Miracle-dz/UniFAR-FTUs huggingface dataset:
Localizing Campus Shuttles from One Single Base Station Using LoRa Link Characteristics

Today more and more bus companies are providing real-time bus locations to their riders to improve passenger experience and increase ridership. Most of the existing bus localization systems rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS). However, it is costly to install GNSS receivers and retrofit existing buses to power them, which prevents them to be adopted by those bus operators with tight budgets. There has been increasing interest in developing GPS-free localization schemes that leverage the wireless signals transmitted by the buses to localize them. Such schemes often require the received signal strength (RSS) measured at multiple base stations and therefore are not applicable to a small transportation service with a single base station, such as the shuttle service for a university campus. This paper presents a novel approach that leverages the LoRa link characteristics measured by a single base station and deep learning to localize a campus shuttle when it approaches a stop. Experimental results show that our solution provides a detection accuracy of no less than 92.07% and significantly outperforms all baselines without requiring new hardware and introducing additional communication overhead.
Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning

Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such tasks are partially-observed; for example, taking a notebook out of a backpack will lead to visual occlusion and require reasoning over the history of audio or tactile information. While robust tactile sensing can be costly to capture on robots, microphones near or on a robot's gripper are a cheap and easy way to acquire audio feedback of contact events, which can be a surprisingly valuable data source for perception in the absence of vision. Motivated by the potential for sound to mitigate visual occlusion, we aim to learn a set of challenging partially-observed manipulation tasks from visual and audio inputs. Our proposed system learns these tasks by combining offline imitation learning from a modest number of tele-operated demonstrations and online finetuning using human provided interventions. In a set of simulated tasks, we find that our system benefits from using audio, and that by using online interventions we are able to improve the success rate of offline imitation learning by ~20%. Finally, we find that our system can complete a set of challenging, partially-observed tasks on a Franka Emika Panda robot, like extracting keys from a bag, with a 70% success rate, 50% higher than a policy that does not use audio.
Physics informed neural fields for smoke reconstruction with sparse data

High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a formidable challenge, due to the complexity of the underlying physics as well as the severe occlusion and complex lighting in the captured data. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting, and thus are unsuitable for real-world scenes with unknown lighting conditions or arbitrary obstacles. We present the first method to reconstruct dynamic fluid phenomena by leveraging the governing physics (ie, Navier -Stokes equations) in an end-to-end optimization from a mere set of sparse video frames without taking lighting conditions, geometry information, or boundary conditions as input. Our method provides a continuous spatio-temporal scene representation using neural networks as the ansatz of density and velocity solution functions for fluids as well as the radiance field for static objects. With a hybrid architecture that separates static and dynamic contents apart, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling. By augmenting time-varying neural radiance fields with physics-informed deep learning, our method benefits from the supervision of images and physical priors. Our progressively growing model with regularization further disentangles the density-color ambiguity in the radiance field, which allows for a more robust optimization from the given input of sparse views. A pretrained density-to-velocity fluid model is leveraged in addition as the data prior to avoid suboptimal velocity solutions which underestimate vorticity but trivially fulfill physical equations. Our method exhibits high-quality results with relaxed constraints and strong flexibility on a representative set of synthetic and real flow captures. Code and sample tests are at https://people.mpi-inf.mpg. de/~mchu/projects/PI-NeRF/.
Reactive power planning in distribution networks with distributed generation

Reactive power planning (RPP) is one of the most important task in operation and control of power systems. This paper presents a methodology, based on genetic algorithms (GA), for optimal placement of DG units in power networks to achieve both effects: to guarantee the voltage profile and to maximize the voltage loadability under normal and/or contingency conditions. The methodology aims in finding the configuration, among a set of system components, which meets the desired system reliability requirements taking into account stability limits. Results shown in the paper indicate that the proposed formulations can be used to determine which the best buses are where the addition of small distributed generator units can greatly enhance the voltage stability of the whole network and power transfer capability under contingencies. (7 pages)
Radiation Induced Lung Injury (RILI) and COVID-19 Pneumonia: Are There Similarities by The Radiation Oncologist’s Point of View?

Coronavirus disease 2019 (COVID-19), is a novel infectious disease responsible for a severe acute respiratory syndrome due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, which was firstly reported in January 2020 in Wuhan, Hubei Province, China, and then rapidly spread to other countries beyond China with a pandemic proportion in these last two years. In its severe lung expression, disease onset may result in death due to massive alveolar damage and progressive respiratory failure due to acute lung injury (ALI) which predisposes to fatal acute respiratory distress syndrome (ARDS). Autopsies reports describe a diffuse alveolar damage with vascular congestion, inflammatory cell population entrapped, thrombosis with hyaline membrane formation in the alveolar walls allowing to an irreversible lung destruction. Altogether these findings remind the radiation oncologists many similarities with radiation induced lung injury which is a well-known side effect of radiotherapy for lung cancer. A fascinating hypothesis is that a common inflammatory paradoxical response of lung could be underlying the injury whatever the causes may be, virus, drugs or ionizing radiation. In this paper we will review the similarities between these two illnesses just going through the mechanisms of ALI-ARDS and the role of the most important immune cells and cytokines involved.
Timing Attacks on Cryptographic Algorithms

Today’s modern processors have many innovations that have allowed computers to be faster. Although these changes in computer architecture have resulted in better efficiency and performance, a lot of private data that is processed by these devices can be leaked through hardware components such as branch predictors and caches. These components would be known as side-channels and leaking information through them would be known as a side-channel attack. A recent and notable group of attacks known as Spectre[5] affects many modern processors today due to their use of speculative execution. Side-channel attacks are dangerous because they could render theoretically secure cryptographic algorithms useless through leaked information. In this work, we highlight the threat to cryptographic code from Spectre style attacks and give an overview of defenses that can be deployed in hardware and software.
How and why to study collaboration at the level of economic ecosystems

In the context of discussions regarding the relevance of innovation to the task of building new economic models that foster sustainable development, this paper focuses on clarifying and specifying the term “ecosystem”, which is typically used as a metaphor. Taking into account research concerning biological ecosystems, the article describes the components, structures and dynamics that biological ecosystems share in common with business, entrepreneurial, and innovation ecosystems, which together form one aspect of economic ecosystems as a whole. The paper utilizes primary data that were collected through a mixed methodology involving participatory workshops and an online survey instrument that involved members of innovation-oriented entrepreneurial ecosystems in eight cities throughout Europe and Latin America from June 2019 through February 2020. Drawing on complex system theory as a unifying approach to describe and explain the components and structural conditions of any ecosystem, whether biological or economic, this paper proposes a theoretical approach and metrics that can be used to attain a better understanding of the social dynamics of ecosystems. Based on observations from the field of biology, it is proposed that such structural conditions tend toward equilibrium when they are constructed mainly through collaborative mechanisms. The results are shown graphically based on the data collected, utilizing metrics taken from complex network analysis and mathematical modeling from the perspective of complex system theory. This paper finds that the ecosystemic approach is more than a metaphor and can functionally describe how an ecosystem is structured and how it works by opening a wider path toward comprehending the dynamics underlying the interactions among components of economic ecosystems and their environment. The paper concludes by proposing that collaboration relationships among actors provide the required characteristics to increase balance and resilience in economic ecosystems.
ASSESSING THE EFFECTIVENESS OF THE PORTUGUESE BORROWER-BASED MEASURE IN THE COVID-19 CONTEXT WORKING PAPERS 2021 10

Based on the macroeconomic projections from Banco de Portugal and using an integrated micro-macro model developed by Gross and Población (2017), this paper makes a ﬁrst attempt at gauging the impact of the Covid-19 pandemic on Portuguese households and banks. To this end, we examine how the borrower-based measure, which has been put into place in 2018, might have been successful in dampening the negative economic eﬀects of the pandemic on households’ debt-servicing capacities and thereby onto the banking system. We ﬁnd that the borrower-based measure, deﬁned as an LTV ratio cap of 90%, a shocked DSTI ratio cap of 50%, and a maturity cap for mortgage loans of 40 years, leads to (i) a reduction in households’ loss rate (LR), caused by both a decrease in households’ probability of default (PD) and loss given default (LGD), and (ii) an increase in the capital ratio of the banking system, compared with a scenario where these limits are not in place. We also ﬁnd positive eﬀects of introducing a shocked DSTI ratio cap, calculated according to the Portuguese borrower-based measure, as it further (i) decreases the risk parameters of the borrowers and (ii) increases the capital ratio of banks. JEL: G01, G51, G21
G EOMETRICALLY G UIDED S ALIENCY M APS

Interpretability methods for deep neural networks mainly focus on modifying the rules of automatic differentiation or perturbing the input and observing the score drop to determine the most relevant features. Among them, gradient-based attribution methods, such as saliency maps, are arguably the most popular. Still, the produced saliency maps may often lack intelligibility. We address this problem based on recent discoveries in geometric properties of deep neural networks’ loss landscape that reveal the existence of a multiplicity of local minima in the vicinity of a trained model’s loss surface. We introduce two methods that leverage the geometry of the loss landscape to improve interpretability: 1) ”Geometrically Guided Integrated Gradients”, applying gradient ascent to each interpolation point of the linear path as a guide. 2) ”Geometric Ensemble Gradients”, generating ensemble saliency maps by sampling proximal iso-loss models. Compared to vanilla and integrated gradients, these methods significantly improve saliency maps in quantitative and visual terms. We verify our findings on MNIST and Imagenet datasets across convolutional, ResNet, and Inception V3 architectures
Learning Outside the Brain: Integrating Cognitive Science and Systems Biology

Learning is commonplace in organisms such as ourselves and even in organisms as far distant as the bee and the octopus. Such learning is implemented by brains, or neuronal networks, and has been extensively studied within ethology, psychology, cognitive science, and neuroscience. Whether learning also takes place in nonneuronal settings has remained a matter of sustained controversy, too often dominated by ideological views. In this survey, I will explain how learning can be rigorously interpreted as a form of information processing and then explore the evidence for whether learning also takes place in organismal contexts outside the brain, such as physiology, development, and individual cells. I will try to explain why it is important to build bridges in this way between cognitive science and systems biology, why concepts and methods from various branches of engineering may be helpful in this task, and what the eventual impact may be on how we think about the organism.
Evaluating and Improving Neural Program-Smoothing-based Fuzzing

Fuzzing nowadays has been commonly modeled as an optimization problem, e.g., maximizing code coverage under a given time budget via typical search-based solutions such as evolutionary algorithms. However, such solutions are widely argued to cause inefficient computing resource usage, i.e., inefficient mutations. To address this issue, two neural program-smoothing-based fuzzers, Neuzz and MTFuzz, have been recently proposed to approximate program branching behaviors via neural network models, which input byte sequences of a seed and output vectors representing program branching behaviors. Moreover, assuming that mutating the bytes with larger gradients can better explore branching behaviors, they develop strategies to mutate such bytes for generating new seeds as test cases. Meanwhile, although they have been shown to be effective in the original papers, they were only evaluated upon a limited dataset. In addition, it is still unclear how their key technical components and whether other factors can impact fuzzing performance. To further investigate neural program-smoothing-based fuzzing, we first construct a large-scale benchmark suite with a total of 28 popular open-source projects. Then, we extensively evaluate Neuzz and MTFuzz on such benchmarks. The evaluation results suggest that their edge coverage performance can be unstable. Moreover, neither neural network models nor mutation strategies can be consistently effective, and the power of their gradient-guidance mechanisms have been compromised. Inspired by such findings, we propose a simplistic technique, PreFuzz, which improves neural program-smoothing-based fuzzers with a resource-efficient edge selection mechanism to enhance their gradient guidance and a probabilistic byte selection mechanism to further boost mutation effectiveness. Our evaluation results indicate that PreFuzz can significantly increase the edge coverage of Neuzz/MTFuzz, and also reveal multiple practical guidelines to advance future research on neural program-smoothing-based fuzzing.
L EARNING TO W ALK OVER R ELATIONAL G RAPHS OF S OURCE C ODE

Information-rich relational have great potential in designing effective representations of code for program-understanding tasks. However, the wealth of structural and semantic information in such graphs can overwhelm models, because of their limited input size. A promising approach for overcoming this challenge is to gather presumed-relevant but smaller context from a larger graph, and random walks over graphs was one of the first such approaches discovered. We propose a deep-learning approach that improves upon random walks by learning task-specific walk policies that guide the traversal of the graph towards the most relevant context. In the setting of relational graphs representing programs and their semantic properties, we observe that models that employ learned policies for guiding walks are 6-36% points more accurate than models that employ uniform random walks, and 0.2-3.5% points more accurate than models that employ expert knowledge for guiding the walks.
Poster: ART-assisted Android App Difﬁng

—Android aims to provide a secure and feature-rich, yet resource-saving platform for its applications (apps). To achieve these goals, the compilation to distributable packages shrinks, obfuscates, and optimizes the code by default. As an additional optimization, the Android Runtime (ART) nowadays compiles the app’s bytecode to native code on the device instead of executing it in the Dalvik VM. We study the effects of these changes in the Android build and runtime environment on the problem of calculating app similarity. We compare existing bytecode-based tools to our novel approach of using the re-compiled (and optimized) binary form. We propose OAT MEAL , an extensible framework to generate reliable ground truth for evaluating app similarity approaches and provide a benchmark dataset to the community. We built this dataset from open-source apps available on F-Droid in various conﬁgurations that optimize and obfuscate the bytecode. Using this dataset, we show the limitations of existing Android-speciﬁc bytecode analysis approaches when faced with the new optimizing R8 bytecode compiler. We further demonstrate how well BinDiff, a state-of-the-art binary-based alternative, works in scoring the similarity of apps. With OAT MEAL , we provide the foundation for integrating and benchmarking further approaches, both for calculating the similarity between apps (based on bytecode or binary code), and for evaluating their robustness to evolving optimization and obfuscation techniques.
Optimal reactive power planning for distribution systems with nonlinear loads

The objective of this paper is to derive a systematic algorithm to decide the optimal location and size of shunt capacitors and filters for distribution systems with harmonic distortion. In this paper, the problem of reactive power compensation is first formulated as a nonlinear programming of minimization of real power loss, energy loss and capacitor cost under voltage constraints. The nonlinear programming problem is solved by the MINOS package to determine the optimal locations and sizes of shunt capacitors. The harmonic load flow is then applied to solve the total voltage harmonic distortion factor and the filter design is executed to solve the harmonic distortion and reactive power compensation simultaneously. To demonstrate the performance and effectiveness of the proposed methodology, a sample distribution feeder has been selected for computer simulation. <<ETX>>
ANR TECAP Deliverable D2.5: def. of encodings for prover communication From CryptoVerif to EasyCrypt

This document presents an encoding from CryptoVerif to EasyCrypt. This encoding deals with the language in which security assumptions on cryptographic primitives are expressed in CryptoVerif: CryptoVerif typically has more difficulties proving primitives than protocols, so our goal is to delegate to EasyCrypt the proof of security assumptions on cryptographic primitives that CryptoVerif is unable to perform.
Distributional health impacts of electricity imports in the United States

The electric grid is evolving rapidly in response to climate change. As renewables are incorporated, more interconnection of the grid is expected. Exposure to fine particulate matter (PM2.5) from fossil-fuel generation causes adverse health impacts, including thousands of premature deaths each year in the United States. It is well understood that PM2.5 exposure can occur at great distances from pollutant sources, but insufficient work has been done to understand the role of grid interconnection and trade in causing pollution-related mortality. Regions with clean generation can import electricity from regions with highly polluting generation sources, allowing them to benefit from the electricity consumption while people in other regions suffer the associated health damages. We use flow tracing and consumption-based accounting to characterize the health damages from exposure to PM2.5 from electricity imports. We find that 8% of our estimated premature deaths from electricity consumption in the United States are due to electricity imports. There is large geographic heterogeneity, with the most impacts occurring in the Midwest. While the West Coast has much cleaner generation and lower impacts overall, in many West Coast Balancing Areas, more than 50% of the estimated premature mortality associated with electricity consumption is caused by electricity imports, with some groups experiencing larger impacts than others.
Mapping out the Stellar Populations of IC 2602 and IC 2391

IC 2391 and IC 2602 are important benchmarks for testing early star and planet evolution theories, both structural and dynamical, because they are the nearest open clusters with ages of ∼50 Myr. We refine membership lists for these clusters by identifying candidate members using Gaia DR2 kinematic and distance information. We identify 451 candidate members of IC 2602 and 350 candidate members of IC 2391. If confirmed, this would increase the known populations of these clusters by 275% and 130%, respectively. We use CHIRON on the CTIO/SMARTS 1.5 m telescope via fiber mode which yields a resolution of 27,400 to acquire high-resolution spectra of 26 new candidate cluster members brighter than G = 13 magnitude, as well as an additional 12 previously known members. Measures of lithium, Hα, stellar properties (T eff, log(g), [Fe/H]), radial velocities, and vsini values from these spectra are used to confirm cluster membership. We find that 37 of 38 stars we observe are bona fide cluster members, of which four are new candidate photometric binaries and 10 are new candidate spectroscopic binaries.