose a novel Retriever for NORMY, which employs keyphrase extraction on the conversation history, and leverages passages retrieved in previous turns as additional context. We also created a new dataset for OrConvQA, by expanding the doc2dial dataset. We implemented various state-of-the-art history modeling techniques and comprehensively evaluated them separately for each module of the pipeline on three datasets: OR-QUAC, our doc2dial extension, and ConvMix. Our extensive experiments show that NORMY outperforms the state-of-the-art in the individual modules and in the end-to-end system.
Mambelli Marco, Donati Simone, Bellettini Giorgio
et al.
Since 1984 the Italian groups of the Istituto Nazionale di Fisica Nucleare (INFN) and Italian Universities, collaborating with the DOE laboratory of Fermilab (US) have been running a two-month summer training program for Italian university students. While in the first year the program involved only four physics students of the University of Pisa, in the following years it was extended to engineering students. This extension was very successful and the engineering students have been since then extremely well accepted by the Fermilab Technical, Accelerator, and Scientific Computing Division groups. Over the many years of its existence, this program has proven to be the most effective way to engage new students in Fermilab endeavors. Many students have extended their collaboration with Fermilab with their Master’s Thesis and PhD. Since 2004 the program has been supported in part by DOE in the frame of an exchange agreement with INFN. Over its almost 40 years of history, the program has grown in scope and size and has involved more than 550 Italian students from more than 20 Italian Universities, Several Institutes of Research, including ASI and INAF in Italy, and the ISSNAF Foundation in the US, have provided additional financial support. Since the program does not exclude appropriately selected non-Italian students, a handful of students from European and non-European Universities were also accepted over the years. Each intern is supervised by a Fermilab Mentor responsible for performing the training program. Training programs spanned from Tevatron, CMS, Muon (g-2), Mu2e, and Short Baseline Neutrino Experiments and DUNE design and experimental data analysis, development of particle detectors (silicon trackers, calorimeters, drift chambers, neutrino and dark matter detectors), design of electronic and accelerator components, development of infrastructures and software for exascale data handling, research on superconductive elements and on accelerating cavities, and theory of particle accelerators. Since 2010, within an extended program supported by the Italian Space Agency and the Italian National Institute of Astrophysics, a total of 30 students in physics, astrophysics, and engineering have been hosted for two months in the summer at US space science Research Institutes and laboratories. In 2015 the University of Pisa included these programs within its educational programs. Accordingly, Summer School students are enrolled at the University of Pisa for the duration of the internship and are identified and ensured as such. At the end of the internship, the students are required to write summary reports on their achievements. After positive evaluation by a University Examining Board, interns are acknowledged credits for their Diploma Supplement. The program was canceled in 2020 and 2021 due to the pandemic but restarted successfully in 2022. We believe this program can be taken as a model and easily adopted by interested institutions.
The ancient Duchy of Urbino (Marche and Emilia-Romagna Regions, Italy) is known for its spectacular landscapes linked to a unique geological history. This area owns an unexpected cultural resource, which concerns using its landscapes in art. Some great Renaissance artists, including Piero della Francesca, Raphael, and Leonardo, were so impressed by the landscapes that they reproduced them in their most famous paintings. This paper summarizes research concerned with their identification, employing a multidisciplinary method that has enabled the recognition of many morphologies. This contribution provides the scientific community with information on the methodology and regional and national projects developed in this area to enhance its cultural landscapes. Starting from the geological description of the territory, the research focuses on famous works by three great Renaissance artists, providing evidence and morphological details related to the recognition of places: “Nativity” by Piero della Francesca, “Madonna Litta” by Leonardo da Vinci, and “Knight’s Dream” by Raphael. Finally, it is proposed to make these landscapes a timeless resource through their inclusion in UNESCO’s cultural heritage. This contribution is addressed to representatives of the administration, conservation, and enhancement of artistic and landscape heritage to stimulate new perspectives for research, education, and tourism within the cultural heritage of this area.
The study of cultural artifact provenance, tracing ownership and preservation, holds significant importance in archaeology and art history. Modern technology has advanced this field, yet challenges persist, including recognizing evidence from diverse sources, integrating sociocultural context, and enhancing interactive automation for comprehensive provenance analysis. In collaboration with art historians, we examined the handscroll, a traditional Chinese painting form that provides a rich source of historical data and a unique opportunity to explore history through cultural artifacts. We present a three-tiered methodology encompassing artifact, contextual, and provenance levels, designed to create a "Biography" for handscroll. Our approach incorporates the application of image processing techniques and language models to extract, validate, and augment elements within handscroll using various cultural heritage databases. To facilitate efficient analysis of non-contiguous extracted elements, we have developed a distinctive layout. Additionally, we introduce ScrollTimes, a visual analysis system tailored to support the three-tiered analysis of handscroll, allowing art historians to interactively create biographies tailored to their interests. Validated through case studies and expert interviews, our approach offers a window into history, fostering a holistic understanding of handscroll provenance and historical significance.
Stefano Mammola, Mattia Falaschi, Gentile Francesco Ficetola
Summary: Emojis enable direct expressions of ideas and emotions in digital communication, also contributing to discussions on biodiversity conservation. Nevertheless, the ability of emojis to represent the Earth’s tree of life remains unexplored. Here, we quantified the taxonomic comprehensiveness of currently available nature-related emojis and tested whether the expanding availability of emojis enables a better coverage of extant biodiversity. Currently available emojis encompass a broad range of animal species, while plants, fungi, and microorganisms are underrepresented. Within animals, vertebrates are significantly overrepresented compared to their actual richness, while arthropods are underrepresented. Notwithstanding these taxonomic disparities, animal taxa represented by emojis more than doubled from 2015 to 2022, allowing an improved representation of both taxonomic and phylogenetic diversity, driven by the recent addition of cnidarians and annelids. Creating an inclusive emoji set is essential to ensure a fair representation of biodiversity in digital communication and showcase its importance for biosphere functioning.