Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the performance of a 700 GHz

We report the successful operation of an SIS nline mixer at frequencies above and below the superconducting gap of Nb. The mixer is fed by an antipodal nline taper deposited on a quartz substrate in one piece with the Nb-A1Q-Nb tunnel junction and Nb tuning circuitry. RF power is coupled to the waveguide mounted nline mixer chip via a Pickett-Potter horn-re ector antenna and the mixer is tuned by a novel three-stage Chebyshev her and radial stub tuner. We describe the design and testing of the mixer between 640 and 710 GHz, paying particular attention to the evaluation of conduction losses in the nline chip, and the prediction of the conversion gain of the mixer. Our results show that nline mixers can have good performance both above and below the superconducting energy gap.
OmegaLog: High-Fidelity Attack Investigation via Transparent Multi-layer Log Analysis

—Recent advances in causality analysis have en- abled investigators to trace multi-stage attacks using provenance graphs. Based on system-layer audit logs (e.g., syscalls), these approaches omit vital sources of application context (e.g., email addresses, HTTP response codes) that can be found in higher layers of the system. Although such information is often essential to understanding attack behaviors, it is difﬁcult to incorporate this evidence into causal analysis engines because of the semantic gap that exists between system layers. To address that short- coming, we propose the notion of universal provenance , which encodes all forensically relevant causal dependencies regardless of their layer of origin. To transparently realize that vision on commodity systems, we present OmegaLog, a provenance tracker that bridges the semantic gap between system and application logging contexts. OmegaLog analyzes program binaries to identify and model application-layer logging behaviors, enabling accurate reconciliation of application events with system-layer accesses. OmegaLog then intercepts applications’ runtime logging activities and grafts those events onto the system-layer provenance graph, allowing investigators to reason more precisely about the nature of attacks. We demonstrate that our system is widely applicable to existing software projects and can transparently facilitate execution partitioning of provenance graphs without any training or developer intervention. Evaluation on real-world attack scenarios shows that our technique generates concise provenance graphs with rich semantic information relative to the state-of-the-art, with an average runtime overhead of 4%.
Secure Sublinear Time Differentially Private Median Computation

—In distributed private learning, e.g., data analysis, machine learning, and enterprise benchmarking, it is common-place for two parties with conﬁdential data sets to compute statistics over their combined data. The median is an important robust statistical method used in enterprise benchmarking, e.g., companies compare typical employee salaries, insurance companies use median life expectancy to adjust insurance premiums, banks compare credit scores of their customers, and ﬁnancial regulators estimate risks based on loan exposures. The exact median can be computed securely, however, it leaks information about the private data. To protect the data sets, we securely compute a differentially private median over the joint data set via the exponential mechanism. The exponential mechanism has a runtime linear in the data universe size and efﬁciently sampling it is non-trivial. Local differential privacy, where each user shares locally perturbed data with an untrusted server, is often used in private learning but does not provide the same utility as the central model, where noise is only applied once by a trusted server. We present an efﬁcient secure computation of a differentially private median of the union of two large, conﬁdential data sets. Our protocol has a runtime sublinear in the size of the data universe and utility like the central model without a trusted third party. We provide differential privacy for small data sets (sublinear in the size of the data universe) and prune large data sets with a relaxed notion of differential privacy providing limited group privacy. We use dynamic programming with a static, i.e., data-independent, access pattern, achieving low complexity of the secure computation circuit. We provide a comprehensive evaluation over multiple AWS regions (from Ohio to N. Virgina, Canada and Frankfurt) with a large real-world data set with a practical runtime of less than 7 seconds for millions of records.
Decay in the Alaskan Tundra: The Effects of Initial Litter Quality and Leaching on Long-Term Plant Decomposition

Litter decomposition contributes to approximately 70% of the annual terrestrial carbon flux to the atmosphere, which is estimated to be 68 Pg C per year (Raich and Schlesinger 1992). Litter decomposition includes physical, chemical, and biological mechanisms responsible for transforming organic matter into more stable, oxidized forms. Decomposition rates of litter depend on four factors: (1) chemical composition of the substrate, such as the amount of nitrogen and lignin; (2) nitrogen availability; (3) soil microbial composition; and (4) environmental conditions, such as moisture, pH, and temperature (Tenney and Waksman 1929). Litter is decomposed in three steps (Fig. 1). First, a fraction of water-soluble compounds are abiotically leached from the litter by rain and snow (Berg and McClaugherty 2008). Second, litter is broken down by detrivores to smaller particle sizes that can be chemically reduced. Third, decomposers, such as bacteria and fungi, chemically transform these particles into basic inorganic molecules (Aerts 2006). The first stage of litter decay is dominated by the decomposition of soluble and lowmolecular weight compounds, while later stages are dominated by the sequential decomposition of hemicellulose, cellulose, and lignin (Berg and McClaugherty 2008). Leaching is the loss of nutrients and organic compounds from decomposing litter by water (Berg and McClaugherty 2008). During the initial stages of decomposition, mass loss is influenced by the leaching rate of water-soluble compounds that are not physiologically modified by microorganisms before being leached (McClaugherty 1983). Immediately at litterfall, mobile nutrients, sugars, and low-molecular-weight phenolics are leached, while nutrients are partially leached (Berg and McClaugherty 2008). The magnitude of leaching losses is influenced by abiotic conditions (e.g., freeze-thaw cycles) and initial litter quality.
Inelastic electron scattering form factors for the positive and negative parity states of 12C in the framework of particle-hole configurations

The longitudinal and transverse form factors for the transitions M1 to the 20.6 MeV (3,T=1) level, M2 to the 19.50 MeV (  1 2 , T=1) level, M2 to the 22.70 MeV ( 2 2 , T=1) level and C3 to the 18.6 MeV (  3 , T=1) level in C have been calculated with the harmonic oscillator (HO) shell model. Correlation in the ground state wave functions by mixing more than one configuration is considered. Core polarization effects are taken into consideration through the use of effective operators. The experimental data are very well described for both positive and negative parity states when these ingredients are employed in the calculations of the form factors. PACS numbers: 25.30. D, 13.40. G, 21.60. C
The efficacy of manual and ultrasonic technique for removal of Calcium Hydroxide medicament from root canals: A comparative study

Background: The presence of microbes within the root canal plays a key role in causing endodontic infections. The main aim of root canal therapy is getting a microbial diminution and subsequent elimination of their byproducts from the root canal system. Several techniques have been proposed to remove the Ca(OH)2 dressing from the root canal system, including the use of endodontic hand files, sonic activation, passive ultrasonic irrigation, the CanalBrush System, and nickel-titanium (NiTi) rotary instruments. Aim of the study: To study efficacy of manual and ultrasonic technique for removal of Calcium Hydroxide medicament from root canals. Materials and methods: The study was conducted in the Department of Conservative Dentistry and Endodontics of Dental Institution. For the study we selected 50 extracted maxillary central incisors. Teeth with incompletely formed apex and having morphological and structural anomalies were excluded from the study. The selected teeth were immersed in sodium hypochlorite solution for 3 days to remove any organic debris. In Group 1, the removal of CaOH2 paste was done manually using Size 30 Flexo file and NaOCl as irrigant. In Group 2, the removal of CaOH2 paste was done using ultrasonic instrumentation and NaOCl as irrigant. After, removal of calcium hydroxide, evaluation of empty canals was done by taking radiographs of roots. Results: We observed that on comparing the gray values before application and after application, there was a statistically significant difference which means after removal of CaOH2 from the canals, the canals did not attain its previous empty state. We observed that the difference was statistically non-significant between both the techniques. Conclusion: Within the limitations of the present study, it can be concluded that both the techniques for removal of calcium hydroxide from the root canals are quite efficient.
Response-Order Effects for Self-report Questionnaires: Exploring the role of Overclaiming Accuracy and Bias

Primacy effects refer to the tendency for participants to choose questionnaire response options that are closer to the beginning of a list. We sought to replicate this effect using measures of personality and well-being. We also explored accuracy and bias on the Overclaiming Questionnaire (OCQ) as moderators. Participants were undergraduates (N = 774; 79.2% female; 73.1% Caucasian). We used a two-group, between-subjects design which manipulated the presentation order for response options on a 5-point Likert scale. The two conditions were ascending (Strongly Disagree first) and descending (Strongly Agree first). We did not find support for OCQ accuracy/bias as a moderator. Because effect sizes were very small, primacy effects may be of little practical importance in this context. Open Data/Methods: https://osf.io/aec25/
Effects of 2D seismic on the snow crab fishery

Sound is used by a variety of marine taxa for feeding, reproduction, navigation and predator avoidance and therefore alterations to the soundscape from industrial noise have the potential to negatively affect an animal’s fitness. Furthermore, responses to industrial noise would also have the potential to negatively influence commercial fishing interests. Unfortunately marine invertebrates are generally underrepresented in the seismic effects literature. Snow crab harvesters in Atlantic Canada contend that seismic noise from widespread hydrocarbon exploration has strong negative effects on catch rates. We repeated a Before-After-Control-Impact study over two years to assess the effects of industry scale seismic exposure on catch rates of snow crab along the continental slope of the Grand Banks of Newfoundland. Our results did not support the contention that seismic activity negatively affects catch rates in shorter term (i.e. within days) or longer time frames (weeks). However, significant differences in catches were observed across study areas and years. While the inherent variability of the CPUE data limited the statistical power of this study, our results do suggest that if seismic effects on snow crab harvests do exist, they are smaller than changes related to natural spatial and temporal variation.
Majority-Strategyproofness in Judgment Aggregation

By a combination of well-known results in judgment aggregation, it is essentially impossible to design an aggregation rule that simultaneously satisfies two crucial requirements: to always return an outcome that is logically consistent, and to be immune to strategic manipulation. To address this dilemma, we put forward a novel notion of strategyproofness, which requires immunity to strategic manipulation only in certain well-defined situations—namely when either the truthful profile of individual judgments or the profile a would-be manipulator is trying to reach are majority-consistent. We argue that this constitutes an attractive compromise for aggregation rules one may want to use in practice, and we prove that several important rules are strategyproof in this sense. This includes, in particular, all rules belonging to the family of additive majority rules, such as the Kemeny rule and the Slater rule.
Changes of antioxidant activity and active compounds content in selected teas

Our study tested 45 tea infusions classified into five groups (white, green, red, black, and other teas) for the content of total polyphenols and flavonoids, as well as antioxidant properties, by the FRAP and DPPH methods. We examined these parameters after prolongation of the brewing time from 10 to 30 min and overnight storage. The results showed that the capacity of the teas to bind free radicals was differentiated and the amount of anti-oxidant compounds depended on their nature. In terms of antioxidant activity and total polyphenol content, the tested tea types were ranked in the following order: white > green > black > red > other teas (yerba mate > rooibos). Our experiment demonstrated a positive correlation between the polyphenol content and antioxidant activity of the analyzed teas. Also, the DPPH antiradical efficiency was comparable to their ability to reduce ferric ions. The extended brewing time had a significant effect on the antioxidant activity of the infusions and the polyphenolic compounds analyzed therein. In contrast, storage of the infusions for 24 h at room temperature changed their antioxidant activity and affected the total polyphenol content.
Combining Classifiers to Extract Information From Bibliographic References

The correct identification of information from fields that form a bibliographic reference is a crucial step to the proper functioning of repositories and search engines that aim to provide organized and accurate information to their users. To perform the extraction of information in bibliographic references, several learning models can be applied with success. However, this paper aimed to investigate if this process can be further improved using a combination based on aggregation techniques. To answer this question, the present work creates and evaluates a system that combines the results from specialized classifiers using different aggregation techniques. Results show that the combination of outputs can improve the performance over individual classifiers. In addition, a voting method that further improves the results of reference extraction is presented. This mechanism intends to be context independent, since it does not rely on training datasets or the knowledge of the classifiers being used in order to properly function.
Carnus: Exploring the Privacy Threats of Browser Extension Fingerprinting

—With users becoming increasingly privacy-aware and browser vendors incorporating anti-tracking mechanisms, browser ﬁngerprinting has garnered signiﬁcant attention. Accord-ingly, prior work has proposed techniques for identifying browser extensions and using them as part of a device’s ﬁngerprint. While previous studies have demonstrated how extensions can be detected through their web accessible resources, there exists a signiﬁcant gap regarding techniques that indirectly detect extensions through behavioral artifacts. In fact, no prior study has demonstrated that this can be done in an automated fashion. In this paper, we bridge this gap by presenting the ﬁrst fully automated creation and detection of behavior-based extension ﬁngerprints. We also introduce two novel ﬁngerprinting techniques that monitor extensions’ communication patterns, namely outgoing HTTP requests and intra-browser message exchanges. These techniques comprise the core of Carnus, a modular system for the static and dynamic analysis of extensions, which we use to create the largest set of extension ﬁngerprints to date. We leverage our dataset of 29,428 detectable extensions to conduct a comprehensive investigation of extension ﬁngerprinting in realistic settings and demonstrate the practicality of our attack. Our in-depth analysis conﬁrms the robustness of our techniques, as 83.6% - 87.92% of our behavior-based ﬁngerprints remain effective against a state-of-the-art countermeasure. Subsequently, we aim to explore the true extent of the privacy threat that extension ﬁngerprinting poses to users, and present a novel study on the feasibility of inference attacks that reveal private and sensitive user information based on the functionality and nature of their extensions. We ﬁrst collect over 1.44 million public user reviews of our detectable extensions, which provide a unique macroscopic view of the browser extension ecosystem and enable a more precise evaluation of the discriminatory power of extensions as well as a new deanonymization vector. We also automatically categorize extensions based on the developers’ descriptions and identify those that can lead to
Electric Power System Fault Analysis

Fault analysis is an important aspect in the successful operation of a power utility grid. The occurrence of faults in the system is not avoidable as causes of faults are not particularly one but of many with different causes and nature. It is one of the most important as well as it one of the most complex tasks in power engineering. The studies and discovery of faults in electric power systems is essential in ensuring the consistency and the stability if the power grid. Another importance of carrying out fault analysis gives a fair idea of the voltages and the current magnitudes under faulty conditions for different scenarios. This is helpful in the design of protection devices such as fuses, circuit breaker ratings, over current protection relays and other devices that are used for the protection of the system. Protection devices are important features of the power system grid mainly because their role in saving equipment and also human lives. In this paper, we investigate and analyze the behavior of electric power systems under fault conditions and then evaluate various practical scenarios.
Active Learning Experimental Design of Bayesian Networks

The importance and use of experimental design in biological applications was one motivation for my thesis [Sverchkov and Craven, 2017]. We go over a theory overview of Bayesian statistics, Bayesian networks, and experiments in networks. We then test different edge-wise priors and explain their effect on expected information gain of experiments.
The IIM System for Blizzard Challenge 2019

This paper introduces the IIM-USTC speech synthesis system for Blizzard Challenge 2019. The task is to build a speech synthesis system on a 8-hour Chinese male talkshow audio corpus. The submitted system followed our previous one proposed in Blizzard Challenge 2018. A hidden Markov model (HMM)- based unit selection system was built with improvements in back-end acoustic modeling. Two models were built for unit selection, an LSTM-RNN based acoustic model was built and the hidden layer was adopted as context embedding feature, a DNN based unit embedding model was built and the unit vector was adopted as phone unit feature. Evaluation results demonstrated that our system performed at the same level as last year.
Tax Knowledge Diffusion via Strategic Alliances Tax Knowledge Diffusion via Strategic Alliances

This study examines strategic alliances as channels for tax knowledge diffusion between firms. Although strategic alliances are primarily expected to foster their main business purposes, we focus on whether tax knowledge potentially diffuses as a second order effect of peer-to-peer cooperation. To tease out diffusion of tax knowledge, we investigate changes in the tax planning behavior of high-tax firms in strategic alliances with low-tax firms in comparison to high-tax firms in strategic alliances with other high-tax firms. Our results suggest an economically meaningful decrease of high-tax firms’ cash effective tax rates when cooperating with low-tax firms. Additionally, we find that this adjustment occurs within two years of a strategic alliance’s initiation. We apply textual analysis to control for the strategic alliances’ main business purposes in our analyses. Because these activities do not appear to drive our findings, we argue to identify tax knowledge diffusion as a second order effect and conjecture that strategic alliances are not intended to be tax planning investments. Finally, we test whether partner characteristics intensify or mitigate the identified effects. Overall, our results provide robust evidence for tax knowledge diffusion via strategic alliances.
Distributed Cryptanalytic Time-Memory Trade-Offs

Information security is usually based on secrets known by authorized parties only, e.g., passwords, PIN codes, and cryptographic keys. Breaking a system means either circumventing the security mechanisms, or exploiting a weakness in these mechanisms, or retrieving the secrets by any other means, including through an exhautive search among all possible secrets. If secrets were long-enough and truly random, the latter attack would not be practical. It is unfortunately very common that real-life secrets do not fulfill these assumptions, meaning that they do not carry enough entropy. A cryptanalytic time-memory trade-off (TMTO) is a technique introduced by Martin Hellman in 1980 to speed up exhaustive searches using stored precomputations. TMTOs were not efficient in the eighties due to the limited resources of that time’s computers, and they have no longer been considered until Philippe Oechslin resurrected them in 2003 to crack Windows passwords. His contribution was outstanding: he demonstrated his ability to crack any alphanumerical Windows password within a few seconds. Today, TMTOs belong to any security expert’s toolbox, e.g., to perform penetration tests, legal investigations, and to test users’ passwords on the fly when they are requested to refresh their passwords. TMTOs’ impact is not limited to passwords, though, and they have been successfully used to attack for example GSM’s security primitives and vehicule ignition systems. This thesis aims at improving TMTOs along two axes: improving the precomputation phase using a distributed approach, and applying the technique to the field of data desanonymisation. Indeed, the main bottleneck of TMTOs is today the precomputation phase, which requires large resources, while most of the precomputations are actually wasted (typically, 96% of the precomputations are eventually thrown away). This offers room for progress. Although there exits a method to make the precomputation phase more efficient, it cannnot be used when considering a distributed precomputation phase, which is yet an unavoidable setup for large-scale TMTOs. We already performed promising experiments on a new way to perform a distributed precomputation phase, and the thesis will formalize and optimize this approach, and discover new approaches. Then, we would like to highlight that anonymisation of data is commonly achieved by applying a one-way function on the field that identifies the data. It can for example be names, email addresses, mac addresses, etc. Desanonymizing data is then very close from cracking passwords. However, no one attempted yet to apply the TMTO technique for desanonymizing data. We expect to do so in this thesis, which may lead to significant scientific advances and a high societal impact.