Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristic-Biased Stochastic Sampling

This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the real-world problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search.
Model AI Assignments 2020

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg. edu.
ASYMPTOTIC AND FINITE SAMPLE PROPERTIES OF POOLED TIME SERIES TESTS WITH AN APPLICATION TO THE PPP HYPOTHESIS NEW RESULTS

This paper studies asymptotic and finite sample properties of statistics devised to test for the null of no cointegration in nonstationary pooled time series panels as both the cross section and time series dimensions grow large. The paper finds that for panels with homogenous long run parameters, the spurious regression coefficient estimates become consistent even under the null of no cointegration, and this generates a superconsistency result for panels whereby it becomes irrelevant for the asymptotic distributions whether the residuals are known or are estimated. For heterogenous panels, on the other hand, this asymptotic equivalency does not hold, and the use of stationarity tests that are convergent for raw data can even become divergent when applied to estimated residuals. Furthermore, the direct use of unit root tests for estimated residuals can generate data dependencies that are not present in unit root statistics that are applied to raw panel data. The paper therefore derives asymptotic distributions for panel cointegration statistics that circumvent these problems, and also reports on finite sample properties of these statistics based on Monte Carlo simulations for a varying number of time periods and cross sections. These statistics allow for complete heterogeneity of the dynamics and potential cointegrating relationships across members of the panel. Finally, an empirical application of these panel cointegration statistics is demonstrated for the purchasing power parity hypothesis, which has been difficult to evidence on the basis of conventional single series cointegration tests. 1 PANEL COINTEGRATION; ASYMPTOTIC AND FINITE SAMPLE PROPERTIES OF POOLED TIME SERIES TESTS WITH AN APPLICATION TO THE PPP HYPOTHESIS
Integer and Constraint Programming Revisited for Mutually Orthogonal Latin Squares

In this paper we provide results on using integer programming (IP) and constraint programming (CP) to search for sets of mutually orthogonal latin squares (MOLS). Both programming paradigms have previously successfully been used to search for MOLS, but solvers for IP and CP solvers have significantly improved in recent years and data on how modern IP and CP solvers perform on the MOLS problem is lacking. Using state-of-the-art solvers as black boxes we were able to quickly find pairs of MOLS (or prove their nonexistence) in all orders up to ten. Moreover, we improve the effectiveness of the solvers by formulating an extended symmetry breaking method as well as an improvement to the straightforward CP encoding. We also analyze the effectiveness of using CP and IP solvers to search for triples of MOLS, compare our timings to those which have been previously published, and estimate the running time of using this approach to resolve the longstanding open problem of determining the existence of a triple of MOLS of order ten.
ReLU Input Layer Nodes : 60 X 15 Depth : 2 Output Layer Nodes : 60 X 15 Depth : 1 Fully Con

Many learning tasks combine perceptual and cognitive (e.g. logical, symbolic) sub-tasks. In some cases end-to-end learning of the perceptual and cognitive subtasks can be more difficult than learning each sub-task individually. In other cases such end-to-end learning can be very efficient. As end-to-end learning is becoming more prevalent in AI, it is important to understand when this approach can work efficiently. A toy example is presented: end-to-end visual learning of arithmetic operations from pictures of numbers. The perceptual and cognitive sub-tasks are OCR and arithmetic respectively. The input consists of two pictures, each showing a 7-digit number. The output is the picture of the result of the arithmetic operation (e.g. addition) on the two input numbers. The concept of a number, or of an operator, is not explicitly introduced. Learning is carried out by a deep neural network. In our experiments, a simple network was successful at visual end-to-end learning of addition and subtraction. Other operations, e.g. multiplication, were not learnable by our network, even when each separate sub-task was easily learnable.
Generating Natural-language Video Descriptions Using Text-mined Knowledge Learning Hierarchical Linguistic Descriptions of Visual Datasets Workshop Program Tutorial: Computational Visual Recognition for Nlp Communicating with an Image Retrieval System via Relative Attributes Invited Student Talk: Id

We are interested in the task of image annotation using noisy natural text as training data. An image and its caption convey different information, but are generated by the same underlying concepts. In this paper, we learn latent mixtures of topics that generate image and product descriptions on shopping websites by adapting a topic model for multilingual data (Mimno et al., 2009). We use the trained model to annotate test images without corresponding text. We capture visual properties such as color, texture, shape, and orientation by computing low-level image features, and measure the contribution of each type of visual feature towards the accuracy of the model. Our model significantly outperforms both a competitive baseline and a previous topic model-based system.
Of Information Systems School of Information Systems 7-2013 Technique for Authenticating H . 264 / SVC Streams in Surveillance Applications

Surveillance codestreams coded by H.264/SVC (scalable video coding), which consists of one base layer and one or more enhancement layers, supply flexible and various quality, resolution, and temporal (sub)codestreams such that clients with different network bandwidth and terminal devices can seamlessly access them. In this paper, we present a robust authentication scheme for them in order to insure the integrity of SVC surveillance codestreams, named AUSSC (Authenticating SVC Surveillance Codestreams). AUSSC exploits cryptographic-based authentication for base layer and content-based authentication for enhancement layers. For content-based authentication, AUSSC extracts full features from the first frame of each GOP (group of picture) and partial features from “active” macroblocks of other frames. Performance analysis indicates that AUSSC is robust to content-preserving manipulations and sensitive to content-changing manipulations of enhancement layers. Compared with cryptographic-based and watermarking-based authentication schemes, experimental results show that AUSSC causes less computation complexity and smaller compression overhead. Thus, it appears that AUSSC is suitable for real time SVC surveillance applications.
It Is Not Just What We Say , But How We Say Them : LDA-based Behavior-Topic Model Supplementary Material Minghui

In this paper, we propose an LDA-based [1] behaviortopic model (B-LDA) which jointly models user topic interests and behavioral patterns for micro-blogs like Twitter. Our model is an extendsion of TwitterLDA [2] which is catered for Twitter setting. This document presents the model and discusses related inference details. Note that this is a supplementary material for our paper entitled “It Is Not Just What We Say, But How We Say Them: LDA-based BehaviorTopic Model”.
A New Method for Solving Hard Satisfiability Problems

We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks.
A Real-Time Decision Support System for High Cost Oil Well Drilling Operations

sion support when drilling oil wells. Decisions are supported through analyzing real-time data streams of parameters measured both on the surface and downhole when drilling. The real-time analysis identifies symptoms of problems, which are combined to provide best practices for how to handle the current situation. Verdande Technology has developed DrillEdge to reduce the cost and decrease the probability of failures in oil well drilling. Currently, DrillEdge continuously monitors around 30 oil well drilling operations in parallel for several customers and has been deployed commercially for two years. Verdande Technology’s customers include Baker Hughes, Petroleum Development Oman (PDO), and Shell, among others. In March 2011, Verdande Technology was awarded the Meritous Award for Engineering Excellence for its DrillEdge software platform by E&P Magazine. More and more oil well drilling operators monitor a large part of their drilling operations in real time from off-site real-time operation centers (RTOCs), which are located close to their experienced subject matter experts of various kinds. In this way the operators seek to provide remote support and ensure that knowledge can be transferred between shifts, operations, and regions. Typically, less experienced personnel are located at the drilling rig and more experienced personnel are stationed in the RTOCs. The experiences gained from handling a problem at one Articles
Irrigation water quality effects on soil salinity and crop production in the Powder River Basin, MT

Saline soils occur for the most part in arid or semi-arid regions. Low rainfall, high evapotranspiration, and inadequate leaching cause salts to accumulate in the root zone of soil profiles. Salinity problems of economic significance arise when cultivated soils become saline as a result of irrigation (U.S. Salinity Staff, 1969). One concern of irrigators in the Powder River basin of Montana is that an increase in irrigation water salinity will result in soil degradation and diminished crop production (Gallagher, 1986). A greenhouse study was conducted to determine if crop yields and soil properties were affected under sustained irrigation with water quality of increasing salinity and sodicity. Twenty undisturbed soil columns were obtained from each of six soil sites and leached for 14 months with water quality treatments representing past, present, and future Powder River water quality. Columns were planted to alfalfa (Medicaqo sativa L.) or western wheatgrass (Agrgpyron smithii R.) and harvested on a monthly basis. Soil water samples were periodically collected from soil columns and tested for salinity by EC (electrical conductivity), SAR (sodium adsorption ratio), and pH. Soil columns were then dismantled and sampled at the end of 14 months of continuous irrigation. During the study period alfalfa yields did not significantly decline as a result of irrigation with water of past, present, or future water quality. However, long term irrigation and accelerated salt loading has resulted in significant amounts of salt and sodium accumulation in all soils of this study. All soils became saline or saline-sodic when irrigated with water assumed to be representative of future irrigation water quality. This will likely present a problem to future irrigation management, especially if present irrigation water quality deteriorates further. IRRIGATION WATER QUALITY EFFECTS ON SOIL SALINITY AND CROP PRODUCTION IN THE POWDER RIVER BASIN, ,MT Kathryn S . Thompson A thesis submitted in partial fulfillment of the requirements for the degree of Master'of Seience in ' Soils MONTANA STATE UNIVERSITY Bozeman, Montana August, 1991
Experimental Results on the Crossover Point in Satisfiability Problems

Determining whether a propositional theory is satisfiable is a prototypical example of an NP-complete problem. Further, a large number of problems that occur in knowledge representation, learning, planning, and other areas of AI are essentially satisfiability problems. This paper reports on a series of experiments to determine the location of the crossover point -- the point at which half the randomly generated propositional theories with a given number of variables and given number of clauses are satisfiable -- and to assess the relationship of the crossover point to the difficulty of determining satisfiability. We have found empirically that, for 3-SAT, the number of clauses at the crossover point is a linear function of the number of variables. This result is of theoretical interest since it is not clear why such a linear relationship should exist, but it is also of practical interest since recent experiments [Mitchell et al. 92; Cheeseman et al. 91] indicate that the most computationally difficult problems tend to be found near the crossover point. We have also found that for random 3-SAT problems below the crossover point, the average time complexity of satisfiability problems seems empirically to grow linearly with problem size. At and above the crossover point the complexity seems to grow exponentially, but the rate of growth seems to be greatest near the crossover point.
Syntax-Directed Attention for Neural Machine Translation

Attention mechanism , including global attention and local attention, plays a key role in neural machine translation ( NMT ). Global attention attends to all source words for word prediction. In comparison, local attention selectively looks at ﬁxed-window source words. However, alignment weights for the current target word often decrease to the left and right by linear distance centering on the aligned source position and neglect syntax distance constraints. In this paper, we extend the local attention with syntax-distance constraint, which focuses on syntactically related source words with the predicted target word to learning a more effective context vector for predicting translation. Moreover, we further propose a double context NMT architecture, which consists of a global context vector and a syntax-directed context vector from the global attention, to provide more translation performance for NMT from source representation. The experiments on the large-scale Chinese-to-English and English-to-German translation tasks show that the proposed approach achieves a substantial and signiﬁcant improvement over the baseline system.
On the Subexponential-Time Complexity of CSP

Not all NP-complete problems share the same practical hardness with respect to exact computation. Whereas some NP-complete problems are amenable to efficient computational methods, others are yet to show any such sign. It becomes a major challenge to develop a theoretical framework that is more fine-grained than the theory of NP-completeness, and that can explain the distinction between the exact complexities of various NP-complete problems. This distinction is highly relevant for constraint satisfaction problems under natural restrictions, where various shades of hardness can be observed in practice. Acknowledging the NP-hardness of such problems, one has to look beyond polynomial time computation. The theory of subexponential-time complexity provides such a framework, and has been enjoying increasing popularity in complexity theory. An instance of the constraint satisfaction problem with n variables over a domain of d values can be solved by brute-force in dn steps (omitting a polynomial factor). In this paper we study the existence of subexponential-time algorithms, that is, algorithms running in do(n) steps, for various natural restrictions of the constraint satisfaction problem. We consider both the constraint satisfaction problem in which all the constraints are given extensionally as tables, and that in which all the constraints are given intensionally in the form of global constraints. We provide tight characterizations of the subexponential-time complexity of the aforementioned problems with respect to several natural structural parameters, which allows us to draw a detailed landscape of the subexponential-time complexity of the constraint satisfaction problem. Our analysis provides fundamental results indicating whether and when one can significantly improve on the brute-force search approach for solving the constraint satisfaction problem.
Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming

We consider the linearly constrained separable convex minimization problem whose objective function is separable into m individual convex functions with nonoverlapping variables. A Douglas–Rachford alternating direction method of multipliers (ADM) has been well studied in the literature for the special case of $m=2$. But the convergence of extending ADM to the general case of $m\ge 3$ is still open. In this paper, we show that the straightforward extension of ADM is valid for the general case of $m\ge 3$ if it is combined with a Gaussian back substitution procedure. The resulting ADM with Gaussian back substitution is a novel approach towards the extension of ADM from $m=2$ to $m\ge 3$, and its algorithmic framework is new in the literature. For the ADM with Gaussian back substitution, we prove its convergence via the analytic framework of contractive-type methods, and we show its numerical efficiency by some application problems.
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings

In this paper, we propose a new algorithm for learning general latent-variable probabilistic graphical models using the techniques of predictive state representation, instrumental variable regression, and reproducing-kernel Hilbert space embeddings of distributions. Under this new learning framework, we first convert latent-variable graphical models into corresponding latent-variable junction trees, and then reduce the hard parameter learning problem into a pipeline of supervised learning problems, whose results will then be used to perform predictive belief propagation over the latent junction tree during the actual inference procedure. We then give proofs of our algorithm's correctness, and demonstrate its good performance in experiments on one synthetic dataset and two real-world tasks from computational biology and computer vision - classifying DNA splice junctions and recognizing human actions in videos.
Increasing The Action Gap : New Operators For Reinforcement Learning Supplemental

Vk+1(x)− Vk(x) ≥ γEP1:k [V1(x)− V0(x)] , (1) where P1:k := PkPk−1 . P1 is the k-step transition kernel at x derived from the nonstationary policy πkπk−1 . π1. Let Ṽ (x) := lim supk→∞ Vk(x). We now show that lim infk→∞ Vk(x) = Ṽ (x) also. First note that Conditions 1 and 2, together with the boundedness of V0, ensure that V1 is also bounded and thus ‖V1 − V0‖∞ <∞. By definition, for any δ > 0 and n ∈ N, ∃k ≥ n such that Vk(x) > Ṽ (x)− δ. Since P1:k is a nonexpansion in∞-norm, we have Vk+1(x)− Vk(x) ≥ −γ ‖V1 − V0‖∞ ≥ −γ ‖V1 − V0‖∞ =: − ,
CIVIC : A Hypervisor Based Virtual Computing Environment

The purpose of virtual computing environment is to improve resource utilization by providing a unified integrated operating platform for users and applications based on aggregation of heterogeneous and autonomous resources. With the rapid development in recent years, hypervisor technologies have become mature and comprehensive with four features, including transparency, isolation, encapsulation and manageability. In this paper, a hypervisor based virtual computing infrastructure, named CIVIC, is proposed. Compared with existing approaches, CIVIC may benefit in several ways. It offers separated and isolated computing environment for end users, and realizes hardware and software consolidation and centralized management. Beside this, CIVIC provides a transparent view to upper layer applications, by hiding the dynamicity, distribution and heterogeneity of underlying resources. Performance of the infrastructure is evaluated by an initial deployment and experiment. The result shows that CIVIC can facilitate installation, configuration and deployment of network-oriented applications.
Dominating Manipulations in Voting with Partial Information

We first investigate the following two special cases: (1) the manipulator knows the profile of the other voters, and (2) the manipulator knows nothing about the preferences of the nonmanipulators. The formal case corresponds to I = {{P} : P ∈ Fn}, and the latter case corresponds to I = {Fn}. When the manipulator has full information about the nonmanipulators’ votes, the DOMINANT-MANIPULATION problem reduces to the standard manipulation problem. Therefore, we immediately obtain the following proposition from the Gibbard-Satterthwaite theorem (Gibbard 1973; Satterthwaite 1975).
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation

Although Generative Adversarial Networks have shown remarkable performance in image generation, there are some challenges in image realism and convergence speed. The results of some models display the imbalances of quality within a generated image, in which some defective parts appear compared with other regions. Different from general single global optimization methods, we introduce an adaptive global and local bilevel optimization model(GL-GAN). The model achieves the generation of high-resolution images in a complementary and promoting way, where global optimization is to optimize the whole images and local is only to optimize the low-quality areas. With a simple network structure, GL-GAN is allowed to effectively avoid the nature of imbalance by local bilevel optimization, which is accomplished by first locating low-quality areas and then optimizing them. Moreover, by using feature map cues from discriminator output, we propose the adaptive local and global optimization method(Ada-OP) for specific implementation and find that it boosts the convergence speed. Compared with the current GAN methods, our model has shown impressive performance on CelebA, CelebA-HQ and LSUN datasets.