Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Mobile OS Snooping By Samsung, Xiaomi, Huawei and Realme Handsets

The privacy of mobile apps has been extensively studied, but much less attention has been paid to the privacy of the mobile OS itself. A mobile OS may communicate with servers to check for updates, send telemetry and so on. We undertake an in-depth analysis of the data sent by six variants of the Android OS, namely those developed by Samsung, Xiaomi, Huawei, Realme, LineageOS and /e/OS. We find that even when minimally configured and the handset is idle these vendorcustomized Android variants transmit substantial amounts of information to the OS developer and also to third-parties (Google, Microsoft, LinkedIn, Facebook etc) that have pre-installed system apps. While occasional communication with OS servers is to be expected, the observed data transmission goes well beyond this and raises a number of privacy concerns. There is no opt out from this data collection.
MirChecker: Detecting Bugs in Rust Programs via Static Analysis

Safe system programming is often a crucial requirement due to its critical role in system software engineering. Conventional low-level programming languages such as C and assembly are efficient, but their inherent unsafe nature makes it undesirable for security-critical scenarios. Recently, Rust has become a promising alternative for safe system-level programming. While giving programmers fine-grained hardware control, its strong type system enforces many security properties including memory safety. However, Rust's security guarantee is not a silver bullet. Runtime crashes and memory-safety errors still harass Rust developers, causing damaging exploitable vulnerabilities, as reported by numerous studies. In this paper, we present and evaluate MirChecker, a fully automated bug detection framework for Rust programs by performing static analysis on Rust's Mid-level Intermediate Representation (MIR). Based on the observation of existing bugs found in Rust codebases, our approach keeps track of both numerical and symbolic information, detects potential runtime crashes and memory-safety errors by using constraint solving techniques, and outputs informative diagnostics to users. We evaluate MirChecker on both buggy code snippets extracted from existing Common Vulnerabilities and Exposures (CVE) and real-world Rust codebases. Our experiments show that MirChecker can detect all the issues in our code snippets, and is capable of performing bug finding in real-world scenarios, where it detected a total of 33 previously unknown bugs including 16 memory-safety issues from 12 Rust packages (crates) with an acceptable false-positive rate.
Can Distributed Intermittent Renewable Generation Reduce Future Grid Investments? Evidence from France

This paper estimates the relationship between investments in five distributed generation technologies and hourly net withdrawals from over 2,000 electricity distribution networks in France between 2005 and 2018. We find that investments in distributed wind and solar generation have little or no impact on the annual peak of hourly net withdrawals from the distribution grid, while investments in hydroelectric and thermal distributed generation significantly reduce it. An optimistic analysis of the impact of investments in battery storage suggests that high levels are required for distributed wind and solar to deliver similar reductions in the annual peak of hourly net withdrawals. Our results imply that public policies favoring distributed wind and solar generation over utility-scale generation cannot be rationalized by savings in future grid investments.
Age distributions for Markov processes in genetics Ffdv

The same relationship also makes it possible to deduce a mapping formula for any proposed distribution of the chiasmata. The probability of no chiasmata occurring in a segment is expressed in terms of a parameter which is related to the length of the segment. Both the recombination fraction and the average number of chiasmata are expressed in terms of this parameter, which is then eliminated from the expressions. If we assume that there is at least one chiasma in every chromosome arm, with the remaining chiasmata distributed at random, we obtain a simple mapping formula which gives positive co-incidence within the arms and no co-incidence across the centromere. It is also possible to derive mapping formulae allowing for chromatid interference. The recombination fraction depends simply on the relative excess of pairs of adjacent chiasmata that involve all four chromatids over those that involve only two. For any given level of chromatid interference the relationship between the distribution of the chiasmata along the chromosome and the mapping formula is many-one so that the second of my practical questions, that of the goodness of fit of the visible ‘chiasmata’ with the expected cross-overs is not so easy to answer, although it is the more useful question to pose.
Toward understanding the COVID-19 impact on data science innovation in Canada

Data science provides methods to understand and solve problems in an evidence-based manner by combining data and experience with scientific methods. When included with advances in robotic technologies, telecommunication technologies and internet coverage (digitally enabling infrastructure), and computer hardware, data science creates a host of “digital technologies” expected to bring value to business, government, and individuals. Recent adoption and development of these innovations have been affected by the pandemic, even as COVID-19 sped up the move to online activities from shopping to working at home. In this paper, we analyze trends and changes to understand the levels of disruption from COVID-19 on the data science innovation ecosystem. We consider three phases of innovation: early-stage research and development; late-stage research and development; and commercialization and diffusion. We show that the COVID-19 pandemic has had a negative impact on innovation in data science across the phases of innovation to varying degrees; however, our analysis suggests a return to previous growth may be expected in the short term should the economy continue to improve. These findings suggest that researchers and practitioners should be prepared to take advantage of this return in growth to invest in early stage R&D, to build research programs in data science, and to find ways to commercialize and adopt data science innovations. Our research also identifies the need for co-ordinated efforts to make current and up-to-date data for tracking innovation impacts available.
Aktuelle Aspekte der Lungentransplantation

Zusammenfassung Seit der ersten Lungentransplantation im Jahr 1963 ist diese Operation zu einer erfolgreichen und etablierten Therapie für Patienten mit Lungenversagen geworden. Obwohl die Sterblichkeitsraten im Vergleich zu anderen Organtransplantationen höher sind, wurden dank Verbesserungen in den Bereichen Technik, Organkonservierung, Immunsuppression und Antibiotika sowie diagnostischer Mittel und Therapie gegen Abstoßung bemerkenswerte Fortschritte erzielt.
Fallacies

Tip: Ask yourself what kind of “sample” you’re using: Are you relying on the opinions or experiences of just a few people, or your own experience in just a few situations? If so, consider whether you need more evidence, or perhaps a less sweeping conclusion. (Notice that in the example, the more modest conclusion “Some philosophy classes are hard for some students” would not be a hasty generalization.)
Considerations for the Control Design of Augmentative Robots

Robotic systems that are intended to augment human capabilities commonly require the use of semi-autonomous control and artificial sensing, while at the same time aiming to empower the user to make decisions and take actions. This work identifies principles and techniques from the literature that can help to resolve this apparent contradiction. It is postulated that augmentative robots must function as tools that have partial agency, as collaborative agents that provide conditional transparency, and ideally, serve as extensions of the human body.
Impact of Critical Influential Factors of Knowledge Management and Innovation on UAE Organization Performance

Although the role in information, growth and communication education is not well known, knowledge management in universities (KM). University programme theory, research and other priorities in advanced education remain relatively confined both inside and beyond the organisational sense. Knowledge must be created, manufactured and distributed. For supervisors, pupils and professors, this has immense consequences. KM's study of higher education institutions allows researchers and specialists to consider the main factors in (and probably external) academia for proper knowledge management results. The goal of this study is to recognize the effect on UAE University Success of Crucial Impact Factors for Information Management and Management. The findings could illustrate that a difficult relationship between variables and actions relevant to the information climate results in a proper application of KM activities and initiatives in the UAE. This study will increase the factors of creativity in knowledge management against the success of UAE public universities.
NCC ‐ TiO 2 ‐ APTMS ) particles and their application in superhydrophilic self ‐ cleaning coatings

Self-cleaning coatings are becoming an integral part of our daily life because of their utility in various applications. These coatings using nanoscale materials have received significant attention due to the importance of their potential. In the current research work, smart coating with self-cleaning, superhydrophilic application was prepared based on commercial grade water-based acrylic emulsion using nanocrystalline cellulose (NCC), nano-TiO2 (NT), and 3-aminopropyltrimethoxy silane (APTMS) composite in the current work. The sonication process achieved the adsorption of NT on NCC, which was further grafted with APTMS via hydrolysis. The primary confirmation of the chemical structures was done by FTIR spectroscopy, the determination of particle size was done by dynamic light scattering (DLS), morphology and elemental analysis by SEM–EDX, and crystallinity by X-ray diffraction (XRD) analysis. The coating was prepared by dispersing synthesized material in a commercial acrylic emulsion and characterized for mechanical, thermal, surface, and self-cleaning properties. The wettability of the as-prepared coating was analyzed by contact angle measurement. The coating with optimized formulation showed a water contact angle near 6° after photocatalytic activity, which confirms the excellent superhydrophilicity of the coating. Also, removing dust contaminants from the coating surface after water action indicates superior self-cleaning properties. * S. T. Mhaske stmhaske@gmail.com; st.mhaske@ictmumbai.edu. in Pavan Y. Borse pborse002@gmail.com Siddhesh U. Mestry siddhesh17mestry@gmail.com 1 Department of Polymer and Surface Engineering, Institute of Chemical Technology, Mumbai, India 9372 Polymer Bulletin (2022) 79:9371–9395 1 3 Graphical abstract
On incompressible flow simulations using Scott-Vogelius finite elements

Recent research has shown that in some practically relevant situations like multi-physics flows[11] divergence-free mixed finite elements may have a significantly smaller discretization error than standard non-divergence-free mixed finite elements. In order to judge the overall performance of divergence-free mixed finite elements, we investigate linear solvers for the saddle point linear systems arising in $((P_k)^d,P_{k-1}^{disc})$ Scott-Vogelius finite element implementations of the incompressible Navier-Stokes equations. We investigate both direct and iterative solver methods. Due to discontinuous pressure elements in the case of Scott-Vogelius elements, considerably more solver strategies seem to deliver promising results than in the case of standard mixed finite elements like Taylor-Hood elements. For direct methods, we extend recent preliminary work using sparse banded solvers on the penalty method formulation to finer meshes, and discuss extensions. For iterative methods, we test augmented Lagrangian and H-LU preconditioners with GMRES, on both full and statically condensed systems. Several numerical experiments are provided that show these classes of solvers are well suited for use with Scott-Vogelius elements, and could deliver an interesting overall performance in several applications.
Wave propagation in the biosonar organ of sperm whales using a finite difference time domain method

The bio-sonar of sperm whales presents many specific characteristics, such as its size, its loudness or its vocalization abilities. Furthermore it fulfills several roles in their foraging and social behaviour. However our knowledge about its operation remains limited to the main acoustic path that the emitted pulse may take. We still ignore the precise mechanisms that shape the wave and on which parts the sperm whale is able to act. In this paper, we describe a technique to simulate sperm whale click generation from a physical perspective. Such an approach aims at unveiling the processes involved in their vocal production, as a stepping stone towards a better understanding of their interaction with peers and the environment.
Superalgebras and the Multiplet Structure of the Genetic Code I : Codon Representations Michael Forger and

It has been proposed [1] that the degeneracy of the genetic code, i.e., the phenomenon that different codons (base triplets) of DNA are transcribed into the same amino acid, may be interpreted as the result of a symmetry breaking process. In ref. [1] this picture was developed in the framework of simple Lie algebras. Here, we explore the possibility of explaining the degeneracy of the genetic code using basic classical Lie superalgebras, whose representation theory is sufficiently well understood, at least as far as typical representations are concerned. In the present paper, we give the complete list of all typical codon representations (typical 64-dimensional irreducible representations), whereas in the second part, we shall present the corresponding branching rules and discuss which of them reproduce the multiplet structure of the genetic code. Work supported by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) and CNPq (Conselho Nacional de Desenvolvimento Cient́ıfico e Tecnológico), Brazil
RANGE MIGRATION ALGORITHM IN THE PROCESSING CHAIN OF SIGNALS OF A GROUND-BASED SAR SENSOR

Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) is used in the processing chain of a Ground-Based SAR (GB-SAR) sensor. The mentioned sensor has been developed in Microwave Remote Sensing Laboratory (MReSL) at the School of Surveying and Geospatial Engineering, the University of Tehran for the generation of a complex image from the raw signal. The raw signal is acquired with that sensor working at S-band, frequency modulating from 2.26 GHz to 2.59 GHz.
Improved Electrical Ideality and Photoresponse in Near Infrared Phototransistors Realized by Incorporating a Bulk Heterojunction Channel

Simultaneous increase in electrical ideality and near infrared (NIR) response in the organic phototransistors (OPTs) is realized by applying a donor/acceptor bulk heterojunction (BHJ) channel layer. A comprehensive analysis has been carried out to understand the effects of the miscibility between the donor and acceptor in the BHJ channel, and the morphology of the channel layer on the electrical charge transport and optical response of the OPTs. The presence of the acceptors in the channel layer helps to trap the undesirable injected electrons, avoiding the accumulation of the electrons at the active channel/dielectric interface, and thereby improving the hole transporting in the p-type channel. The electrical stability is then improved with mitigated dependence of charge mobility on gate voltage in the saturation region. The use of a BHJ channel in the OPTs also offers an improved photoresponse through enhanced exciton dissociation at the donor/acceptor interface and the stable mobility in the saturation region, leading to more than one order of magnitude increase in responsivity than that in a conventional OPT with a single p-type semiconductor channel layer. The OPTs with a BHJ channel layer exhibit an excellent hole mobility of 1.0 cm2V-1s-1, a high photoresponsivity of the 104 A/W and a high specific detectivity of 1.2×1012 Jones in the NIR wavelength.
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years. In this paper, we re-examine the challenges posed by distributed RL and try to view it through the lens of an old idea: distributed dataflow. We show that viewing RL as a dataflow problem leads to highly composable and performant implementations. We propose RLlib Flow, a hybrid actor-dataflow programming model for distributed RL, and validate its practicality by porting the full suite of algorithms in RLlib, a widely adopted distributed RL library. Concretely, RLlib Flow provides 2-9 code savings in real production code and enables the composition of multi-agent algorithms not possible by end users before. The open-source code is available as part of RLlib at https://github.com/ray-project/ray/tree/master/rllib.
Guided Policy Search for Parameterized Skills using Adverbs

We present a method for using adverb phrases to adjust skill parameters via learned adverb-skill groundings. These groundings allow an agent to use adverb feedback provided by a human to directly update a skill policy, in a manner similar to traditional local policy search methods. We show that our method can be used as a drop-in replacement for these policy search methods when dense reward from the environment is not available but human language feedback is. We demonstrate improved sample efficiency over modern policy search methods in two experiments.
On the Feasibility of Predicting Questions being Forgotten in Stack Overflow

For their attractiveness, comprehensiveness and dynamic coverage of relevant topics, community-based question answering sites such as Stack Overflow heavily rely on the engagement of their communities: Questions on new technologies, technology features as well as technology versions come up and have to be answered as technology evolves (and as community members gather experience with it). At the same time, other questions cease in importance over time, finally becoming irrelevant to users. Beyond filtering low-quality questions,"forgetting"questions, which have become redundant, is an important step for keeping the Stack Overflow content concise and useful. In this work, we study this managed forgetting task for Stack Overflow. Our work is based on data from more than a decade (2008 - 2019) - covering 18.1M questions, that are made publicly available by the site itself. For establishing a deeper understanding, we first analyze and characterize the set of questions about to be forgotten, i.e., questions that get a considerable number of views in the current period but become unattractive in the near future. Subsequently, we examine the capability of a wide range of features in predicting such forgotten questions in different categories. We find some categories in which those questions are more predictable. We also discover that the text-based features are surprisingly not helpful in this prediction task, while the meta information is much more predictive.
Dual-band Harmonic and Subharmonic Frequency Generation Circuitry for Joint Communication and Localization Applications Under Severe Multipath Environment

Abstract— The next generation of ultra-dense connected and automated wireless sensor networks (WSN) requires proximity intelligence for many of its applications, especially for identification and localization. This work presents the first bidirectional circuitry for Internet of Things (IoT) transponder that reciprocally generates harmonics and subharmonics, dual band frequencies. A multi-band or wideband localization system is essential for future intelligent WSN to mitigate the effect of multipath signals in indoor dense environments. The proposed frequency generation circuitry is based on the novel nonlinear ring resonator (NRR) operating based on standing wave resonation. The proposed NRR generates two sustainable oscillation frequencies based on the periodicity of the nonlinear circuit in the ring configuration. Due to the symmetry and reciprocity of the ring layout, the two bidirectional ports can excite the circuit at the two opposite nodes while maintaining the required boundary conditions for oscillation. The sustainable resonance conditions occur by creating zero, short impedance, and pole, infinite impedance, at subharmonic and harmonic excitation ports. The NRR circuit consumes zero DC power and covers two communication frequency plans interchangeably, which makes it a premier technique compared to the conventional ultra-wideband (UWB) localization system and conventional single band nonlinear passive circuitry. The latter is narrowband due to the tunning limitation of the nonlinear varactor while the former is a power hungry approach with complex hardware requirements. The circuit has 10 dB conversion loss with 200 MHz bandwidth in subharmonic generation or divider mode, and 18 dB loss in the harmonic generation or doubler mode. The average output power of the divider with 0-dBm input power is -9 dBm at fin = 4.8 GHz and -16 dBm for doubler at fin = 2.4 GHz, supporting in the order of few meter level link distance for joint communications and localization.
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scoresaround 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible.
ON A BERNSTEIN–SATO POLYNOMIAL OF A MEROMORPHIC FUNCTION

Abstract We define Bernstein–Sato polynomials for meromorphic functions and study their basic properties. In particular, we prove a Kashiwara–Malgrange-type theorem on their geometric monodromies, which would also be useful in relation with the monodromy conjecture. A new feature in the meromorphic setting is that we have several b-functions whose roots yield the same set of the eigenvalues of the Milnor monodromies. We also introduce multiplier ideal sheaves for meromorphic functions and show that their jumping numbers are related to our b-functions.