Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperglycemia is associated with worse 3-year survival in older patients admitted to the intensive care unit after non-cardiac surgery: Post hoc analysis of a randomized trial

Objective Hyperglycemia is common in critically ill patients after surgery and is associated with worse perioperative outcomes. Yet, the impact of postoperative hyperglycemia on long-term outcomes remains unclear. We therefore analyzed the association between early postoperative hyperglycemia and 3-year overall survival in older patients who were admitted to the intensive care unit after surgery. Methods This was a post hoc analysis of database obtained from a previous randomized trial and 3-year follow-up. The underlying trial enrolled 700 patients aged 65 years or older who were admitted to the intensive care unit after elective non-cardiac surgery. Early postoperative time-weighted average blood glucose was calculated and was divided into three levels, i.e., <8.0 mmol/L, from 8.0 to 10.0 mmol/L, and >10.0 mmol/L. The primary outcome was 3-year overall survival. The association between time-weighted average blood glucose level and 3-year overall survival was analyzed with Cox proportional hazard regression models. Subgroup analyses were also performed in patients with or without diabetes, and in patients following cancer or non-cancer surgery. Results A total of 677 patients (mean age 74 years, 60% male sex) were included in the final analysis. Within 3 years after surgery, deaths occurred in 22.1% (30/136) of patients with time-weighted average blood glucose <8.0 mmol/L, compared with 35.7% (81/227) of those from 8.0 to 10.0 mmol/L (unadjusted hazard ratio 1.75, 95% CI 1.15 to 2.67, P = 0.009), and 36.9% (116/314) of those >10.0 mmol/L (unadjusted hazard ratio 1.91, 95% CI 1.28 to 2.85, P = 0.002). After adjustment for confounding factors, the risk of 3-year mortality remained higher in patients with time-weighted average blood glucose from 8.0 to 10.0 mmol/L (adjusted hazard ratio 2.28, 95% CI 1.47 to 3.54, P < 0.001) and in those >10.0 mmol/L (adjusted hazard ratio 2.00, 95% CI 1.29 to 3.10, P = 0.002). Similar results were obtained in the subgroups of patients without diabetes and patients following cancer surgery. Conclusion For older patients admitted to the intensive care unit after elective non-cardiac surgery, high early blood glucose (time-weighted average blood glucose ≥ 8.0 mmol/L) was associated with poor 3-year overall survival. The impact of moderate glycemic control on long-term survival deserves further investigation.
CUMUL & Co: High-Impact Artifacts for Website Fingerprinting Research

Anonymous communication on the Internet is about hiding the relationship between communicating parties. At NDSS ’16, we presented a new website fingerprinting approach, CUMUL, that utilizes novel features and a simple yet powerful algorithm to attack anonymization networks such as Tor. Based on pattern observation of data flows, this attack aims at identifying the content of encrypted and anonymized connections. Apart from the feature generation and the used classifier, we also provided a large dataset to the research community to study the attack at Internet scale. In this paper, we emphasize the impact of our artifacts by analyzing publications referring to our work with respect to the dataset, feature extraction method, and source code of the implementation. Based on this data, we draw conclusions about the impact of our artifacts on the research field and discuss their influence on related cybersecurity topics. Overall, from 393 unique citations, we discover more than 130 academic references that utilize our artifacts, 61 among them are highly influential (according to Se-manticScholar), and at least 43 are from top-ranked security venues. This data underlines the significant relevance and impact of our work as well as of our artifacts in the community and beyond
Neuroscience and the Multiple Realization of Cognitive Functions*

Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one‐to‐one structure‐function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience‐based arguments against multiple realization go wrong.
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation

Paraphrase identification has been formulated as a binary classification task to decide whether two sentences hold a paraphrase relationship. Existing paraphrase datasets only annotate a binary label for each sentence pair. However, after a systematical analysis of existing paraphrase datasets, we found that the degree of paraphrase cannot be well characterized by a single binary label. And the criteria of paraphrase are not even consistent within the same dataset. We hypothesize that such issues would limit the effectiveness of paraphrase models trained on these data. To this end, we propose a novel fine-grained paraphrase annotation schema that labels the minimum spans of tokens in a sentence that don’t have the corresponding paraphrases in the other sentence. Under this setting, we frame paraphrasing as a sequence tagging task. We collect 30k sentence pairs in English with the new annotation schema, resulting in the ParaTag dataset. In addition to reporting baseline results on ParaTag using state-of-art language models, we show that ParaTag is especially useful for training an automatic scorer for language generation evaluation. Finally, we train a paraphrase generation model from ParaTag and achieve better data augmentation performance on the GLUE benchmark than other public paraphrasing datasets.
Master Thesis Proposal Exact Inference for Probabilistic Loops

Due to the probabilistic and uncertain nature of numerous real-world processes such as financial markets, robotic environments and randomized algorithms, statistical modeling and machine learning are two widely used tools in various areas of applied engineering. A statistical model can be used twofold: (a) to model the data generation process and evaluate the probability of possible outcomes, and (b) to perform (Bayesian) inference on the model using observed output data and automatically learn/adapt the model parameters. Traditionally, such models have been created manually and evaluated by tailored mathematical approaches based on e.g., statistics, logic and algebra, which are tedious and require significant mathematical expertise. To overcome these limitations, Probabilistic Programming Languages (PPLs) allow the user to specify the probabilistic model in a designated programming language and provide means to run fully-automated inference procedures, significantly facilitating the deployment of such models [1, 2]. Moreover, probabilistic programming can be viewed as an attempt to provide machine learning procedures with domain knowledge by giving insight to how data is generated.
Formal Analysis of Session-Handling in Secure Messaging: Lifting Security from Sessions to Conversations

The building blocks for secure messaging apps, such as Signal’s X3DH and Double Ratchet (DR) protocols, have received a lot of attention from the research community. They have notably been proved to meet strong security properties even in the case of compromise such as Forward Secrecy (FS) and Post-Compromise Security (PCS). However, there is a lack of formal study of these properties at the application level. Whereas the research works have studied such properties in the context of a single ratcheting chain, a conversation between two persons in a messaging application can in fact be the result of merging multiple ratcheting chains. In this work, we initiate the formal analysis of secure messaging taking the session-handling layer into account, and apply our approach to Sesame, Signal’s session management. We first experimentally show practical scenarios in which PCS can be violated in Signal by a clone attacker, despite its use of the Double Ratchet. We identify how this is enabled by Signal’s session-handling layer. We then design a formal model of the session-handling layer of Signal that is tractable for automated verification with the Tamarin prover, and use this model to rediscover the PCS violation and propose two provably secure mechanisms to offer stronger guarantees.
Constraining flow and sediment transport intermittency in the geological past

Quantitative investigations of ancient rivers usually provide insights into either instantaneous or mean flow conditions. There is a critical gap between these time scales of investigation, which reflects the intermittency of flow and sediment transport, and closing this gap is crucial to fully explore the dynamics and evolution of ancient fluvial landscapes. Here, we combined fluvial stratigraphic data sets, flow and sediment transport models, and paleoclimate general circulation model (GCM) results to develop new methods to estimate intermittency in the geological past, specifically flow intermittency factors (Iw) and sediment transport intermittency factors (Is), and we show how they can be used to explore past hydrograph shapes. We illustrated these methods using the Upper Cretaceous Last Chance Ferron Sandstone in Utah, western United States. For sand-transporting flow conditions in Last Chance Ferron rivers, we estimated Iw values of 0.54−0.90, which imply that channel-forming conditions were sustained for the majority of the year, consistent with perennial systems in which relatively large discharges were sustained. In contrast, for gravel-transporting flow conditions, Iw values of 0.28−0.38 suggest that the largest formative flows may have occupied Last Chance Ferron rivers for nearly a third of the year, which could be explained by a monsoonal system in which high-magnitude discharge events were sustained, or a subtropical system in which high-magnitude discharge events had short durations but high frequencies. Meanwhile, Is values of 0.075−0.15 suggest that annual sediment budgets could have been transported in as little as 10 days and up to 2 months, if channel-forming conditions were sustained, and these values highlight that small changes in the duration of channel-forming conditions could significantly impact sediment budgets. Our results are consistent with independent facies- and proxy-based insights into Last Chance Ferron rivers, which point to a perennially wet system characterized by a monsoonal or subtropical discharge regime. Further, our results highlight new opportunities to use paleoclimate GCMs to constrain intermittency factors in the geological past. Going forward, paleoclimate GCMs will be particularly useful where the rock record is incomplete or inaccessible, or where stratigraphic approaches are limited, and they will enable us to tackle pertinent research questions pertaining to past surface processes on both Earth and other planets.
The Discriminating Power of Items That Measure More Than One Dimension

Determining a correct response to many test items frequently requires more than one ability. This paper describes the characteristics of items of this type by proposing generalizations of the item response theory concepts of discrimination and information. The conceptual framework for these statistics is presented, and the formulas for the statistics are derived for the multidimensional extension of the two-parameter logistic model. Use of the statistics is demonstrated for a form of the ACT Mathematics Usage Test.
Geometric transforms for fast geometric algorithms

Many computational problems are inherently geometrical in nature. For example, cluster analysis involves construction of convex hulls of sets of points, LSI artwork analysis requires a test for intersection of sets of line segments, computer graphics involves hidden line elimination, and even linear programming can be expressed in terms of intersection of half-spaces. As larger geometric problems are solved on the computer, the need grows for faster algorithms to solve them. The topic of this thesis is the use of geometric transforms as algorithmic tools for constructing fast geometric algorithms. We describe several geometric problems whose solutions illustrate the use of geometric transforms. These include fast algorithms for intersecting half-spaces, constructing Voronoi diagrams, and computing the Euclidean diameter of a set of points. For each of the major transforms we include a set of heuristics to enable the reader to use geometric transforms to solve his own problems.
Window Based Monitoring: Packet Drop Detection in the Network Data Plane

Detecting network anomalies, such as packet loss, is becoming an increasingly important task to network operators, as applications are becoming more and more performance sensitive. Several solutions aim to detect these events as soon as they occur, as well as to disclose where they are taking place. Unfortunately, some of them incur in unacceptable overhead while others have to sacrifice coverage in order to cope with the increasing traffic intensity. Software Defined Networks and the Programmable Data Plane are relatively recent technologies that allow network operators to configure how switches process packets, which opens the door to efficient monitoring solutions. In this thesis, we develop WB-Mon, a passive solution that leverages the Data Plane pro-grammability to perform an inter-switch coordination algo-rithm that detects packet drops in arbitrary paths at line speed. Additionally, we employ a Failure Inference Algorithm (Net-Bouncer [9]) to enable localizing the links responsible for packet drops. Our evaluation shows that WBMon is able to detect every packet drop in less than 2ms, which allows to detect short-lived failures.
Solving Graph Flow Problems with Neural Networks: A Lagrangian Duality Approach

The problem of computing minimum cost graph flows has important applications in the management of transportation systems. Existing approaches to solving this problem, however, have high computational complexity and cannot scale to larger graphs. This research presents an entirely new approach to solving the minimum cost flow problem when the cost function is convex. The proposed technique uses message-passing graph neural networks to learn graph flows. This work addresses the question: can graph neural networks compute minimum cost flows faster than existing techniques? The goal of learning minimum cost flows fits within the larger context of using graph neural networks to solve algorithmic problems. Training a neural network to learn minimum cost graph flows is challenging for two reasons. Firstly, the model must output arc flows which constitute a valid circulation. Secondly, ground-truth labels are difficult to compute because existing algorithms are inefficient. These problems are solved by introducing a powerful reparameterization. Instead of directly learning flow values, the neural network learns relative flow proportions. This formulation allows the neural network to always produce valid flows. This maintenance of feasibility gives rise to a novel training technique based on Lagrangian duality. This method, named DualityLoss, uses separate neural networks for the primal and dual optimization problems. The models are then jointly trained with the goal of minimizing the estimated duality gap. Given a strongly dual problem and neural networks which maintain feasibility, this objective encourages the primal and dual models to produce optimal solutions. This training technique uses properties of Lagrangian duality to remove the need for labeled datasets. This novel approach is used to train a graph neural network on the task of minimum cost graph flows. The graph neural network features an architecture which is more compatible with high-diameter road graphs. This architecture uses ideas of graph attention, gated state updates, and neighborhood expansion. The neural network approach is evaluated against pure optimization techniques on a set of real-world road graphs. These results show that the model learns flows which are close to the optimal cost. Furthermore, the neural network computes these flows significantly faster than the best optimization algorithm. Qualitative examples show how the model learns to coordinate the behavior of vertices within the graph. These results are encouraging because the neural network learns these abilities without ever receiving ground-truth information.
AUTONOMOUS ANOMALY DETECTION VIA UNSUPERVISED MACHINE LEARNING

Manual inspection of satellite telemetry data in search of anomalies is a time-consuming detection technique. Most multi-signal systems send back extensive data that a single person cannot easily monitor. Machine learning techniques that scan the data and flag anomalies autonomously are an attractive alternative. The autonomous anomaly detection problem can be divided into two sub-problems: regression analysis and a classification process. In the regression analysis, a machine learning model is trained to reconstruct a given signal, and the classification process categorizes the reconstruction error as anomalous or nominal. This paper studies the autonomous anomaly detection problem and proposes improvements to the regression and classification sub-problems. Regarding the regression analysis, it was found that including the physics of the target signal in the machine learning model yields a lower reconstruction error than a purely data-driven model. The classification approaches studied showed that cluster-based thresholding techniques accompanied by a pruning procedure can outperform non-parametric dynamic thresholds.
MOBILE VIDEO SYSTEM FOR SURVEILLANCE OF ROADWAY AND ROADSIDE ENVIRONMENT

algorithms of our proposed system. To reduce the size of data to be processed, we have used the methods for the selective perception of video information, the detection of moving objects, the parallel and conveyor processing of video information to accelerate its processing, as well as the methods and means of rapid release of informative patterns for searching and recognizing objects. Results. The concept and structure of the hardware and software of the mobile video surveillance system have been developed, the system has been protected by the patent of Ukraine for a utility model; a new type camera for shooting and analyzing video data has been designed and created (in cooperation with a partner); the software components of the system for the automatic detection of license plates and their recognition and the automatic detection of people without a masks inside a public transport for the further data analysis have been developed. Conclusions. Using mobile systems of this type allows expanding video surveillance zone in cities and outsides and reducing costs of road video surveillance systems.
A full-parallax 3D display with restricted viewing zone tracking viewer's eye

The Three-Dimensional (3D) vision became widely known as familiar imaging technique now. The 3D display has been put into practical use in various fields, such as entertainment and medical fields. Development of 3D display technology will play an important role in a wide range of fields. There are various ways to the method of displaying 3D image. There is one of the methods that showing 3D image method to use the ray reproduction and we focused on it. This method needs many viewpoint images when achieve a full-parallax because this method display different viewpoint image depending on the viewpoint. We proposed to reduce wasteful rays by limiting projector’s ray emitted to around only viewer using a spinning mirror, and to increase effectiveness of display device to achieve a full-parallax 3D display. We propose a method by using a tracking viewer’s eye, a high-speed projector, a rotating mirror that tracking viewer (a spinning mirror), a concave mirror array having the different vertical slope arranged circumferentially (a concave mirror array), a cylindrical mirror. About proposed method in simulation, we confirmed the scanning range and the locus of the movement in the horizontal direction of the ray. In addition, we confirmed the switching of the viewpoints and convergence performance in the vertical direction of rays. Therefore, we confirmed that it is possible to realize a full-parallax.
Relationship between Solar Wind and Coronal Heating: Scaling Laws from Solar X-Rays

Pevtsov et al. recently showed that the luminosity of solar and stellar X-rays from closed magnetic structures scales nearly linearly with magnetic flux over 12 decades. We show here that the total power available to accelerate the solar wind also scales linearly with magnetic flux, provided that its sources inject a roughly constant energy per particle prior to losses from heat conducted by electrons into radiation. Using a recently developed model of the solar wind energy source and particle source, we calculate the available solar wind power and convert it into an equivalent X-ray luminosity to explore whether the same process that drives solar wind may also power coronal heating. The quantitative results agree remarkably well with the Pevtsov et al. X-ray observations and with GOES X-ray observations over almost two solar cycles from 1985 to 2004. The model for the solar wind energy and particle source relies on the continual reconfiguration of the supergranular network through the emergence of small bipolar or more complex closed magnetic fields. This naturally leads to an energy flux proportional to field strength on large-scale field structures with field strengths larger than the emerging flux. We conclude that the sources of energy for the solar wind and coronal heating are linked, likely through the emergence of new magnetic flux that continually reconfigures large-scale solar magnetic fields and powers and heats the corona.
ON THE CONSTANCY OF THE ELECTRON TEMPERATURE IN THE EXPANDING CORONA THROUGHOUT SOLAR CYCLE 23

A recent analysis of Fe emission lines observed during the total solar eclipses of 2006 March 29 and 2008 August 1 established the first empirical link between the electron temperature in the expanding corona and Fe charge states measured in interplanetary space. In this Letter, we use this link to infer this temperature throughout solar cycle 23 from in situ charge state measurements from the Solar Wind Ion Composition Spectrometer (SWICS) on the Advanced Composition Explorer (ACE) and on Ulysses. The distribution of the SWICS/ACE Fe charge states, which span cycle 23 from 1998 to 2009, is skewed with a peak centered on Fe8+, Fe9+, and Fe10+ and a tail spanning Fe12+ to Fe20+. An iterative process based on this distribution and on the Fe ion fraction as a function of electron temperature yields a narrow peak at 1.1 × 106 K. The tail in the measured charge state distribution is attributed to the sporadic release of material hotter than 2 × 106 K from closed magnetic structures within the bulges of streamers. The Fe Ulysses charge state measurements between 1992 and 1997 from cycle 22 peaked at Fe11+, indicative of a slightly higher temperature of 1.5 × 106 K. The relative constancy of the electron temperature in the expanding corona throughout solar cycle 23 points to the presence of an unknown mechanism regulating the energy input to electrons in the acceleration region of the solar wind at all latitudes during this cycle.