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Threshold public-key encryption (threshold PKE) has various useful applications. A lot of threshold PKE schemes are proposed based on RSA, Diffie–Hellman and lattice, but to the best of our knowledge, code-based threshold PKEs have not been proposed. In this paper, we provide three IND-CCA secure code-based threshold PKE schemes. The first scheme is the concrete instantiation of Dodis–Katz conversion (Dodis and Katz, TCC'05) that converts an IND-CCA secure PKE into an IND-CCA secure threshold PKE using parallel encryption and a signature scheme. This approach provides non-interactive threshold decryption, but ciphertexts are large (about 16 kilobytes for 128-bit security) due to long code-based signatures even in the state-of-the-art one. The second scheme is a new parallel encryption-based construction without signature schemes. Unlike the Dodis–Katz conversion, our parallel encryption converts an OW-CPA secure PKE into an OW-CPA secure threshold PKE. To enhance security, we use Cong et al.'s conversion (Cong et al., ASIACRYPT'21). Thanks to eliminating signatures, its ciphertext is 512 bytes, which is only 3% of the first scheme. The decryption process needs an MPC for computing hash functions, but decryption of OW-CPA secure PKE can be done locally. The third scheme is an MPC-based threshold PKE scheme from code-based assumption. We take the same approach Cong et al. took to construct efficient lattice-based threshold PKEs. We build an MPC for the decryption algorithm of OW-CPA secure Classic McEliece PKE. This scheme has the shortest ciphertext among the three schemes at just 192 bytes. Compared to the regular CCA secure Classic McEliece PKE, the additional ciphertext length is only 100 bytes. The cons are heavy distributed computation in the decryption process. [ABSTRACT FROM AUTHOR]

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management. [ABSTRACT FROM AUTHOR]

Aiming at few-shot classification in the field of hyperspectral remote sensing images, this paper proposes a classification method based on cross-scene adaptive learning. First, based on the unsupervised domain adaptive technology, cross-scene knowledge transfer learning is carried out to reduce the differences between source scene and target scene. At the same time, depthwise over-parameterized convolution is used in the deep embedding model to improve the convergence speed and feature extraction ability. Second, two symmetrical subnetworks are designed in the model to further reduce the differences between source scene and target scene. Then, Manhattan distance is learned in the Manhattan metric space in order to reduce the computational cost of the model. Finally, the weighted K-nearest neighbor is introduced for classification, in which the weighted Manhattan metric distance is assigned to the clustered samples to improve the processing ability to the imbalanced hyperspectral image data. The effectiveness of the proposed algorithm is verified on the Pavia and Indiana hyperspectral dataset. The overall classification accuracy is 90.90% and 65.01%. Compared with six other kinds of hyperspectral image classification methods, the proposed cross-scene method has better classification accuracy. [ABSTRACT FROM AUTHOR]

Trapdoor functions contributed since their announcement in the evolvement cryptography as we know it, especially the lossy mode, by helping reduce the residual leakage for an optimal rate, but to make it more resilient cryptographically: generic constructions were made based on graph isomorphism, or other NP-hard problems defended by the zero-knowledge proof, such were used in Indistinguishability under Chosen-Plaintext Attack (IND-CPA), Computationnal Diffie-Hellman (CDH), or Decisional Diffie-Hellman (DDH). Once schemes like Indistinguishability under Chosen- Ciphertext Attack (IND-CCA) were adopted it became clear it cannot simulate a decryption using Lossy Trapdoor Functions (LTF); the problem with existing trapdoor functions in general is partial information leakage, lack of randomness and multiple messages insecurity. In the light of the following issues came the idea to present through this paper a simple but important fix, in the note of randomness a new Variate of the Engel expansion (VEE) is chosen, providing a pseudo-random bit sequence as an output, the reason being to recover the seed of the algorithm for an attacker, it is considered a hard number theory problem, and surely after the new construction in this paper, another NP-complete problem emerging from tensors the scheme is more secure. As for the strenghtening evidence of how it can be trusted, it seems more robust to supply a proof of its ergodicity as being done in this article, instead of semantic security analysis, to prove the efficiency of the new construction resolving the issues surrounding multi-challenge using a lossy trapdoor function. [ABSTRACT FROM AUTHOR]

At the start of a moderate‐intensity square‐wave exercise, after a short delay, breath‐by‐breath O2 uptake at the mouth is approximated to a mono‐exponential function, whose time constant is considered matched to that of the O2 uptake of the working muscles. We compared the kinetic parameters obtained from the breath‐by‐breath gas exchange data yielded by the 'Independent‐breath' algorithm (IND), which accounts for the changes in lung gas stores, with those obtained with the classical 'Expiration‐only' algorithm (EXP). The two algorithms were applied on the same flow and gas fraction traces acquired on 10 healthy volunteers, performing 10 times the same moderate‐intensity exercise transition. Repeated O2 uptake responses were stacked together and the kinetic parameters of a mono‐exponential function were estimated by non‐linear regression, removing the data pertaining to 1‐s progressively longer initial periods (ΔTr). Independently of ΔTr, the mean response time (time constant + time delay) obtained for the IND data was faster compared to the EXP data (∼43 s vs. ∼47 s, P < 0.001), essentially because of shorter time delays. Between ΔTr = 16 s and ΔTr = 29s, the time constants of the IND data decreased (30.7 s vs. 28.0 s, P < 0.05; drop = 10%), but less than those of the EXP data (32.2 s vs. 26.2 s, P < 0.001; drop = 23%); with the same ΔTr, the time constants of the two algorithms' data were not different (P > 0.07). The different decrease in the time constant, together with the different mean response time, suggests that the data yielded by the two algorithms provide a different picture of the phenomena occurring at the beginning of the exercise. What is the central question of this study?Is the kinetics of breath‐by‐breath gas exchange at the start of a square‐wave moderate intensity exercise faster when accounting for the changes in lung gas stores?What is the main finding and its importance?Accounting for the changes in lung gas stores, the time constant of the V̇O2${\dot V_{{{\mathrm{O}}_{\mathrm{2}}}}}$ kinetics changed less when the data pertaining to the initial period were progressively removed; time delays and mean response times were faster. Consequently, using different gas exchange calculation algorithms, the physiological phenomena occurring at the beginning of moderate‐intensity exercise are characterized differently. [ABSTRACT FROM AUTHOR]

This paper surveys results related to well-known works of B. Plotkin and V. Remeslennikov on the edge of algebra, logic and geometry. We start from a brief review of the paper and motivations. The first sections deal with model theory. In the first part of the second section we describe the geometric equivalence, the elementary equivalence, and the isotypicity of algebras. We look at these notions from the positions of universal algebraic geometry and make emphasis on the cases of the first order rigidity. In this setting Plotkin's problem on the structure of automorphisms of (auto)endomorphisms of free objects, and auto-equivalence of categories is pretty natural and important. The second part of the second section is dedicated to particular cases of Plotkin's problem. The last part of the second section is devoted to Plotkin's problem for automorphisms of the group of polynomial symplectomorphisms. This setting has applications to mathematical physics through the use of model theory (non-standard analysis) in the studying of homomorphisms between groups of symplectomorphisms and automorphisms of the Weyl algebra. The last sections deal with algorithmic problems for noncommutative and commutative algebraic geometry.The first part of it is devoted to the Gröbner basis in non-commutative situation. Despite the existence of an algorithm for checking equalities, the zero divisors and nilpotency problems are algorithmically unsolvable. The second part of the last section is connected with the problem of embedding of algebraic varieties; a sketch of the proof of its algorithmic undecidability over a field of characteristic zero is given. [ABSTRACT FROM AUTHOR]

Huntingtons disease (HD) is a hereditary disorder involving the central nervous system. Its effects are devastating, to the affected person as well as his family. The Department of Medical and Molecular Genetics at Indiana University (IU) plays an integral part in Huntingtons research by providing computerized repositories of HD family information for researchers and families. The National Huntingtons Disease Research Roster, founded in 1979 at IU, and the Huntingtons Disease in Venezuela Project database contain information that has proven to be invaluable in the worldwide field of HD research. This paper addresses the types of information stored in each database, the pedigree database program (MEGADATS) used to manage the data, and significant findings that have resulted from access to the data.

Objectives: To determine whether first-year college students cluster in networks based on subjective perceptions of loneliness. Participants: 492 first-year Notre Dame students completed surveys across two semesters and provided communication data used to reconstruct their social networks. Methods: Subjective perceptions of loneliness are measured using the Social and Emotional Loneliness Scale for Adults (SELSA). Correlations between an individual's loneliness and the average loneliness of their alters are compared to associations in random networks created using a rewiring algorithm to determine statistical significance. Results: During their first semester, students are more likely than chance to form ties with other students with similar levels of family and romantic loneliness. In their second semester, students cluster on romantic loneliness but not on family or social loneliness. Conclusions: Students are more likely than chance to form ties with people with similar self-perceived levels of loneliness, but only for certain types of loneliness and during certain periods. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the algorithm of ground reflection regarding highway noise propagation, and evaluates the accuracy of predicted absolute noise levels without barriers of FHWA TNM 2.5. To achieve this purpose, new highway noise prediction program, Highway Noise Predictor (HNP 1.0) with the improved ground reflection algorithm, is compared with TNM 2.5 based on the field measurement data of a point source by Parkin and Scholes and the field measurement on roadway 111 in Indiana State. The findings of the study show that TNM 2.5 over-predicts the absolute noise levels by an average error of 2.0dB(A) compared with field measurements with soft ground, while HNP predicts more accurate absolute noise levels with an average error of 0.5dB(A). This improved performance is due to the improved reflection algorithm deployed in HNP. These improvements in noise prediction accuracy of absolute noise levels projects major implications on highway noise planning. [Copyright &y& Elsevier]

The landscapes across much of the Midwestern United States are characterized by glacial activity that left water‐holding kettles, depressions, and potholes. Until recently, traditional watershed algorithms assumed these depressions to be errors in the elevation data and filled them as a means of correction, when many of these features may rarely fill and have the potential to dramatically affect surface flow patterns. These depressions play an important role in hydrology, water management, site planning, and agronomy. An optimized sequential depression‐filling algorithm (SDFA) was developed which fills these water‐holding features sequentially based on their respective retention capacity and contributing area. Outputs reflect the state of connectivity following the application of a user‐specified amount of rainfall excess (i.e., in excess of infiltration); depressions that have not been filled will remain as their own hydrologically common subcatchments. A performant algorithm is integral to future delivery and utilization by practitioners both in the office and in the field. The optimal set of subroutines were able to fill all of the depressions in a 239 km2 watershed in northeastern Indiana in 42 s and 1.5 h on a consumer desktop computer for digital elevation models (DEMs) at 30 m and 3 m resolutions, respectively (O(n2) overall performance). Plain Language Summary: The landscapes across much of the Midwestern United States are characterized by glacial activity that left numerous water‐holding features. Until recently, traditional watershed algorithms assumed these depressions to be errors in the elevation data and filled them as a means of correction. In reality, many of these features may rarely fill and have the potential to dramatically affect water flow patterns. These depressions play an important role in hydrology, water management, site planning, and agronomy. An improved algorithm was developed which fills these water‐holding features sequentially based on their respective storage capacity and drainage area. The algorithm's outputs reflect the state of the water‐holding feature's connectivity resulting from a given amount of rainfall. This allows depressions to remain which would not ordinarily be filled given this rainfall amount, which differs from existing algorithms. An efficient algorithm is important to future delivery and use by practitioners both in the office and in the field. The algorithm was assessed and able to fill all of the depressions in a 239 km2 watershed in northeastern Indiana in 42 s and 1.5 h on a consumer desktop computer for digital elevation models at 30 m and 3 m resolutions, respectively. Key Points: Developed an algorithm that fills depressions sequentially based on uniformly applied rainfall excessThe algorithm produces depression hierarchies and intermediate states of flow connectivitySignificant rainfall excess was required to reach 100% watershed connectivity on the study area [ABSTRACT FROM AUTHOR]

Capacity constraints and efficiency considerations require that disease management programs identify patients most likely to benefit from intervention. Predictive modeling with available administrative data has been used as a strategy to match patients with appropriate interventions. Administrative data, however, can be plagued by problems of incompleteness and delays in processing. In this article, we examine the effects of these problems on the effectiveness of using administrative data to identify suitable candidates for disease management, and we evaluate various proposed solutions. We build prospective models using regression analysis and evaluate the resulting stratification algorithms using R2 statistics, areas under receiver operator characteristic curves, and cost concentration ratios. We find delays in receipt of data reduce the effectiveness of the stratification algorithm, but the degree of compromise depends on what proportion of the population is targeted for intervention. Surprisingly, we find that supplementing partial data with a longer panel of more outdated data produces algorithms that are inferior to algorithms based on a shorter window of more recent data. Demographic data add little to algorithms that include prior claims data, and are an inadequate substitute when claims data are unavailable. Supplementing demographic data with additional information on self-reported health status improves the stratification performance only slightly and only when disease management is targeted to the highest risk patients. We conclude that the extra costs associated with surveying patients for health status information or retrieving older claims data cannot be justified given the lack of evidence that either improves the effectiveness of the stratification algorithm. ( Population Health Management 2010;13:325-330) [ABSTRACT FROM AUTHOR]

The article describes the research work of Jennifer Neville, assistant professor at the departments of computer science and statistics at Purdue University in West Lafayette, Indiana. Neville's research focuses on the development and analysis of statistical models exploiting statistical dependencies among related instances as well as algorithms to accurately learn the structure and parameters of the said models. Neville was able to develop efficient and accurate learning algorithms for making relational analysis feasible for large-scale relation data sets. She is currently working on the development of methods for dynamic, transactional and multisources networks.