The heat capacity and derived thermophysical properties of the high TC superconductor YBa2Cu3O7−δ from 5.3 to 350 K

The heat capacity of the perovskite high‐TC superconductor YBa2Cu3O7−δ was measured from 5.3 to 350 K in an adiabatic calorimetric cryostat. A break in the heat‐capacity curve, associated with the critical temperature for superconductivity was observed between 90.09 and 92.59 K. The transition temperature was identified as 91.44 K, and ΔCp,m was calculated to be 0.559R at that temperature. The lattice heat capacity was evaluated by means of the recently developed Komada/Westrum phonon distribution model and the apparent characteristic temperature ΘKW was calculated to be 107.7 K. The excess electronic heat capacity for the superconducting phase was evaluated and the energy gap was identified as 234. R K. Excess contribution, resulting from magnetic impurities, was noted below 20 K. Thermodynamic properties at selected temperatures are presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71226/2/JCPSA6-92-11-6794-1.pd

The thermodynamics of the divalent metal fluorides. III. Heat capacity of the fast ion conductor SrSnF4 from 6 to 344 K

The heat capacity of the fast ion conductor SrSnF4 was measured by adiabatic calorimetry from 6 T/K 19F spin-lattice relaxation time T1 occurs. Standard molar thermodynamic functions are given at selected temperatures from 5 to 345 K.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27858/1/0000271.pd

Privacy-Preserving Decision Tree Training and Prediction against Malicious Server

Privacy-preserving machine learning enables secure outsourcing of machine learning tasks to an untrusted service provider (server) while preserving the privacy of the user\u27s data (client). Attaining good concrete efficiency for complicated machine learning tasks, such as training decision trees, is one of the challenges in this area. Prior works on privacy-preserving decision trees required the parties to have comparable computational resources, and instructed the client to perform computation proportional to the complexity of the entire task. In this work we present new protocols for privacy-preserving decision trees, for both training and prediction, achieving the following desirable properties: 1. Efficiency: the client\u27s complexity is independent of the training-set size during training, and of the tree size during prediction. 2. Security: privacy holds against malicious servers. 3. Practical usability: high accuracy, fast prediction, and feasible training demonstrated on standard UCI datasets, encrypted with fully homomorphic encryption. To the best of our knowledge, our protocols are the first to offer all these properties simultaneously. The core of our work consists of two technical contributions. First, a new low-degree polynomial approximation for functions, leading to faster protocols for training and prediction on encrypted data. Second, a design of an easy-to-use mechanism for proving privacy against malicious adversaries that is suitable for a wide family of protocols, and in particular, our protocols; this mechanism could be of independent interest

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Specific heat of a high‐Tc perovskite superconductor YBa2Cu3O8−δ

Specific heat measuremnts on high transition temperature perovskite superconductor were conducted from 5.3 K to 345 K. The sample was prepared by mixing stoichiometric amounts of Yttrium oxide, BaO, and CuO so as to yield a 1:2:3 ratio of metals. T he decrease in specific heat at the transition temperature ‐ 91 K is discussed.(AIP)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69438/2/JCPSA6-87-8-5040-1.pd

qPCR Raw Data Fig 5

qPCR Raw Data Fig