Miracle-dz/UniFAR-FTUs huggingface dataset:
Lie Transform--based Neural Networks for Dynamics Simulation and Learning

In the article, we discuss the architecture of the polynomial neural network that corresponds to the matrix representation of Lie transform. The matrix form of Lie transform is an approximation of the general solution of the nonlinear system of ordinary differential equations. The proposed architecture can be trained with small data sets, extrapolate predictions outside the training data, and provide a possibility for interpretation. We provide a theoretical explanation of the proposed architecture, as well as demonstrate it in several applications. We present the results of modeling and identification for both simple and well-known dynamical systems, and more complicated examples from price dynamics, chemistry, and accelerator physics. From a practical point of view, we describe the training of a Lie transform--based neural network with a small data set containing only 10 data points. We also demonstrate an interpretation of the fitted neural network by converting it to a system of differential equations.
The emergence of one-dimensional channels in marginal-angle twisted bilayer graphene

We generalize the continuum model for Moiré structures made from twisted graphene layers, in order to include lattice relaxation and the formation of channels at very small (marginal) twist angles. We show that a precise description of the electronic structure at such small angles can be achieved by (i) calculating first the relaxed atomic structure, (ii) projecting the interlayer electronic hopping parameters using a suitable basis of Bloch states, and (iii) increasing the number of harmonics in the continuum approximation to interlayer hopping. The results show a complex structure of quasi one dimensional states when a finite bias is applied.
Distributed Edge Partitioning for Trillion-edge Graphs

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partitioning quality. Distributed NE is based on a new heuristic, called parallel expansion, where each partition is constructed in parallel by greedily expanding its edge set from a single vertex in such a way that the increase of the vertex cuts becomes local minimal. We theoretically prove that the proposed method has the upper bound in the partitioning quality. The empirical evaluation with various graphs shows that the proposed method produces higher-quality partitions than the state-of-the-art distributed graph partitioning algorithms. The performance evaluation shows that the space efficiency of the proposed method is an order-of-magnitude better than the existing algorithms, keeping its time efficiency comparable. As a result, Distributed NE can partition a trillion-edge graph using only 256 machines within 70 minutes.
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation

Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random Field (CRF) model. The two GANs aim at generating distinct and complementary disparity maps and at improving the generation quality via exploiting the adversarial learning strategy. The deep CRF coupling model is proposed to fuse the generative and discriminative outputs from the dual GAN nets. As such, the model implicitly constructs mutual constraints on the two network branches and between the generator and discriminator. This facilitates the optimization of the whole network for better disparity generation. Extensive experiments on the KITTI, Cityscapes, and Make3D datasets clearly demonstrate the effectiveness of the proposed approach and show superior performance compared to state of the art methods. The code and models are available at https://github.com/mihaipuscas/3dv—coupled-crf-disparity
Effects of cold acclimation on proteome expression patterns related to freezing tolerance in a Tibetan alpine plant Saussurea laniceps.

Saussurea laniceps is a perennial herbal alpine plant possessing strong cold tolerance. To get a deeper insight into its cold tolerance mechanisms, freezing tolerance and the proteomic profiles of cold-acclimated S. laniceps plantlets were analyzed. The survival rate of plantlets with height of 8-9 cm were recorded after exposure to chilling stress (2 degrees C) and compared to plant material kept at a control temperature (23 degrees C). The survival percent at -10degreesC increased from 0% to 40% during the 12 d of acclimation. Proteomic analyses, by two-dimensional gel electrophoresis (2-DE), performed during this stage revealed that 9 proteins were up-regulated, while 5 proteins were down-regulated. Among the proposed accumulating or appearing proteins, chlorophyll a-b binding protein 2 (LHCP-2), ribulose-1, 5-bisphosphate carboxylase/oxygenase activase (RCA), 33kDa manganese stabilizing chloroplast protein (33kDa MSP), and oxygen-evolving complex protein 1(OEC-1) were all related to photosynthesis, Maturase K is involved in gene expression regulation and galactinol synthase (GS) related to raffinose synthesis. Cold exposure induced a decrease in the candidate proteins including microtubule plus-end binding protein, ribosomal protein S13, O-acetylserine (thiol) lyase and photosystem I assembly protein ycf4. These results suggest changes in proteins associated with energy production, microtubule dynamic, raffinose synthesis and gene expression regulation process allow S. laniceps to enhance its freezing tolerance in the chilling environment.
Stance Detection Attending External Knowledge from Wikipedia

: This paper presents a novel approach to stance detection for unseen topics that takes advantage of external knowledge about the topics. We build a new stance detection dataset consisting of 6,701 tweets on seven topics with associated Wikipedia articles. An analysis of this dataset conﬁrms the necessity of external knowledge for this task. This paper also presents a method of extracting related concepts and events from Wikipedia articles. To incorporate this extracted knowledge into stance detection, we propose a novel neural network model that can attend to such related concepts and events when encoding the given text using bi-directional long short-term memories. Our experimental re-sults demonstrate that the proposed method, using knowledge extracted from Wikipedia, can improve stance detection performance.
SEAL: Attack Mitigation for Encrypted Databases via Adjustable Leakage

Building expressive encrypted databases that can scale to large volumes of data while enjoying formal security guarantees has been one of the holy grails of security and cryptography research. Searchable Encryption (SE) is considered to be an attractive implementation choice for this goal: It naturally supports basic database queries such as point, join, group-by and range, and is very practical at the expense of well-defined leakage such as search and access pattern. Nevertheless, recent attacks have exploited these leakages to recover the plaintext database or the posed queries, casting doubt to the usefulness of SE in encrypted systems. Defenses against such leakage-abuse attacks typically require the use of Oblivious RAM or worst-case padding—such countermeasures are however quite impractical. In order to efficiently defend against leakage-abuse attacks on SE-based systems, we propose SEAL, a family of new SE schemes with adjustable leakage. In SEAL, the amount of privacy loss is expressed in leaked bits of search or access pattern and can be defined at setup. As our experiments show, when protecting only a few bits of leakage (e.g., three to four bits of access pattern), enough for existing and even new more aggressive attacks to fail, SEAL query execution time is within the realm of practical for real-world applications (a little over one order of magnitude slowdown compared to traditional SE-based encrypted databases). Thus, SEAL could comprise a promising approach to build efficient and robust encrypted databases.
A generic construction for revocable identity-based encryption with subset difference methods

To deal with dynamically changing user’s credentials in identity-based encryption (IBE), providing an efficient key revocation method is a very important issue. Recently, Ma and Lin proposed a generic method of designing a revocable IBE (RIBE) scheme that uses the complete subtree (CS) method by combining IBE and hierarchical IBE (HIBE) schemes. In this paper, we propose a new generic method for designing an RIBE scheme that uses the subset difference (SD) method instead of using the CS method. In order to use the SD method, we generically design an RIBE scheme by combining IBE, identity-based revocation (IBR), and two-level HIBE schemes. If the underlying IBE, IBR, and HIBE schemes are adaptively (or selectively) secure, then our RIBE scheme is also adaptively (or selectively) secure. In addition, we show that the layered SD (LSD) method can be applied to our RIBE scheme and a chosen-ciphertext secure RIBE scheme also can be designed generically.
Open Archive Toulouse Archive

Both multi-criteria recommendation and context-aware recommendation are well addressed in previous research but separately in most of existing work. In this paper, we aim to contribute to the under-explored research problem which consists in tailoring the multi-criteria rating predictions to users involved in speciﬁc contexts. We investigate the application of simultaneous clustering based on the application of a spectral partitioning graph method over situational contexts in the one hand and criteria in the other hand. Besides, we conjecture that even with similar criteria-related ratings, the importance of criteria might differ among users. This idea leads us to use prioritized aggregation operators as means of multi-criteria rating aggregations. Our experimental results on a real-world dataset show the eﬀectiveness of our approach.
Scope-aware Code Completion with Discriminative Modeling

: Code completion is a traditional popular feature for API access in integrated development environments (IDEs). It not only frees programmers from remembering speciﬁc details about an API but also saves keystrokes and corrects typographical errors. Existing methods for code completion usually suggest APIs based on statistics in code bases described by language models. However, they neglect the fact that the user’s input is also very useful for ranking, as the underlying patterns can be used to improve the accuracy of predictions of intended APIs. In this paper, we propose a novel method to improve the quality of code completion by incorporating the users’ acronym-like input conventions and the APIs’ scope context into a discriminative model. The users’ input conventions are learned using a logistic regression model by extracting features from collected training data. The weights in the discriminative model are learned using a support vector machine (SVM). To improve the real-time e ﬃ ciency of code completion, we employ a trie to index and store the scope context information. An e ﬃ cient top-k algorithm is developed. Experiments show that our proposed method outperforms the baseline methods in terms of both e ﬀ ectiveness and e ﬃ ciency.
Co-ingestion of whole almonds and almond oil with carbohydrate suppresses postprandial glycaemia in mice in an insulin-dependent and insulin-independent manner

Abstract Co-ingestion of almonds with carbohydrate prevents excessive increase in plasma glucose level (PGL), but information about the functional fraction is limited. Identifying the functional fraction is necessary to use almonds more efficiently in terms of controlling postprandial glycaemia after a high-carbohydrate meal. In the present study, we evaluated the effects of almond skin, oil, water-soluble fraction and water-insoluble fraction on both postprandial glycaemia and insulinaemia. The effect of almond skin was tested by comparing the effect of whole almonds with the effect of skinless almonds. Male ICR mice were administered dextrin and 4 g/kg body weight test samples. After the administration, 2-h postprandial changes in glycaemia and insulinaemia were measured. Oil was the only fraction being able to blunt postprandial glycaemia. Interestingly, when co-ingesting with dextrin, almond oil did not change the insulin level compared with the control but whole almonds or skinless almonds triggered a 4-fold increase in insulin level. The co-ingestion of whole almonds or skinless almonds similarly suppressed the PGL at 15 and 30 min (P < 0·05), which means almond skin has no effect on postprandial glycaemia. Neither soluble nor insoluble fractions lead to any significant changes in postprandial glycaemia and insulinaemia. In conclusion, oil is the main functional component accounting for the glycaemia-lowering effect without altering insulin level.
On nonary cubic forms.

is that the form /(x) have a non-trivial zero in every p-adic field Qp. There being no other apparent barrier to the solubility of (1), it has been widely supposed that this condition is also sufficient provided that the number of indeterminates in the form be not very small. In particular, therefore, it is conjectured that (1) is non-trivially soluble whenever n ̂ 10, since in that case it has been proved by Demyanov, Lewis, and Davenport that the condition is always met. Indeed, in view of the recent important work of Heath-Brown [8], the latter conjecture has been raised to the Status of a theorem in the most significant case where the form is non-singular, earlier work by Lewis [14], Birch [1], and Davenport [3], [4], [5] having progressively reduced to sixteen the value of n needed for general forms.
On a partition theorem of Göllnitz.

Many classical partition identities state that for each positive integer n, the partitions of n with parts restricted to certain residue classes are equinumerous with the partitions of n on which certain difference conditions are imposed. Most prominent among these are Euler's identity: partitions into odd parts are equinumerous with partitions into distinct parts, and the Rogers-Ramanujan identities: for r= l or 2, partitions into parts congruent to ± r (mod 5) are equinumerous with partitions into parts with minimal difference two and smallest part greater than or equal to r. Less well known, but no less interesting, are the following theorems due to H. Göllnitz [4]:
Modeling Jump and Continuous Components in the Volatility of Oil Futures

In this study, we use the ‘heterogeneous autoregressive’ (HAR) model and replace all squared returns with a squared range to estimate realized range-based volatility (RRV) forecasts for oil futures prices. Our findings demonstrate that the HAR-RRV models, involving volatility measures with a realized range-based estimator, successfully capture the long-term memory behavior of volatility in oil futures contracts. We find that realized range-based bi-power variation (RBV), which is also immune to jumps, is a better regressor for future volatility prediction, significantly outperforming the AR model. Similar to the findings for financial markets, we also find that the jump components of RRV have little predictive power for oil futures contracts. ∗We thank the editor, Bruce Mizrach, and an anonymous reviewer for helpful comments. Any remaining errors are our responsibility. Huimin Chung would like to gratefully acknowledge the financial support provided by the MoE ATU Plan of the National Chiao Tung University and National Science Council, Taiwan.
Discovering Hidden Structures Using Mixture Models: Application to Nonlinear Time Series Processes

This paper discuses the application of mixture models for analyzing data with hidden structure. More specifically, we are interested in situations where the structure is due to some unobserved factors. Detecting the latent structures in data is not trivial. On the other hand, since such structures could obscure the effect of the observed variables, ignoring them (e.g., using simple linear models) could result in wrong inference. To address this issue, we propose to use Dirichlet process mixtures. In this approach, the mixture identifier (i.e., the latent variable that assigns each component to a data point) acts as a surrogate for possible missing factors and provides insight into possible hidden structure in the data. An illustrative example is provided to show the usefulness of this approach. We also discuss the application of our method for analyzing time series processes that are subject to regime changes where there is no specific economic theory about the structure of the model. ∗The author would like to thank Radford Neal for helpful discussion.
Extracting ontological knowledge from Java source code using Hidden Markov Models

Abstract Ontologies have become a key element since many decades in information systems such as in epidemiological surveillance domain. Building domain ontologies requires the access to domain knowledge owned by domain experts or contained in knowledge sources. However, domain experts are not always available for interviews. Therefore, there is a lot of value in using ontology learning which consists in automatic or semi-automatic extraction of ontological knowledge from structured or unstructured knowledge sources such as texts, databases, etc. Many techniques have been used but they all are limited in concepts, properties and terminology extraction leaving behind axioms and rules. Source code which naturally embed domain knowledge is rarely used. In this paper, we propose an approach based on Hidden Markov Models (HMMs) for concepts, properties, axioms and rules learning from Java source code. This approach is experimented with the source code of EPICAM, an epidemiological platform developed in Java and used in Cameroon for tuberculosis surveillance. Domain experts involved in the evaluation estimated that knowledge extracted was relevant to the domain. In addition, we performed an automatic evaluation of the relevance of the terms extracted to the medical domain by aligning them with ontologies hosted on Bioportal platform through the Ontology Recommender tool. The results were interesting since the terms extracted were covered at 82.9% by many biomedical ontologies such as NCIT, SNOWMEDCT and ONTOPARON.
Spiky density of states in large complex Al–Mn phases

First-principle electronic structure calculations have been performed in crystalline complex phases mAl4:12Mn and l-Al4:6Mn using the TB-LMTO method. The atomic structures contain about 560 atoms in a large hexagonal unit cell. One of the main characteristic of their density of states is the presence of fine peaks the so-called “spiky structure”. From multiple-scattering calculations in real space, we show that these fine peaks are not artifacts in ab-initio calculations, since they result from a specific localization of electrons by atomic clusters of different length scales.
On the intervals between numbers that are sums of two squares: IV.

Plaksin ([7], [8]) and Harman [2] in now considering how small h can be taken under the relaxed requirement that (0) hold for almost all values of x. In the revised Situation the condition A/log* -> oo was shewn to be sufficient by the latter author, who reported Professor R. C. Baker's observations about the flaws in the former's work. Now, however, by a different method from that used by Harman, we shew it suffices that A/j/logx -» oo, a result that is almost certainly best possible because it is to be conjectured that
Skin impedance

Most measurements in bioimpedance yield a fixed reproducible result. But I always liked to work on bioimpedance of the skin because it is so challenging. It is important to know the impedance of the skin when measuring the electrocardiogram because if your amplifier input impedance is not high enough, undesirable attenuation will occur. A typical skin impedance is about 500 kΩ for 1 cm. But Rosell et al. [1] showed that at 1 Hz there is a wide spread for different subjects from 10 kΩ to 1 MΩ. And as the frequency increases to 1 MHz, impedance decreases to about 300 Ω. Then impedance changes with time. Olson et al. [2] showed that impedance decreases steadily or exponentially with time constants of several hours. Lozano et al. [3] note that impedance varies with body location and is much lower on the tongue than elsewhere on dry skin. Bahr et al. [4] show that impedance decreases from 1 MΩ to 200 kΩ in 1 min during a hot flash. De Talhouet et al. [5] showed that impedance drops from 500 kΩ to 5 kΩ after 12 skin strippings using Scotch tape. If you calculate the skin impedance under a defibrillator electrode you get 500 kΩ/100 cm = 5 kΩ. But after defibrillation if you remeasure you get 50 Ω. What has happened is the high defibrillator voltage has arced through the skin and created many holes that lower the impedance and redden the skin. There are a variety of subject areas if you want to further explore skin impedance.
Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

Abstract The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013 and was validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).
Simple and convenient two step synthesis of 5-bromo-2,3-dimethoxy-6-methyl-1,4-benzoquinone

Abstract 5-bromo-2,3-dimethoxy-6-methyl-1,4-benzoquinone 3, a key intermediate for preparing coenzyme Q compounds, was readily synthesized in two steps by a reaction sequence starting from the commercially available 3,4,5-trimethoxytoluene 1 via bromination and oxidation reactions. Persulfate salts were first employed as oxidants to synthesize 1,4-benzoquinone, the overall yield of title compound 3 was 65%.
Refinement of crystal structure of β-phenylglutaric acid, C11H12O4

C11H12O4, orthorhombic, Pbca (no. 61), a = 10.469(2) Â, b = 7.153(1) Â ,c = 26.363(5) A, V= 1974.2 Â 3 , Z = 8, Rgt(F) = 0.058, wR^F) = 0.153, T= 293 K.
The Purpose of Remittances: Evidence from Germany

Summary This paper examines the purpose of remittances using individual data of migrants in Germany. Particular attention is paid to migrants’ savings and transfers to family members in the home country. Our findings indicate that migrants who intend to stay in Germany only temporarily have a higher propensity to save and save larger amounts in their home country than permanent migrants. A similar picture emerges when considering migrants’ payments to family members abroad. The results of a decomposition analysis indicate that temporary and permanent migrants seem to have different preferences towards sending transfers abroad, while economic characteristics and the composition of households in home and host countries are less relevant.