Miracle-dz/UniFAR-FTUs huggingface dataset:
From puzzles to portraits: Enhancing situation awareness during natural disasters using a design science approach

During emergency situations, a quick and concise summary from the deluge of messages improves “Situation Awareness” and enables informed decisions. However, this is challenging due to the volume, variety and veracity of information. Grounded in the Situation Awareness Theory, this study presents a streamlined protocol to process millions of social media messages and render them in an easily comprehensible format for various stakeholders to gain deeper insights. Specifically, using a design science approach, we develop a set of artifacts using incremental hierarchical clustering and enhanced text summarization algorithm to produce informative summaries under crisis situations. We implemented our protocol on 2.5 and 3 million tweets collected during the two major hurricanes in 2017 and 2018. The results show that our protocol can derive critical information not captured by Twitter’s search tools and mainstream news media and significantly improve on accuracy and efficiency when compared to other contemporary tools.
Propagation of Monetary Policy through Production Networks*

We study the importance of production networks for the transmission of macroeconomic shocks using the stock market reaction to monetary policy shocks as a laboratory. We decompose the overall effect of monetary policy shocks into a direct effect and a network effect and attribute 55 to 85 percent of the overall effect to the network effect. Large network effects are a robust feature of the data, and we document similar patterns in realized cash-flow fundamentals. A simple model with intermediate inputs predicts that the reaction of stock returns to shocks follows a spatial autoregression, which we exploit for our empirical strategy. Our results suggest that production networks are an important mechanism for transmitting aggregate shocks to the real economy. JEL classification: E12, E31, E44, E52, G12, G14
High‐Field Losses of Adulterated Lead Zirconate‐Titanate Piezoelectric Ceramics

The high-field losses of adulterated lead zirconate-titanate ceramics were compared on the basis of rates of temperature rise of standard-size samples in the same transducer configuration. Systematic chemical modification in light of the ionic model of the perovskite structure was used to produce ceramics with widely varying high-field behavior. High-field losses could be greatly reduced by adulteration of either cation site with ions of lower than normal valency. At 1 at. % concentration, lowest losses were found with adulterants whose valence was one unit less than normal. The results are explained as stoichiometric effects. Normal air-fired lead zirconate-titanate ceramics are considered to be deficient in metal tons. Adulteration with subvalent cations reduces the iotal oxygen content of the ceramic and brings it toward true ABO3 stoichiometry. Lowest losses are postulated for truly stoichiometric bodies.
Sharp asymptotics for q-norms of random vectors in high-dimensional ℓpn-balls

Abstract. Sharp large deviation results of Bahadur–Ranga Rao type are provided for the q-norm of random vectors distributed on the lp -ball B n p according to the cone probability measure or the uniform distribution for 1 ≤ q < p < ∞, thereby furthering previous large deviation results by Kabluchko, Prochno and Thäle in the same setting. These results are then applied to deduce sharp asymptotics for intersection volumes of different lp -balls in the spirit of Schechtman and Schmuckenschläger, and for the length of the projection of an lp -ball onto a line with uniform random direction. The sharp large deviation results are proven by providing convenient probabilistic representations of the q-norms, employing local limit theorems to approximate their densities, and then using geometric results for asymptotic expansions of Laplace integrals to integrate these densities and derive concrete probability estimates.
Nitrogen and Phosphorus Recovery from Human Urine by Struvite Precipitation and Air Stripping in Vietnam

A “No Mix” sanitation system was installed in a dormitory at the University of CanTho in South Vietnam, with the objective of recycling nutrients from source separated wastewater streams. This paper presents the “Yellow Water” treatment plant and its efficiency in recovering phosphorus and nitrogen from human urine. The pilot plant achieved phosphorus removal efficiencies of 98% with both diluted and undiluted urine. Phosphorus was recovered in the form of struvite, a solid mineral fertilizer with heavy metal concentrations being below the German Fertilizer Regulation's threshold limits. About 110 g of struvite could be generated after one treatment cycle, during which 50 L of urine were processed. Nitrogen removal by air stripping showed best results when circulating the urine for 3 h through the stripping column at a high flow rate (80 L/h). With these settings, more than 90% of the nitrogen could be removed from the urine, and virtually 100% of this nitrogen could be recovered in the form of liquid ammonium sulfate. In the future, treatment costs could be further reduced by making use of the solar energy that is available during daytime in South Vietnam.
ompleteness of SoV Representation for SL(2,R) Spin Chains

This work develops a new method, based on the use of Gustafson’s integrals and on the evaluation of singular integrals, allowing one to establish the unitarity of the separation of variables transform for infinite-dimensional representations of rank one quantum integrable models. We examine in detail the case of the SL(2,R) spin chains.
Machine Unlearning via Algorithmic Stability

We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearning. For convex risk minimization problems, we design TV-stable algorithms based on noisy Stochastic Gradient Descent (SGD). Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure. To understand the trade-offs between accuracy and unlearning efficiency, we give upper and lower bounds on excess empirical and populations risk of TV stable algorithms for convex risk minimization. Our techniques generalize to arbitrary non-convex functions, and our algorithms are differentially private as well.
Second law of thermodynamics for quantum correlations

B. C¸ . is supported by the BAGEP Award of the Science Academy and by the Research Fund of Bah¸ce¸sehir University (BAUBAP) under project no: BAP.2019. 02.03. S.D. acknowledges support from the U.S. National Science Foundation under Grant No. DMR-2010127. This research was supported by grant number FQXi-RFP-1808 from the Foundational Questions Institute and Fetzer Franklin Fund, a donor advised fund of Silicon Valley Community Foundation (SD).
Effects of ionic charge on light scattering by macromolecules in solution

Starting with Stephen’s simplified theory, a detailed solution is derived and numerically evaluated for the spectrum of light scattered by a solution of charged macromolecules, in a form that shows explicitly the dependence on the ionic charge parameter. A scheme is outlined for assessing the nature and extent of the deviation of the Debye–Huckel predictions from experimental results.
Integrated Dataset of Brazilian Flights

The Brazilian commercial aviation system achieved the first position among Latin American countries and the fifteenth place worldwide on the Revenue Passenger-Kilometer ranking. The availability of flight information, including meteorological conditions, enables studies about the Brazilian flight system, such as flight delays and timetabling. Therefore, this paper contributes to such studies by offering an integrated dataset containing data on departure and arrival for flights departing and arriving at Brazilian airports comprising the period from 2000 to 2019. This paper presents a dataset composed of 15, 505, 922 records of flight data, each containing 45 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, scheduled and elapsed times for departure and arrival.
Tensors Fitting Perfectly

Multidimensional arrays (NDArrays) are a central abstraction in modern scientific computing environments. Unfortunately, they can make reasoning about programs harder as the number of different array shapes used in an execution of a program is usually very large, and they rarely appear explicitly in program text. To make things worse, many operators make implicit assumptions about the shapes of their inputs: array addition is commonly enriched with broadcasting semantics, while matrix multiplication assumes that the lengths of contracted dimensions are equal. Because precise reasoning about shapes is crucial to write correct programs using NDArrays, and because shapes are often hard to infer from a quick glance at the program, we developed Tensors Fitting Perfectly, a static analysis tool that reasons about NDArray shapes in Swift for TensorFlow programs by synthesizing a set of shape constraints from an abstract interpretation of the program. It can both (1) check for possible inconsistencies, and (2) provide direct insights about the shapes of intermediate values appearing in the program, including via a mechanism called shape holes. The static analysis works in concert with optional runtime assertions to improve the productivity of program authors.
Exploratory Analysis of Bioprocesses Using Artificial Neural Network‐Based Methods

A process data driven procedure has been developed that allows a universal time‐efficient bioprocess analysis. The procedure is particularly suited for industrial production processes which have not yet been comprehensively investigated. It makes use of artificial neural networks in combination with mass balance equations to represent the process dynamics on a commercial workstation. The essential concept behind the procedure is to start with the already available knowledge formulated by a very simple process representation which includes only those variables that are firmly known to be essential. Then, stepwise, additional variables are added to the basic representation after they passed a test procedure in which they proved to enhance the model's performance. The result of the procedure is a numerical representation of the important process relationships that immediately allows to determine improved set points and/or profiles for the manipulated variables with respect to process performance. It may be used to improve state estimation and control. The procedure has already been tested in industrial applications. In this paper, a validation of the procedure with simulated bioprocess data is presented.
Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification

Chest X-rays are an important and accessible clinical imaging tool for the detection of many thoracic diseases. Over the past decade, deep learning, with a focus on the convolutional neural network (CNN), has become the most powerful computer-aided diagnosis technology for improving disease identification performance. However, training an effective and robust deep CNN usually requires a large amount of data with high annotation quality. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. Thus, existing public chest X-ray datasets usually adopt language pattern based methods to automatically mine labels from reports. However, this results in label uncertainty and inconsistency. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods from two perspectives to improve a single model's disease identification performance, rather than focusing on an ensemble of models. MODL integrates multiple models to obtain a soft label distribution for optimizing the single target model, which can reduce the effects of original label uncertainty. Moreover, KNNS aims to enhance the robustness of the target model to provide consistent predictions on images with similar medical findings. Extensive experiments on the public NIH Chest X-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods.
Role of chemical disorder in tuning the Weyl points in vanadium doped 
Co2TiSn

The lack of time-reversal symmetry and Weyl fermions give exotic transport properties to Cobased Heusler alloys. In the present study, we have investigated the role of chemical disorder on the variation of Weyl points in Co2Ti 1-xVxSn magnetic Weyl semimetal candidate. We employ the first principle approach to track the evolution of the nodal lines responsible for the appearance of Weyl node in Co2TiSn as a function of V substitution in place of Ti. By increasing the V concentration in place of Ti, the nodal line moves toward Fermi level and remains at Fermi level around the middle composition. Further increase of the V content, leads shifting of nodal line away from Fermi level. Density of state calculation shows half-metallic behavior for the entire range of composition. The magnetic moment on each Co atom as a function of V concentration increases linearly up to x=0.4, and after that, it starts decreasing. We also investigated the evolution of the Weyl nodes and Fermi arcs with chemical doping. The first-principles calculations reveal that via replacing almost half of the Ti with V, the intrinsic anomalous Hall conductivity increased twice as compared to the undoped composition. Our results indicate that the composition close to the 50% V doped Co2TiSn, will be an ideal composition for the experimental investigation of Weyl physics.
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach

Smart homes are becoming increasingly popular as a result of advances in machine learning and cloud computing. Devices, such as smart thermostats and speakers, are now capable of learning from user feedback and adaptively adjust their settings to human preferences. Nonetheless, these devices might in turn impact human behavior. To investigate the potential impacts of smart homes on human behavior, we simulate a series of hierarchical-reinforcement learning-based human models capable of performing various activities—namely, setting temperature and humidity for thermal comfort inside a Q-Learning-based smart home model. We then investigate the possibility of the human models’ behaviors being altered as a result of the smart home and the human model adapting to one another. For our human model, the activities are based on hierarchical-reinforcement learning. This allows the human to learn how long it must continue a given activity and decide when to leave it. We then integrate our human model in the environment along with the smart home model and perform rigorous experiments considering various scenarios involving a model of a single human and models of two different humans with the smart home. Our experiments show that with the smart home, the human model can exhibit unexpected behaviors such as frequent changing of activities and an increase in the time required to modify the thermal preferences. With two human models, we interestingly observe that certain combinations of models result in normal behaviors, while other combinations exhibit the same unexpected behaviors as those observed from the single human experiment.
The Magic of Superposition: A Survey on Simultaneous Transmission Based Wireless Systems

In conventional communication systems, any interference between two communicating points is regarded as unwanted noise since it distorts the received signals. On the other hand, allowing simultaneous transmission and intentionally accepting the superposition of signals and even benefiting from it have been considered for a range of wireless applications. As prominent examples, non-orthogonal multiple access (NOMA), joint source-channel coding, and the computation codes are designed to exploit this scenario. They also inspired many other fundamental works from network coding to consensus algorithms. Especially, federated learning is an emerging technology that can be applied to distributed machine learning networks by allowing simultaneous transmission. Although various simultaneous transmission applications exist independently in the literature, their main contributions are all based on the same principle; the superposition property. In this survey, we aim to emphasize the connections between these studies and provide a guide for the readers on the wireless communication techniques that benefit from the superposition of signals. We classify the existing literature depending on their purpose and application area and present their contributions. The survey shows that simultaneous transmission can bring scalability, security, low-latency, low-complexity and energy efficiency for certain distributed wireless scenarios which are inevitable with the emerging wireless technologies.
Information algebras of coherent sets of gambles

In this paper, we show that coherent sets of gambles can be embedded into the algebraic structure of information algebra. This leads firstly, to a new perspective of the algebraic and logical structure of desirability and secondly, it connects desirability, hence imprecise probabilities, to other formalism in computer science sharing the same underlying structure. Both the domain free and the labeled view of the information algebra of coherent sets of gambles are presented, considering a special case of possibility space.
Deep learning can differentiate IDH-mutant from IDH-wild type GBM

1Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, Rome 00189, Italy 2Neuroradiology Unit, Radiology Department, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA 3Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, Piazza di Sant'Onofrio, 4, Rome 00165, Italy 4Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi 53, Rome 00144, Italy 5Department of Clinical and Molecular Medicine, Surgical Pathology Unit, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, Rome 00189, Italy * Authors contributed equally to this work.
Private and Utility Enhanced Recommendations With Local Differential Privacy and Gaussian Mixture Model

Recommendation systems rely heavily on behavioural and preferential data (e.g., ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users’ data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math><alternatives><mml:math><mml:mi>ɛ</mml:mi></mml:math><inline-graphic xlink:href="neera-ieq1-3126577.gif"/></alternatives></inline-formula>-LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester.
Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants

We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistant’s role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command “Q&As”.
Spurious Local Minima Are Common for Deep Neural Networks with Piecewise Linear Activations

In this article, theoretically, it is shown that spurious local minima are common for deep fully connected networks and average-pooling convolutional neural networks (CNNs) with piecewise linear activations and datasets that cannot be fit by linear models. Motivating examples are given to explain why spurious local minima exist: each output neuron of deep fully connected networks and CNNs with piecewise linear activations produces a continuous piecewise linear (CPWL) function, and different pieces of the CPWL output can optimally fit disjoint groups of data samples when minimizing the empirical risk. Fitting data samples with different CPWL functions usually results in different levels of empirical risk, leading to the prevalence of spurious local minima. The results are proved in general settings with arbitrary continuous loss functions and general piecewise linear activations. The main proof technique is to represent a CPWL function as maximization over minimization of linear pieces. Deep networks with piecewise linear activations are then constructed to produce these linear pieces and implement the maximization over minimization operation.
From atom to pedon: Linking processes to phenomena and function

The fundamental fact that the processes in soil cannot be observed directly demands inverse methods which explain the phenomena on the continuum scale with processes at the local scale in a mechanistic way. In this paper, an approach will be presented which links retardation, a continuum scale phenomena related to reactive transport, to the governing sorption processes. This approach builds upon effective models considering both the role of soil structure and the physicochemical properties of the interface. A multidomain sorption model has been developed accordingly and will be used to predict sorption and retardation of polycyclic aromatic hydrocarbons at the local and the pedon scale, respectively. The parameters and coefficient functions can be directly parameterized from local scale spectroscopic and spectromicroscopic information. An important additional back-up on the “atom scale” is provided by molecular modelling techniques which can be used to confirm the validity of the measured properties and interaction mechanisms.