Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction

Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist clinical decision making. Recurrent neural network (RNN) and point process models are usually employed in modelling temporal sequential data. Simple RNN models assume that sequences of hospital visits follow strict causal dependencies between consecutive visits. However, in the real-world, a patient may have multiple co-existing chronic medical conditions, i.e., multimorbidity, which results in a cascade of visits where a non-immediate historical visit can be most influential to the next visit. Although a point process (e.g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption. We propose a novel model, MEDCAS, to address these challenges. MEDCAS combines the strengths of RNN-based models and point processes by integrating point processes in modelling visit types and time gaps into an attention-based sequence-to-sequence learning model, which is able to capture the temporal cascade relationships. To supplement the patients with short visit sequences, a structural modelling technique with graph-based methods is used to construct the markers of the point process in MEDCAS. Extensive experiments on three real-world EHR datasets have been performed and the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art models in both tasks.
Online Sketch-based Query Optimization

Cost-based query optimization remains a critical task in relational databases even after decades of research and industrial development. Query optimizers rely on a large range of statistical synopses -- including attribute-level histograms and table-level samples -- for accurate cardinality estimation. As the complexity of selection predicates and the number of join predicates increase, two problems arise. First, statistics cannot be incrementally composed to effectively estimate the cost of the sub-plans generated in plan enumeration. Second, small errors are propagated exponentially through join operators, which can lead to severely sub-optimal plans. In this paper, we introduce COMPASS, a novel query optimization paradigm for in-memory databases based on a single type of statistics -- Fast-AGMS sketches. In COMPASS, query optimization and execution are intertwined. Selection predicates and sketch updates are pushed-down and evaluated online during query optimization. This allows Fast-AGMS sketches to be computed only over the relevant tuples -- which enhances cardinality estimation accuracy. Plan enumeration is performed over the query join graph by incrementally composing attribute-level sketches -- not by building a separate sketch for every sub-plan. We prototype COMPASS in MapD -- an open-source parallel database -- and perform extensive experiments over the complete JOB benchmark. The results prove that COMPASS generates better execution plans -- both in terms of cardinality and runtime -- compared to four other database systems. Overall, COMPASS achieves a speedup ranging from 1.35X to 11.28X in cumulative query execution time over the considered competitors.
The formation history of the Milky Way disc with high-resolution cosmological simulations

We analyse from an observational perspective the formation history and kinematics of a Milky Way-like galaxy from a high-resolution zoom-in cosmological simulation that we compare to those of our Galaxy as seen by Gaia DR2 to better understand the origin and evolution of the Galactic thin and thick discs. The cosmological simulation was carried out with the GADGET-3 TreePM+SPH code using the MUlti Phase Particle Integrator (MUPPI) model. We disentangle the complex overlapping of stellar generations that rises from the top-down and inside-out formation of the galactic disc. We investigate cosmological signatures in the phase-space of mono-age populations and highlight features stemming from past and recent dynamical perturbations. In the simulation, we identify a satellite with a stellar mass of 1.2 · 109 M , i.e. stellar mass ratio Δ ∼ 5.5 per cent at the time, accreted at z ∼ 1.6, which resembles the major merger Gaia-Sausage-Enceladus that produced the Galactic thick disc, i.e. Δ ∼ 6 per cent. We found at z ∼ 0.5 – 0.4 two merging satellites with a stellar mass of 8.8 · 108 M and 5.1 · 108 M that are associated to a strong starburst in the Star Formation History, which appears fairly similar to that recently found in the Solar Neighbourhood. Our findings highlight that detailed studies of coeval stellar populations kinematics, which are made available by current and future Gaia data releases and in synergy with simulations, are fundamental to unravel the formation and evolution of the Milky Way discs.
Conformal Methods in General Relativity with application to Conformal Cyclic Cosmology: A minicourse given at the IXth IMLG Warsaw 2018

In these lectures my aim is to review enough of conformal differential geometry in four dimensions to give an account of Penrose’s conformal cyclic cosmology. 1 Tensor calculus and conformal rescaling My approach will be fairly concrete. For more abstract approaches see [9] or [12]. 1.1 Differential geometry conventions These largely follow [52]: we have a metric gab with signature (+ − −−) and will use abstract indices where possible. The Christoffel symbols, which need concrete indices, here written as bold, are Γabc = 1 2 gad(gdb,c + gdc,b − gbc,d), (1) and the metric or Levi-Civita covariant derivative in concrete indices is ∇aX = ∂ ∂x X + ΓbacX . The Riemann tensor is defined in abstract indices by (∇a∇b −∇b∇a)X = R c abd X. (2)
Provably End-to-end Label-Noise Learning without Anchor Points

In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we propose an end-to-end framework for solving label-noise learning without anchor points, in which we simultaneously optimize two objectives: the cross entropy loss between the noisy label and the predicted probability by the neural network, and the volume of the simplex formed by the columns of the transition matrix. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered. This is by far the mildest assumption under which the transition matrix is provably identifiable and the learned classifier is statistically consistent. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of the proposed method.
Edge computing: A systematic mapping study

Edge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three‐tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.
Neural Recursive Belief States in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning, the problem of learning to act is particularly difficult because the policies of co-players may be heavily conditioned on information only observed by them. On the other hand, humans readily form beliefs about the knowledge possessed by their peers and leverage beliefs to inform decision-making. Such abilities underlie individual success in a wide range of Markov games, from bluffing in Poker to conditional cooperation in the Prisoner's Dilemma, to convention-building in Bridge. Classical methods are usually not applicable to complex domains due to the intractable nature of hierarchical beliefs (i.e. beliefs of other agents' beliefs). We propose a scalable method to approximate these belief structures using recursive deep generative models, and to use the belief models to obtain representations useful to acting in complex tasks. Our agents trained with belief models outperform model-free baselines with equivalent representational capacity using common training paradigms. We also show that higher-order belief models outperform agents with lower-order models.
Transient heat transfer of superfluid $^4$He in nonhomogeneous geometries -- Part I: Second sound, rarefaction, and thermal layer

Transient heat transfer in superfluid He (He II) is a complex process that involves the interplay of the unique counterflow heattransfer mode, the emission of second-sound waves, and the creation of quantized vortices. Many past researches focused on homogeneous heat transfer of He II in a uniform channel driven by a planar heater. In this paper, we report our systematic study of He II transient heat transfer in nonhomogeneous geometries that are pertinent to emergent applications. By solving the He II two-fluid equation of motion coupled with the Vinen’s equation for vortex-density evolution, we examine and compare the characteristics of transient heat transfer from planar, cylindrical, and spherical heaters in He II. Our results show that as the heater turns on, an outgoing second-sound pulse emerges, in which the vortex density grows rapidly. These vortices attenuate the second sound and result in a heated He II layer in front of the heater, i.e., the thermal layer. In the planar case where the vortices are created throughout the space, the second-sound pulse is continuously attenuated, leading to a strong thermal layer that diffusively spreads following the heat pulse. On the contrary, in the cylindrical and the spherical heater cases, vortices are created mainly in a thin thermal layer near the heater surface. As the heat pulse ends, a rarefaction tail develops following the second-sound pulse, in which the temperature drops. This rarefaction tail can promptly suppress the thermal layer and take away all the thermal energy deposited in it. The effects of the heater size, heat flux, pulse duration, and temperature on the thermal-layer dynamics are discussed. We also show how the peak heat flux for the onset of boiling in He II can be studied in our model.
Entanglement cost for steering assemblages

In this paper we answer the question of how one can quantify the amount of entanglement necessary to create a given steering assemblage, when the original state and measurements are unknown. To do this, we extend the concepts of entanglement cost and entanglement of formation to steering assemblages, and provide easy to calculate upper and lower bounds. We prove that the entanglement of formation for assemblages is not generally continuous and not a flat roof extension (in contrast to the state-based quantity); and use numerical analysis to illustrate these properties. Finally, we discuss the consequences of these results for assemblage-to-assemblage conversion and suggest potential applications.
One Size Does Not Fit All: Finding the Optimal N-gram Sizes for FastText Models across Languages

Unsupervised word representation learning from large corpora is badly needed for downstream tasks such as text classification, information retrieval, and machine translation. The representation precision of the fastText language models is mostly due to their use of subword information. In previous work, the optimization of fastText subword sizes has been largely neglected, and non-English fastText language models were trained using subword sizes optimized for English and German. In our work, we train English, German, Czech, and Italian fastText language models on Wikipedia, and we optimize the subword sizes on the English, German, Czech, and Italian word analogy tasks. We show that the optimization of subword sizes results in a 5% improvement on the Czech word analogy task. We also show that computationally expensive hyperparameter optimization can be replaced with cheap n-gram frequency analysis: subword sizes that are the closest to covering 3.76% of all unique subwords in a language are shown to be the optimal fastText hyperparameters on the English, German, Czech, and Italian word analogy tasks.
FuzzSplore: Visualizing Feedback-Driven Fuzzing Techniques

Fuzz Testing techniques are the state of the art in software testing for security issues nowadays. Their great effectiveness attracted the attention of researchers and hackers and involved them in developing a lot of new techniques to improve Fuzz Testing. The evaluation and the cross-comparison of these techniques is an almost open problem. In this paper, we propose a human-driven approach to this problem based on information visualization. We developed a prototype upon the AFL++ fuzzing framework, FuzzSplore, that an analyst can use to get useful insights about different fuzzing configurations applied to a specific target in order to choose or tune the best technique during a fuzzing campaign.
ProxyFAUG: Proximity-based Fingerprint Augmentation

The proliferation of data-demanding machine learning methods has brought to light the necessity for methodologies which can enlarge the size of training datasets, with simple, rule-based methods. In-line with this concept, the fingerprint augmentation scheme proposed in this work aims to augment fingerprint datasets which are used to train positioning models. The proposed method utilizes fingerprints which are recorded in spacial proximity, in order to perform fingerprint augmentation, creating new fingerprints which combine the features of the original ones. The proposed method of composing the new, augmented fingerprints is inspired by the crossover and mutation operators of genetic algorithms. The ProxyFAUG method aims to improve the achievable positioning accuracy of fingerprint datasets, by introducing a rule-based, stochastic, proximity-based method of fingerprint augmentation. The performance of ProxyFAUG is evaluated in an outdoor Sigfox setting using a public dataset. The best performing published positioning method on this dataset is improved by 40% in terms of median error and 6% in terms of mean error, with the use of the augmented dataset. The analysis of the results indicates a systematic and significant performance improvement at the lower error quartiles, as indicated by the impressive improvement of the median error.
The Wisdom of the Crowd and Higher-Order Beliefs

The classic wisdom-of-the-crowd problem asks how a principal can "aggregate" information about an unknown state of the world from agents without understanding the information structure among them. Such aggregation obviously has large social and private value, especially when it concerns important social or economic events. Therefore, it is important to understand the limits of such an exercise: without specific assumptions on the information agents have, how can we aggregate it? Classic results by Prelec et al. [2017] (henceforth PSM) and Arieli et al. [2017] show that that even when agents' signals are i.i. d. conditional on the state, knowing the first-order beliefs of even an infinite set of agents is generally not sufficient to learn the state. In the terminology of econometrics, there is an "identification problem. "
Multifunctional bioerodible/biodegradable polymeric materials

As a part of our continuing interest in the preparation of multifunctional polymeric materials and evaluation of their potential in biomedical and environmental field, a survey on new polymeric systems susceptible to bioerosion and/or biodegradation attack is presented. In particular attention has been focused on the preparation and characterization of the following classes of polymeric materials: 1) hydroxyl-containing polyesters; 2) functionalized cyclodextrins; 3) hydrophilic polymers containing carboxylic groups; 4) hydrogels for the release of phytodrugs; 5) hybrid polymeric materials; 6) synthetic and semisynthetic polyfunctional polymeric materials for the removal of uremic toxins. A few examples of the use of these polymers in the controlled release of drugs and as coadjuvants in the chronic renal failure treatment will be also presented.
Simple and Convenient Synthesis of 4‐Unsubstituted‐3‐(3‐Oxoalkyl)isocoumarins

Abstract A simple transformation of 2‐alkylfurans and 2‐formylbenzoic acids into 4‐unsubstituted 3‐(3‐oxoalkyl)isocoumarins is described. It is based on the synthesis of 2‐(2‐carboxybenzyl)furans followed by their acid‐catalyzed recyclization to the target isocoumarins.
Nanostructured electrode materials for electrochemical energy storage and conversion

Manipulation of matter at the nanoscale is a way forward to move beyond our current choices in electrochemical energy storage and conversion technologies with promise of higher efficiency, environmental benignity, and cost‐effectiveness. Electrochemical processes being basically surface phenomena, tailored multifunctional nanoarchitecturing can lead to improvements in terms of electronic and ionic conductivities, diffusion and mass transport, and electron transfer and electrocatalysis. The nanoscale is also a domain in which queer properties surface: those associated with conversion electrodes, ceramic particles enhancing the conductivity of polymer electrolytes, and transition metal oxide powders catalyzing fuel cell reactions, to cite a few. Although this review attempts to present a bird's eye view of the vast literature that has accumulated in this rather infant field, it also lists a few representative studies that establish the beneficial effects of going ‘nano’. Investigations on nanostructuring and use of nanoparticles and nanoarchitectures related to lithium‐ion batteries (active materials and electrolytes), supercapacitors (electrical double‐layer capacitors, supercapacitors based on pseudo‐capacitance, and hybrid supercapacitors), and fuel cells (electrocatalysts, membranes and hydrogen storage materials) are highlighted.
The Protective Effect of Anthocyanins Extracted from Aronia Melanocarpa Berry in Renal Ischemia-Reperfusion Injury in Mice

Background Our previous research showed the antioxidant activity of anthocyanins extracted from Aronia melanocarpa of black chokeberry in vitro. Ischemia acute kidney injury is a significant risk in developing progressive and deterioration of renal function leading to clinic chronic kidney disease. There were many attempts to protect the kidney against this progression of renal damage. Current study was designed to examine the effect of pretreatment with three anthocyanins named cyanidin-3-arabinoside, cyanidin-3-glucodise, and cyaniding-3-galactoside against acute ischemia-reperfusion injury in mouse kidney. Methods Acute renal injury model was initiated by 30 min clamping bilateral renal pedicle and followed by 24-hour reperfusion in C57Bl/6J mice. Four groups of mice were orally pretreated in 50 mg/g/12 h for two weeks with cyanidin-3-arabinoside, cyanidin-3-glucodise, and cyaniding-3-galactoside and anthocyanins (three-cyanidin mixture), respectively, sham-control group and the renal injury-untreated groups only with saline. Results The model resulted in renal dysfunction with high serum creatinine, blood urea nitrogen, and changes in proinflammatory cytokines (TNF-ɑ, IL-1β, IL-6, and MCP-1), renal oxidative stress (SOD, GSH, and CAT), lipid peroxidation (TBARS and MDA), and apoptosis (caspase-9). Pretreatment of two weeks resulted in different extent amelioration of renal dysfunction and tubular damage and suppression of proinflammatory cytokines, oxidative stress, lipid peroxidation, and apoptosis, thus suggesting that cyanidins are potentially effective in acute renal ischemia by the decrease of inflammation, oxidative stress, and lipid peroxidation, as well as apoptosis. Conclusion the current study provided the first attempt to investigate the role of anthocyanins purified from Aronia melanocarpa berry in amelioration of acute renal failure via antioxidant and cytoprotective effects.
The Dutch Auditory & Image Vocabulary Test (DAIVT): A New Dutch Receptive Vocabulary Test for Students

We introduce a new Dutch receptive vocabulary test, the Dutch auditory & image vocabulary test (DAIVT). The test is multiple choice and assesses vocabulary knowledge for spoken words. The measure has an online (available at https://tpsurvey.ugent.be/limesurvey315/index.php/923234? lang=nl) format, has free access, and allows easy data collection. The test was developed with the intent to enable testing for research purposes with university students. This paper describes the test construction. We cover three phases: 1) collecting stimulus materials and developing the test’s first version, 2) an exploratory item-analysis on the first draft (n = 93), and 3) validating the test (both the second and the final version) by comparing it to two existing tests (n = 270, n = 157). The results indicate that the test is reliable and correlates well with existing Dutch receptive vocabulary tests (convergent validity). The final version of the DAIVT comprises 90 test items and 1 practice item. It can be used freely for research purposes.
A theoretical study of the interactions of water with gallic acid and a PEO/TGG complex

PM3 Semi-empirical molecular orbital calculations on the interaction of water and gallic acid (GA) show the hydrophilic part of the solute (GA) to form strong directional bonds to 5 H2O molecules. To form a monolayer around GA, containing trigalloyl glucose both hydrophilic and hydrophobic regions, requires about 43 H2O molecules. PM3, MM and MD calculations on complexes between the hexamer of poly(ethylene oxide) (PEO)6 and cofactor containing trigalloyl glucose (TGG) show that (PEO)6/TGG complexes do not form in aqueous solution, which agrees with the experimental results in pure water, in the absence of salt, obtained with PEO and corilagin, a very similar molecule. It is possible that a (PEO) conformers, with the oxygens outside, are favoured in aqueous solution, because the H2O molecules can more easily hydrogen bond with the PEO ether oxygens. A distinct change in the rate of change of the relative total energy per H2O molecule occurs at 10 H2O molecules: beyond 10 H2O molecules a slow varying plateau is observed to and beyond 120 H2O molecules. This is critical when calculating the enthalpy of association of a solvated molecular complex.