Miracle-dz/UniFAR-FTUs huggingface dataset:
Ergeben sich durch die Einführung der 8. Auflage der TNM-Klassifikation Änderungen für eine leitliniengerechte, chirurgische Therapiestrategie des Lungenkarzinoms?

Zusammenfassung Einleitung Durch die 8. Auflage der TNM-Klassifikation und die aktualisierte S3-Leitlinie ergaben sich in den letzten beiden Jahren Neuerungen in der Stadieneinteilung und Behandlung des Lungenkarzinoms. Ziel dieser Arbeit ist es, die Stadiendifferenzierungen und -wechsel von der 7. hin zur 8. Auflage zu identifizieren und mögliche therapeutische, insbesondere chirurgische Konsequenzen darzustellen. Methoden Prospektive Datenerhebung aller Lungenkrebsprimärfälle vom 01.01. 2016 bis 31.12. 2016 an 2 thoraxchirurgischen Zentren und Nachuntersuchung im März 2019. Vergleich der 7. Auflage der Tumorklassifikation für das Lungenkarzinom mit der 8. Auflage in Hinblick auf Veränderungen des Stadiums, Auswirkungen auf die leitliniengerechte, insbesondere chirurgische Therapie des Lungenkarzinoms und das Gesamtüberleben. Ergebnisse Es konnten 432 Primärfälle einer Lungenkrebserkrankung ermittelt werden. Davon konnten entsprechend der 8. Auflage 82 Patienten (7. Auflage: n = 85) dem Stadium I, 43 (n = 49) Patienten dem Stadium II, 100 (n = 91) Patienten dem Stadium III und 207 (n = 207) Patienten dem Stadium IV zugeordnet werden. Es wurden 81 (18,7%) Veränderungen des Tumorstadiums mit einer deutlichen Tendenz zur Höherstufung (77 Aufwertungen vs. 4 Abwertungen) detektiert. Bei 63 Patienten (14,6%) zeigte sich ein Stadienwechsel innerhalb eines Stadiums (z. B. IIA → IIB), für 18 Patienten (4,1%) ergab die Auswertung einen übergreifenden Stadienwechsel (z. B. IIB → IIIA) mit einer formalen Änderung in der Therapieempfehlung ohne primär operative Therapieindikation bei 12 Patienten (2,8%). Die neu eingeführten Subgruppen (IA1–3, IIIC und IVA/B) ergaben für 290 Patienten (67,1%) eine spezifischere Stadiendifferenzierung. Für die neuen Stadien IVA/B konnte ein signifikanter Unterschied im 2-Jahres-Überleben dargestellt werden (IVA = 25,2%; IVB = 13,0%; p < 0,05). Schlussfolgerung Die Weiterentwicklung der TNM-Klassifikation für das Bronchialkarzinom zur aktuellen 8. Auflage zeichnet sich durch eine differenziertere Einteilung aus. Im untersuchten Kollektiv zeigte sich eine deutliche Tendenz zur Höherstufung des Tumorstadiums. Unter Berücksichtigung der aktuellen Empfehlungen aus der S3-Leitlinie reduziert sich daher die Anzahl der Patienten mit formal primärer OP-Empfehlung im Stadium I – IIIA. Vor allem die Veränderungen des T-Status bei gleichbleibendem N-Status und die Aufwertung einzelner Tumorformeln in ein höheres Tumorstadium verursachen diese Dynamik. Das signifikant bessere 2-Jahres-Überleben im Stadium IVA bei singulärer Fernmetastasierung (M1b) und M1a-Metastasierung im Vergleich zum Stadium IVB unterstützt die differenzierte Betrachtung der unterschiedlichen Metastasierungsgrade.
Point Defects in Hard-Sphere Crystals†

We report numerical calculations of the concentration of interstitials in hard-sphere crystals. We find that in a three-dimensional fcc hard-sphere crystal at the melting point, the concentration of interstitials is 2.7(4) × 10-8. This is some 3 orders of magnitude lower than the concentration of vacancies. A simple, analytical estimate yields a value that is in fair agreement with the numerical results.
Comparison of Strategies for the Measurement of Mass Emissions from Diesel Engines Emitting Ultra-Low Levels of Particulate Matter

Regulatory methods for the measurement of particulate matter (PM) mass emissions have traditionally been gravimetric. Modern diesel engines equipped with aftertreatment systems, especially Diesel Particulate Filters (DPFs), however, emit much smaller amounts of particulate matter as compared to traditional diesel engines and emit particulate matter with variable compositions. These changes have led to difficulties in measuring PM emissions rates from modern diesel engines using gravimetric methods. Issues associated with diesel PM mass measurement, such as the semi-volatile nature of PM, the interactions with components in the dilution air such as water and ammonia, and the possibility of sampling artifacts, have counteracted a singular focus on mass measurements. These inherent problems may warrant some alternative approaches to characterizing emissions, using methods related to mass and impacts of emissions that can be more accurately defined. The present study provides a comparison and relative precision of several alternative mass measurement methods employed to measure the mass emissions of particulate matter from diesel engines with low and ultra-low levels of emissions. The methods of measurement reviewed in this study include two gravimetrically based methods, a chemically reconstructed mass method, and an integrated particle size distribution (IPSD) method. The mass measurements were consistent at low emission levels but the chemical speciation and IPSD methods achieved closer agreement and were more precise at ultra-low emission levels. Although mass measurement is a NIST-traceable quantity, alternative methods may present a new paradigm that better characterizes engine emissions in an atmospherically relevant manner.
A Novel Way of Catalyst-Free-Esterification of Ethyl Cellulose with Phthalic Anhydride

Ethyl cellulose (EC) was grafted with phthalic anhydride (PA) by esterification reaction without any catalyst in ethanol-methylbenzene mixture. The chemical structure and thermal stability of grafted copolymer (EC-g-PA) were analyzed by FT-IR and TGA, respectively, and compared with those of EC. The FT-IR results showed that EC has been grafted successfully with PA and the TGA results also show different thermal behavior between EC and EC-g-PA.
UV-Induced Photoisomerization of Acetylacetone and Identification of Less-Stable Isomers by Low-Temperature Matrix-Isolation Infrared Spectroscopy and Density Functional Theory Calculation

UV-induced photoisomerization of acetylacetone in low-temperature argon matrixes has been studied by Fourier transform infrared spectroscopy. Identifications of the species produced by UV irradiation (λ > 280 nm) were carried out with the aid of the density functional theory (DFT) calculation, in which the 6-31G* basis set was used to optimize the geometrical structures. By comparison of the observed infrared bands with the calculated spectral patterns, it was found that cis−trans isomerization around the C−C, CC, and C−O bonds occurs to produce less-stable enol isomers, 2-hydroxy-2-penten-4-one. Shorter-wavelength irradiation (λ < 280 nm) induced hydrogen-atom migration of the enol isomers to produce a keto isomer, 2,4-pentanedione.
Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.
Pseudo Shots: Few-Shot Learning with Auxiliary Data

In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary data sets that potentially contain useful information. We propose a framework to address the challenges of efficiently selecting and effectively using auxiliary data in image classification. Given an auxiliary dataset and a notion of semantic similarity among classes, we automatically select pseudo shots, which are labeled examples from other classes related to the target task. We show that naively assuming that these additional examples come from the same distribution as the target task examples does not significantly improve accuracy. Instead, we propose a masking module that adjusts the features of auxiliary data to be more similar to those of the target classes. We show that this masking module can improve accuracy by up to 18 accuracy points, particularly when the auxiliary data is semantically distant from the target task. We also show that incorporating pseudo shots improves over the current state-of-the-art few-shot image classification scores by an average of 4.81 percentage points of accuracy on 1-shot tasks and an average of 0.31 percentage points on 5-shot tasks.
Privacy-preserving Decentralized Federated Learning

In this paper, we develop SecureD-FL, a privacy-preserving decentralized federated learning algorithm, i.e., without the traditional centralized aggregation server. For the decentralized aggregation, we employ the Alternating Direction Method of Multiplier (ADMM) and examine its privacy weakness. To address the privacy risk, we introduce a communication pattern inspired by the combinatorial block design theory and establish its theoretical privacy guarantee. We also propose an efficient algorithm to construct such a communication pattern. We evaluate our method on image classification and next-word prediction applications over federated benchmark datasets with nine and fifteen distributed sites hosting training data. While preserving privacy, SecureD-FL performs comparably to the standard centralized federated learning method; the degradation in test accuracy is only up to 0.73%.
Disentangling Homophemes in Lip Reading using Perplexity Analysis

The performance of automated lip reading using visemes as a classification schema has achieved less success compared with the use of ASCII characters and words largely due to the problem of different words sharing identical visemes. The Generative Pre-Training transformer is an effective autoregressive language model used for many tasks in Natural Language Processing, including sentence prediction and text classification. This paper proposes a new application for this model and applies it in the context of lip reading, where it serves as a language model to convert visual speech in the form of visemes, to language in the form of words and sentences. The network uses the search for optimal perplexity to perform the viseme-to-word mapping and is thus a solution to the one-to-many mapping problem that exists whereby various words that sound different when spoken look identical. This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known. Sentences from the benchmark BBC dataset"Lip Reading Sentences in the Wild"(LRS2), are classified with a character error rate of 10.7% and a word error rate of 18.0%. The main contribution of this paper is to propose a method of predicting words through the use of perplexity analysis when only visual cues are present, using an autoregressive language model.
Measuring Electrostatic Double-Layer Forces at High Surface Potentials with the Atomic Force Microscope

The aim of this study was to measure interaction forces between surfaces with high electric potentials in aqueous electrolyte solutions. Therefore, the force between a platinum or gold sample, which served as the working electrode, and a silicon nitride tip of an atomic force microscope was measured. Various potentials were applied between the sample and a reference electrode. Experimental results were compared to forces calculated with the Poisson−Boltzmann equation. As predicted by theory, the electrostatic double-layer force changed only in a narrow potential range of about 300 mV and saturated below and above this range. Within this range the repulsion grew with more negative sample potentials. This was expected, since the tip was negatively charged at the high pH chosen. At strong negative sample potentials this saturation was not complete and the force continued to rise slightly when lowering the potential. Another surprising and yet unexplained observation was a weak long-range attraction at positi.
Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback

We study the multi-armed bandit (MAB) problem with composite and anonymous feedback. In this model, the reward of pulling an arm spreads over a period of time (we call this period as reward interval) and the player receives partial rewards of the action, convoluted with rewards from pulling other arms, successively. Existing results on this model require prior knowledge about the reward interval size as an input to their algorithms. In this paper, we propose adaptive algorithms for both the stochastic and the adversarial cases, without requiring any prior information about the reward interval. For the stochastic case, we prove that our algorithm guarantees a regret that matches the lower bounds (in order). For the adversarial case, we propose the first algorithm to jointly handle non-oblivious adversary and unknown reward interval size. We also conduct simulations based on real-world dataset. The results show that our algorithms outperform existing benchmarks.
Influence of crystal chemistry on ideal plastic shear anisotropy in forsterite: First principle calculations

Abstract We present ab initio calculations of ideal shear strengths (ISS) in forsterite at zero temperature using pseudopotential density functional theory within the generalized gradient approximation. A localized rigid-body shear is imposed on a given plane of an infinite defect-free crystal. The energy increase associated with this shear (called the generalized stacking fault energy) gives access to the ISS. The goal of this study is to assess the influence of crystal chemistry on the intrinsic resistance of plastic shear of a mineral like forsterite. ISS have been calculated for plastic shear along [100], [010], and [001] in various potential glide planes of forsterite. We show that the [001] slip, which corresponds experimentally to an easy glide at low temperature, exhibits the lowest energy barrier. The [010] glide is precluded because it involves very unfavorable atom impingements.
A One-Size-Fits-All Solution to Conservative Bandit Problems

In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a given baseline at any time. We propose a One-Size-Fits-All solution to CBPs and present its applications to three encompassed problems, i.e. conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, we focus on a sample-path constraint on the actually received reward, and achieve better theoretical guarantees (T-independent additive regrets instead of T-dependent) and empirical performance. Furthermore, we extend the results and consider a novel conservative mean-variance bandit problem (MV-CBP), which measures the learning performance with both the expected reward and variability. For this extended problem, we provide a novel algorithm with O(1/T) normalized additive regrets (T-independent in the cumulative form) and validate this result through empirical evaluation.
Random matrix theory of the isospectral twirling

In this paper, we present a systematic construction of probes into the dynamics of isospectral ensembles of Hamiltonians by the notion of Isospectral twirling, expanding the scopes and methods of ref. [1]. The relevant ensembles of Hamiltonians are those defined by salient spectral probability distributions. The Gaussian Unitary Ensembles (GUE) describes a class of quantum chaotic Hamiltonians, while spectra corresponding to the Poisson and Gaussian Diagonal Ensemble (GDE) describe non chaotic, integrable dynamics. We compute the Isospectral twirling of several classes of important quantities in the analysis of quantum many-body systems: Frame potentials, Loschmidt Echos, OTOCS, Entanglement, Tripartite mutual information, coherence, distance to equilibrium states, work in quantum batteries and extension to CP-maps. Moreover, we perform averages in these ensembles by random matrix theory and show how these quantities clearly separate chaotic quantum dynamics from non chaotic ones.
Uniform Capacitated Facility Location Problems with Penalties/Outliers

In this paper, we present a framework to design approximation algorithms for capacitated facility location problems with penalties/outliers using LP-rounding. Primal-dual technique, which has been particularly successful in dealing with outliers and penalties, has not been very successful in dealing with capacities. On the other hand, no primal-dual solution has been able to break the hardness of capacitated facility location problem(CFLP). LP-rounding techniques had also not been very successful in dealing with capacities until a recent work by Grover et al. \cite{GroverGKP18}. Their constant factor approximation violating the capacities by a small factor ($1 + \epsilon$) is promising while dealing with capacities.Though LP-rounding has not been very promising while dealing with penalties and outliers, we successfully apply it to deal with them along with capacities. That is, our results are obtained by rounding the solution to the natural LP once again exhibiting the power of LP-rounding technique. Solutions obtained by LP-rounding are easy to integrate with other LP-based algorithms.In this paper, we apply our framework to obtain first constant factor approximations for capacitated facility location problem with outlier (CFLPO) and capacitated $k$-facility location problem with penalty(C$k$FLPP) for hard uniform capacities using LP-rounding. Our solutions incur slight violations in capacities, ($1 + \epsilon$) for the problems without cardinality($k$) constraint and ($2 + \epsilon$) for the problems with the cardinality constraint. For the outlier variant, we also incur a small loss ($1 + \epsilon$) in outliers. Due to the hardness of the underlying problems, the violations are inevitable. Thus we achieve the best possible by rounding the solution of natural LP for these problems. To the best of our knowledge, no results are known for CFLPO and C$k$FLPP.
Double Stimuli‐Responsive Isoporous Membranes via Post‐Modification of pH‐Sensitive Self‐Assembled Diblock Copolymer Membranes

Double stimuli‐responsive membranes are prepared by modification of pH‐sensitive integral asymmetric polystyrene‐b‐poly(4‐vinylpyridine) (PS‐b‐P4VP) diblock copolymer membranes with temperature‐responsive poly(N‐isopropylacrylamide) (pNIPAM) by a surface linking reaction. PS‐b‐P4VP membranes are first functionalized with a mild mussel‐inspired polydopamine coating and then reacted via Michael addition with an amine‐terminated pNIPAM‐NH2 under slightly basic conditions. The membranes are thoroughly characterized by nuclear magnetic resonance (1H‐NMR), Fourier transform infrared spectroscopy and X‐ray‐induced photoelectron spectroscopy. Additionally dynamic contact angle measurements are performed comparing the sinking rate of water droplets at different temperatures. The pH‐ and thermo‐double sensitivities of the modified membranes are proven by determining the water flux under different temperature and pH conditions.
AIR-FI: Generating Covert Wi-Fi Signals from Air-Gapped Computers

In this paper, we show that attackers can exfiltrate data from air-gapped computers via Wi-Fi signals. Malware in a compromised air-gapped computer can generate signals in the Wi-Fi frequency bands. The signals are generated through the memory buses - no special hardware is required. Sensitive data can be modulated and secretly exfiltrated on top of the signals. We show that nearby Wi-Fi capable devices (e.g., smartphones, laptops, IoT devices) can intercept these signals, decode them, and send them to the attacker over the Internet. To extract the signals, we utilize the physical layer information exposed by the Wi-Fi chips. We implement the transmitter and receiver and discuss design considerations and implementation details. We evaluate this covert channel in terms of bandwidth and distance and present a set of countermeasures. Our evaluation shows that data can be exfiltrated from air-gapped computers to nearby Wi-Fi receivers located a distance of several meters away.