Miracle-dz/UniFAR-FTUs huggingface dataset:
Isabelle/HOL: A Proof Assistant for Higher-Order Logic

Elementary Techniques. - 1. The Basics. - 2. Functional Programming in HOL. - 3. More Functional Programming. - 4. Presenting Theories. - Logic and Sets. - 5. The Rules of the Game. - 6. Sets, Functions, and Relations. - 7. Inductively Defined Sets. - Advanced Material. - 8. More about Types. - 9. Advanced Simplification, Recursion, and Induction. - 10. Case Study: Verifying a Security Protocol.
YIELD AND ECONOMICS OF RELAY CROPPED LATHYRUS (Lathyrus sativus) AS INFLUENCED BY SEED TREATMENT AND FOLIAR SPRAY OF NUTRIENTS UNDER RAINFED ECOLOGY OF CHHATTISGARH PLAIN

YIELD AND ECONOMICS OF RELAY CROPPED LATHYRUS (Lathyrus sativus) AS INFLUENCED BY SEED TREATMENT AND FOLIAR SPRAY OF NUTRIENTS UNDER RAINFED ECOLOGY OF CHHATTISGARH PLAIN
A reduced amino acid alphabet for understanding and designing protein adaptation to mutation

Short title: Amino acids equivalences within protein structures Amino acids equivalences within protein structures 2 Abstract Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Proteins Blocks. This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analysed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the α-helix Ncap). Some similar associations are found in other reduced amino acid alphabets, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.
Determination of levels of naturally occurring radioactive materials in lagoon samples containing water from oil fields in the State of Kuwait.

An evaluation of the specific activity concentrations associated with technologically enhanced naturally occurring radioactive materials (TENORM) and anthropogenic radionuclides has been undertaken, with particular focus on produced water from the Kuwait oil industry. This work is part of a systematic study to provide a radiological map of the outer boundary of the produced water lagoon located in the Minagish oil field in the south west region of the State of Kuwait. This particular lagoon contains material from the discharge of produced water which is a by-product of oil production in the region. The lagoon samples were prepared and placed into sealed, marinelli beakers for a full gamma-ray spectrometric analysis using a high-resolution, low-background, high-purity germanium detection systems at the University of Surrey Environmental Radioactivity Laboratory. Of particular interest are the calculation of the activity concentrations associated with members of the decay chains following decays of the primordial radionuclides of the 238U chain (226Ra, 214Pb, 214Bi) and the 232Th chain (228Ra, 228Ac, 212Pb, 212Bi, 208Tl), and the enhanced concentrations of radium isotopes. This thesis presents overview of the experimental samples which have been measured and the analysis techniques applied, including isotopic correlation plots across the sample region. The result shows the expected significant increase in 226Ra (and progeny) concentrations compared to the NORM values were taken from outside of the lagoon, with 226Ra activity concentrations ranging from approximately 10 Bq/kg to 2 kBq/kg from different samples. Radium equivalent and hazard indices are calculated for different regions of the pool boundary, with some regions have hardard indices greater than unity, and therefore constituting a potential radiological hazard. The analysis also investigates the correlations between members of the 232Th and 238U chain in these samples, and shows evidence for enhancement of radium isotopes (226Ra and 228Ra) in the lagoon produced water samples compared to the background levels.
Beyond the "best" match: machine learning annotation of protein sequences by integration of different sources of information

MOTIVATION Accurate automatic assignment of protein functions remains a challenge for genome annotation. We have developed and compared the automatic annotation of four bacterial genomes employing a 5-fold cross-validation procedure and several machine learning methods. RESULTS The analyzed genomes were manually annotated with FunCat categories in MIPS providing a gold standard. Features describing a pair of sequences rather than each sequence alone were used. The descriptors were derived from sequence alignment scores, InterPro domains, synteny information, sequence length and calculated protein properties. Following training we scored all pairs from the validation sets, selected a pair with the highest predicted score and annotated the target protein with functional categories of the prototype protein. The data integration using machine-learning methods provided significantly higher annotation accuracy compared to the use of individual descriptors alone. The neural network approach showed the best performance. The descriptors derived from the InterPro domains and sequence similarity provided the highest contribution to the method performance. The predicted annotation scores allow differentiation of reliable versus non-reliable annotations. The developed approach was applied to annotate the protein sequences from 180 complete bacterial genomes. AVAILABILITY The FUNcat Annotation Tool (FUNAT) is available on-line as Web Services at http://mips.gsf. de/proj/funat.
Exact marginal prior distributions of ﬁnite Bayesian neural networks

Bayesian neural networks are theoretically well-understood only in the inﬁnite-width limit, where Gaussian priors over network weights yield Gaussian priors over network outputs. Recent work has suggested that ﬁnite Bayesian networks may outperform their inﬁnite counterparts, but their non-Gaussian function space priors have been characterized only though perturbative approaches. Here, we derive exact solutions for the function space priors for individual input examples of a class of ﬁnite fully-connected feedforward Bayesian neural networks. For deep linear networks, the prior has a simple expression in terms of the Meijer G -function. The prior of a ﬁnite ReLU network is a mixture of the priors of linear networks of smaller widths, corresponding to different numbers of active units in each layer. Our results unify previous descriptions of ﬁnite network priors in terms of their tail decay and large-width behavior.
Goal-Directed Decision Making: Computations and Neural Circuits

It is said that the only constant in life is change. We are routinely faced with different situations, no two exactly alike. We visit new places, try new foods, meet new people, find new jobs, and invent new products. You have probably never read this very sentence before. The pervasiveness of novelty can be paralyzing if one is not prepared for it. By their nature, unfamiliar situations challenge our ability to draw on past experience. And by our nature, humans make do. In general, how do we appraise courses of action in various contexts? We could form projections of what’s likely to happen as a result of each action in a context and combine that with an evaluation of how desirable those outcomes are. Or we could lean on the automatic attitudes drilled into us by extensive experience. These are the two prevailing theories in neuroeconomics, expressed mathematically in terms of expected utility and reinforcement learning. The biggest success story of decision neuroscience to date has been in uncovering neural instantiations of these decision-making rules. How might these two systems go awry when there is little direct experience to work from? The former relies heavily on a cognitive map or mental structure but does not have much to hang its structure on. The latter depends vitally on preexisting experience, but this direct experience is unavailable. The capacity to cope in new circumstances is an important but tricky skill. A plausible alternative is to recall how well or poorly similar actions turned out in similar contexts in the past. Such an approach to decision-making enables us to draw on the variety of disparate experiences we acquire over time and respond gracefully to the novelty and complexity that pervades real life. It imposes fewer assumptions about the structure of the world compared with sophisticated probabilistic judgments, while squeezing more information out of background knowledge than simple value caching. I lay out a “case-based” system combining theory and empirical evidence from economics, psychology, neuroscience, statistics, and computer science. Value judgment by similarity corresponds to an economic model called case-based decision theory (CBDT), inspired in part by a computational problem-solving process known as case-based reasoning (CBR). This theory has links to nonparametric statistics, suggesting why and when the system works well. Recent evidence from neuroscience indicates that we use this kind of
A one‐dimensional eco‐geomorphic model of marsh response to sea level rise: Wind effects, dynamics of the marsh border and equilibrium

[1] In this study we investigate the long term evolutionary trend of tidal marshes and their possible tendency to approach equilibrium. We account of the dynamic interaction between the marsh and its adjacent environment, allowing for both variations of marsh elevation and displacement of the marsh boundary. We thus consider a 1-D configuration consisting of a tidal channel merging into a 1-D marsh. Starting from some initial configuration of the channel we model how a salt marsh forms at the landward end of the channel and determine the long term evolution of the channel - marsh configuration under different scenarios of relative sea level rise. Previously established results on the morphodynamic evolution of a tidal channel are extended accounting for the effects of vegetation and wind driven sediment resuspension in regions where tidal stresses are too weak to mobilize sediments. Results suggest that, for sufficiently low rates of relative sea level rise the marsh platform may be able to reach an equilibrium elevation, provided wind resuspension is able to maintain a sufficiently large sediment concentration close to the marsh boundary. This is in general agreement with recent results based on zero dimensional modeling. However, we find that the marsh boundary is unstable, as progradation or retreat is generally experienced depending on a delicate balance between tidal transport and transport driven by wind setup. In this sense, actual morphodynamic equilibrium is a rather exceptional and unstable state.
Bulletin of Electrical Engineering and Informatics

Aziemah Athirah As’ari, Nur Ilyana Anwar Apandi, Nor Aishah Muhammad, Rozeha A. Rashid, Mohd Adib Sarijari, Jamaliah Salleh Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Melaka, Malaysia Telecommunication Software and Systems (TeSS) Research Group, Department of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Wireless Communication Centre, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Suboptimal Schemes for Retrospective Data Assimilation Based on the Fixed Lag Kalman Smoother

The xed lag Kalman smoother was proposed recently as a framework for providing retrospective data assimilation capability in atmospheric reanalysis projects Cohn et al Mon Wea Rev Retrospective data assimilation refers to the dynamically consistent incorporation of data observed well past each analysis time into each analysis Like the Kalman lter the xed lag Kalman smoother requires statistical information that is not available in practice and involves an excessive amount of computation if implemented by brute force and must therefore be approximated sensibly to become feasible for operational use In this article the performance of suboptimal retrospective data assimilation systems RDASs based on a variety of approximations to the optimal xed lag Kalman smoother is evaluated Since the xed lag Kalman smoother formula tion employed in this work separates naturally into a Kalman lter portion and an optimal retrospective analysis portion two suboptimal strategies are considered i viable approximations to the Kalman lter portion coupled with the optimal retrospective analysis portion and ii viable approximations to both portions These two strategies are studied in the context of a linear dy namical model and observing system since it is only under these circumstances that performance can be evaluated exactly A shallow water model linearized about an unstable basic ow is used for this purpose Results indicate that retrospective data assimilation can be successful even when simple ltering schemes are used such as one resembling current oper ational statistical analysis schemes In this case however on line adaptive tuning of the forecast error covariance matrix is necessary The performance of this RDAS is similar to that of the Kalman lter itself More sophisticated ap proximate ltering algorithms such as ones employing singular values vectors of the propagator or eigenvalues vectors of the error covariances as a way to account for error covariance propagation lead to even better RDAS perfor mance Approximating both the lter and retrospective analysis portions of the RDAS is also shown to be an acceptable approach in some cases
On the Role of the Eastern Pacific Teleconnection in ENSO Impacts on Wintertime Weather over East Asia and North America

Previous studies have mainly focused on the influence of El Niño–Southern Oscillation (ENSO) on seasonal-mean conditions over East Asia and North America. This study, instead, proposes an ENSO pathway that influences the weather events over East Asia and North America, in which the eastern Pacific teleconnection pattern (EP) plays an important role. On the one hand, the EP pattern can induce significant surface temperature anomalies over East Asia during its development and mature stages, with the positive (negative) EPs causing colder (warmer) than normal weather events. Besides, the frequency of occurrence of EPs is significantly modulated by ENSO, with 50% of the positive EPs occurring in La Niña winters, and 47% of the negative EPs occurring in El Niño winters. As a result, in El Niño winters, more negative and fewer positive EPs tend to occur, and thus more warm and fewer cold weather events are expected in East Asia. For La Niña winters, the reverse is true. On the other hand, for the EP pattern without its canonical convection pattern (referred to as the nonconvective EP), extremely cold anomalies over the northern United States and western Canada are induced in its negative phase. Moreover, when there are positive sea surface temperature anomalies in the central equatorial Pacific, the frequency of occurrence of negative nonconvective EPs is 2.0 times greater than the climatological value, and thus an enhanced likelihood of extremely cold spells over North America may be expected.
Tidal Mixing Sustains a Bottom-Trapped River Plume and Buoyant Coastal Current on an Energetic Continental Shelf

Conventional wisdoms on river plume dynamics suggest that a down-shelf buoyant coastal current will ultimately be trapped at a specific depth, that is, the trapping depth, as constrained by riverine outflow and offshore bottom Ekman transport. Theoretically, a prerequisite down-shelf current is necessary to form a stable bottom-trapped river plume. In this study an alternative is described by carrying out a modeling study on the Zhe-Min Coastal Current (ZMCC). Buoyant water from the Changjiang River is a major factor driving the ZMCC, as is common in bottom-trapped river plumes; however, the trapping depth is more determined by tidal mixing. When the plumewater comes to the sloping topography, strong tidal mixing induces a mixing front, shoreward of which the bottom Ekman layer occupies the entire water column. Such a tidal-induced front maintains a down-shelf frontal current, which is intensified both at the surface due to the thermal wind balance and on the top of bottom boundary layer due to the tidal rectification. Direct wind-induced transport only covers a small fraction of the ZMCC; however, it redistributes the plume water and, thus, affects the coastal current. The tide-induced frontal trapping depth varies much less between seasons than that predicted by previous plume theories. Instead, it fluctuates strongly in the spring-neap cycle. Even in summer when upwelling-favorable winds prevail, the mixing front still sustains a down-shelf coastal current. Intense tidal mixing exists in many coastal waters, which might be an alternative mechanism in forming bottom-trapped river plumes and their associated buoyant coastal current. Plain Language Summary Large rivers, such as the Amazon, Mississippi, and Changjiang, export a huge amount of terrestrial materials to the receiving waterbodies. Typically, the lighter riverine freshwater mixes with the denser ambient seawater and then propagates rightward along the coast (in the Northern Hemisphere), which is known as the buoyant coastal current. Large river-induced buoyant coastal current can propagate hundreds of kilometers. It frequently causes harmful algal blooms, hypoxia, and other environmental problems. Therefore, understanding the formation mechanisms of buoyant coastal currents is very important. Conventional theories on buoyant coastal currents are very successful; however, they rarely consider the effect of tidal mixing, which exists ubiquitously in coastal waters. Here in this study, we investigated this issue by using the Changjiang River-induced buoyant coastal current as an example, through a series of well-designed numerical experiments. The results showed that tidal mixing plays an essential role in maintaining a stable buoyant coastal current under unfavorable wind conditions. Without the tidal mixing, the stable buoyant coastal current can hardly exist. The finding of this study can promote our understandings on the coastal dynamic processes and other relevant processes.
Lateral Response of a Single Pile under Combined Axial and Lateral Cyclic Loading in Sandy Soil

According to practical situation, there have been limited investigations on the response of piles subjected to combined loadings especially when subjected to cyclic lateral loads. Those few studies led to contradictory results with regard to the effects of vertical loads on the lateral response of piles. Therefore, a series of experimental investigation into piles in dense sand subjected to combination of static vertical and cyclic lateral loading were conducted with instrumented model piles. The effect of the slenderness ratio (L/D) was also considered in this study (i.e. L/D= 25 and 40). In addition, a variety of two-way cyclic lateral loading conditions were applied to model piles using a mechanical loading system. One hundred cycles were used in each test to represent environmental loading such as offshore structures. It was found that under combined vertical and cyclic lateral loads the lateral displacement of piles decreased with an increase in vertical load whereas it causes large vertical displacements at all slenderness ratios. In addition, for all loading conditions the lateral, vertical (settlement and upward) displacements and bending moments increased as either the magnitude of cyclic load or the number of cycles increases.
Automatic Identification of Arabic Dialects USING Hidden Markov Models

The Arabic language has many different dialects, they must beidentified before Automatic Speech Recognition can take place.This thesis examines the difficult task of properly identifyingvarious Arabic dialects. We also present a novel design of anArabic dialect identification system using Hidden Markov Models(HMM). Due to the similarities and the differences between Arabicdialects, we build a ergodic HMM that has two types of states; oneof them represents the common sounds across Arabic dialects, whilethe other represents the unique sounds of the specific dialect. Wetie the common states across all models since they share the samesounds. We focus only on two major dialects: Egyptian and theGulf. An improved initialization process is used to achieve betterArabic dialect identification. Moreover, we utilize many differentcombinations of speech features related to MFCC such as timederivatives, energy, and the Shifted Delta Cepstra in training andtesting the system. We present a detailed comparison of theperformance of our Arabic dialect identification system using thedifferent combinations. The best result of the Arabic dialectidentification system is 96.67\% correct identification.
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

Recently, there has been growing interest in solving consensus optimization problems in a multiagent network. In this paper, we develop a decentralized algorithm for the consensus optimization problem $\mathrm{minimize}_{x\in\mathbb{R}^p}~\bar{f}(x)=\frac{1}{n}\sum_{i=1}^n f_i(x),$ which is defined over a connected network of $n$ agents, where each function $f_i$ is held privately by agent $i$ and encodes the agent's data and objective. All the agents shall collaboratively find the minimizer while each agent can only communicate with its neighbors. Such a computation scheme avoids a data fusion center or long-distance communication and offers better load balance to the network. This paper proposes a novel decentralized exact first-order algorithm (abbreviated as EXTRA) to solve the consensus optimization problem. “Exact” means that it can converge to the exact solution. EXTRA uses a fixed, large step size, which can be determined independently of the network size or topology. The local variable of every a.
Using Live Modeling to Train Preservice Teachers to Integrate Technology Into Their Teaching

USING LIVE MODELING TO TRAIN PRESERVICE TEACHERS TO INTEGRATE TECHNOLOGY INTO THEIR TEACHING Richard E. West Department of Instructional Psychology and Technology Master of Science Many researchers feel that teacher preparation programs are not doing enough to prepare teachers to effectively use technology. The result is a plethora of teachers who may know the basic functions of different programs, but who are unprepared to integrate these skills into their teaching. One method used by a few preservice programs, including BYU’s, is the use of modeling sessions, otherwise referred to as live modeling. In these modeling sessions, the instructor models for the preservice teachers how a K-12 teacher could teach with technology, while the preservice teachers participate as if they were K12 students. This thesis is a qualitative investigation of how this method of live modeling has impacted students, according to the perceptions of a sample of former students of the course. This project also has a practical focus of identifying strategies for improving modeling, and pitfalls that may indicate when modeling is not as effective. Overall, this study found that modeling was perceived by most students to be effective at teaching technology skills and ideas for integrating technology as teachers.