Candidate Space Research Articles

Algorithms for optimization of branching gravity-driven water networks

Abstract. The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.

Constructive Preference Elicitation Over Hybrid Combinatorial Spaces

Peference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.

Geometric Approach to the Realization of Planar Elastic Behaviors With Mechanisms Having Four Elastic Components

This paper addresses the passive realization of any selected planar elastic behavior with redundant elastic manipulators. The class of manipulators considered are either serial mechanisms having four compliant joints or parallel mechanisms having four springs. Sets of necessary and sufficient conditions for mechanisms in this class to passively realize an elastic behavior are presented. The conditions are interpreted in terms of mechanism geometry. Similar conditions for nonredundant cases are highly restrictive. Redundancy yields a significantly larger space of realizable elastic behaviors. Construction-based synthesis procedures for planar elastic behaviors are also developed. In each, the selection of the mechanism geometry and the selection of joint/spring stiffnesses are completely decoupled. The procedures require that the geometry of each elastic component be selected from a restricted space of acceptable candidates.

Geometric Construction-Based Realization of Spatial Elastic Behaviors in Parallel and Serial Manipulators

This paper addresses the realization of spatial elastic behavior with a parallel or a serial manipulator. Necessary and sufficient conditions for a manipulator (either parallel or serial) to realize a specific elastic behavior are presented and interpreted in terms of the manipulator geometry. These conditions completely decouple the requirements on component elastic properties from the requirements on mechanism kinematics. New construction-based synthesis procedures for spatial elastic behaviors are developed. With these synthesis procedures, one can select each elastic component of a parallel (or serial) mechanism based on the geometry of a restricted space of allowable candidates. With each elastic component selected, the space of allowable candidates is further restricted. For each stage of the selection process, the geometry of the remaining allowable space is described.

Alchemical Predictions for Computational Catalysis: Potential and Limitations.

Kohn-Sham density functional theory (DFT) is the workhorse method for calculating adsorbate binding energies relevant for catalysis. Unfortunately, this method is too computationally expensive to methodically and broadly search through catalyst candidate space. Here, we assess the promise of computational alchemy, a perturbation theory approach that allows for predictions of binding energies thousands of times faster than DFT. We first benchmark the binding energy predictions of oxygen reduction reaction intermediates on alloys of Pt, Pd, and Ni using alchemy against predictions from DFT. Far faster alchemical estimates yield binding energies within 0.1 eV of DFT values in many cases. We also identify distinct cases where alchemy performs significantly worse, indicating areas where modeling improvements are needed. Our results suggest that computational alchemy is a very promising tool that warrants further consideration for high-throughput screening of heterogeneous catalysts.

NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints.

Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods.

A New Progressive Method for Computing Skyline Queries

Skyline queries are important in many fields, especially for decision making. In this context, objects or tuples of databases are defined according to some numerical and non numerical attributes. The skyline operator acts on the numerical ones. The algorithms that implements this skyline operator are genrally of progressive or non progressive. The progressive ones return the skyline operator during its execution while non preogressive alogrithms return the result at the end of its execution. This paper presents a new progressive algorithm for computing the skyline points. This algorithm is based on sorting as a preprocessing of the input. The authors present new theorems for deducing promptly the first skyline points and reducing the candidate space. A new version of Divide-and-Conquer algorithm is used for computing the final skyline. Intensive experimentations on both real and synthetic datasets show that our algorithm presents best performance comparatively to other methods.

Analysis of permutation choices for enhanced generalised Feistel structure with SP‐type round function

Since the proposition of improved generalised Feistel structure (GFS), many researches and applications have been published. In this study, the authors further enhance the improved GFS with SP-type round function by extending the sub-block-wise permutation to word-wise permutation which can have better diffusion and security effect. Then, they study the security effect of different permutation choices for this kind of enhanced GFS cipher with SP-type round function. By proving several propositions about the equivalent situation, they can eliminate isomorphic permutations so as to narrow down the candidate space notably and propose a method to compute the number of effective permutation candidates. Finally, they take three typical scenes as example, and for each experimental scene, they compute the number of effective permutation candidates and exhaustively evaluate their security results. They also give an optimum permutation as example for each scene.

Polarization-operator approach to optical signatures of axion-like particles in strong laser pulses

Hypothetical oscillations of probe photons into axion-like particles might be revealed by exploiting the strong fields of high-intensity laser pulses. Considering an arbitrary plane-wave background, we determine the polarization tensor induced by the quantum fluctuations of the axion field and use it to calculate how the polarimetric properties of an initially linear-polarized probe beam are modified. We find that various experimental setups based on contemporary facilities and instrumentation might lead to new exclusion bounds on the parameter space of these particle candidates. The impact of the pulse shape on the discovery potential is studied via a comparison between the cases in which the wave is modulated by a Gaussian envelope and a sin2 profile. This analysis shows that the upper limits resulting from the ellipticity are relatively insensitive to this change, whereas those arising from the rotation of the polarization plane turn out to be more dependent on the field shape.

Fingerprint indexing based on minutiae pairs and convex core point

Fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms. Fingerprint indexing is a key strategy in automatic fingerprint identification systems (AFISs) which allows us to reduce the number of candidates, the search space, and the occurrences of false acceptance in large databases. In this paper, an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k-means clustering and candidate list reduction criteria to improve the identification performance. Our proposal can effectively reduce the search space and number of candidates for fingerprint matching, and thus achieves higher efficiency and significantly improves the system retrieval performance. Experimental results over some of the fingerprint verification competition (FVC) and the national institute of standards and technology (NIST) databases prove the superiority of the proposed approach against some of the well known indexing algorithms.

Autonomous Relay for Millimeter-Wave Wireless Communications

Millimeter-wave (mmWave) communication is the rising technology for next-generation wireless transmission. Benefited by its abundant bandwidth and short wavelength, mmWave is advanced in multi-gigabit transmittability and beamforming. In contrast, the short wavelength also makes mmWave easily blocked by obstacles. In order to bypass these obstacles, relays are widely needed in mmWave communications. Unmanned autonomous vehicles (UAVs), such as drones and self-driving robots, enable the mobile relays in real applications. Nevertheless, it is challenging for a UAV to find its optimal relay location automatically. On the one hand, it is difficult to find the location accurately due to the complex and dynamic wireless environment; on the other hand, most applications require the relay to forward data immediately, so the autonomous process should be fast. To tackle this challenge, we propose a novel method AutoRelay specialized for mmWave communications. In AutoRelay, the UAV samples the link qualities of mmWave beams while moving. Based on the real-time sampling, the UAV gradually adjusts its path to approach the optimal location by leveraging compressive sensing theory to estimate the link qualities in candidate space, which increases the accuracy and save the time. Performance results demonstrate that AutoRelay outperforms existing methods in achieving an accurate and efficient relay strategy.

Discriminative Part Selection For Human Action Recognition

Semantic parts have shown a powerful discriminative capacity for action recognition. However, many existing methods select parts according to predefined heuristic rules, which may cause the correlation among parts to be lost, or do not appropriately consider the cluttered candidate part space, which may result in weak generalizability of the resulting action labels. Therefore, better consideration of the correlation among parts and refinement of the candidate space will lead to a more discriminative action representation. This paper achieves improved performance by more elegantly addressing these two factors. First, considering the cluttered nature of the candidate space, we propose a recursive part elimination strategy for iterative refinement of the candidate parts. In each iteration, we eliminate the parts with the lowest weights, which are deemed to be noise. Second, we measure the discriminative capabilities of the candidates and select the top-ranked parts by applying a maximum margin model, which can alleviate overfitting while simultaneously improving generalizability and correlation extraction. Finally, using the selected parts, we extract mid-level features. We report experiments conducted on four datasets (KTH, Olympic Sports, UCF50, and HMDB51). The proposed method can achieve significant improvements compared with other recent methods, including a lower computational cost, a faster speed, and higher accuracy.

Minicharged particles search by strong laser pulse-induced vacuum polarization effects

Laser-based searches of the yet unobserved vacuum birefringence might be sensitive for very light hypothetical particles carrying a tiny fraction of the electron charge. We show that, with the help of contemporary techniques, polarimetric investigations driven by an optical laser pulse of moderate intensity might allow for excluding regions of the parameter space of these particle candidates which have not been discarded so far by laboratory measurement data. Particular attention is paid to the role of a Gaussian wave profile. It is argued that, at energy regimes in which the vacuum becomes dichroic due to these minicharges, the transmission probability of a probe beam through an analyzer set crossed to the initial polarization direction will depend on both the induced ellipticity as well as the rotation of the initial polarization plane. The weak and strong field regimes, relative to the attributes of these minicharged particles, and the relevance of the polarization of the strong field are investigated.

Level-aware collective spatial keyword queries

The collective spatial keyword query (CoSKQ), which takes a location and a set of keywords as arguments, finds a group of objects that collectively satisfy the query and achieve the smallest cost. However, few studies concern the keyword level (e.g., the level of hotels), which is of critical importance for decision support. Motivated by this, we study a novel query paradigm, namely Level-aware Collective Spatial Keyword (LCSK) query. The LCSK query asks for a group of objects that cover the query keywords collectively with a threshold constraint and minimize the cost function, which takes into account both the cost of objects and the spatial distance. In our settings, each keyword that appears in the textual description of objects is associated with a level for capturing the feature of keyword.We prove the LCSK query is NP-hard, and devise exact algorithm as well as approximate algorithm with provable approximation bound to this problem. The proposed exact algorithm, namely MergeList, explores the candidate space progressively with several pruning strategies, which is based on the keyword hash table index structure. Unfortunately, this approach is not scalable to large datasets. We thus develop an approximate algorithm called MaxMargin. It finds the answer by traversing the proposed LIR-tree in the best-first fashion. Moreover, two optimizing strategies are used to improve the query performance. The experiments on real and synthetic datasets verify that the proposed approximate algorithm runs much faster than the competitor with desired accuracy.

Listwise Ranking Functions for Statistical Machine Translation

Decision rules play an important role in the tuning and decoding steps of statistical machine translation. The traditional decision rule selects the candidate with the greatest potential from a candidate space by examining each candidate individually. However, viewing each candidate as independent imposes a serious limitation on the translation task. We instead view the problem from a ranking perspective that naturally allows the consideration of an entire list of candidates as a whole through the adoption of a listwise ranking function. Our shift from a pointwise to a list-wise perspective proves to be a simple yet powerful extension to current modeling that allows arbitrary pairwise functions to be incorporated as features, whose weights can be estimated jointly with traditional ones. We further demonstrate that our formulation encompasses the minimum Bayes risk (MBR) approach, another decision rule that considers restricted listwise information, as a special case. Experiments show that our approach consistently outperforms the baseline and MBR methods across the considered test sets.

Ensemble Multiple-Kernel Based Manifold Regularization

As a class of semi-supervised learning methods, manifold regularization learning has recently attracted a lot of attention, due to their great success in exploiting the underlying geometric structures among data. This paper presents a novel semi-supervised approach by combining manifold regularization learning with the idea of multiple kernels, named after ensemble multiple-kernel manifold regularization learning. In our approach, multiple kernels we introduced are not only used to add the flexibility and diversity of the candidate space for the learning problem, but also act as a similarity measure to search for an optimal graph Laplacian in some sense. In other words, the proposed method allows us to learn an 'ideal' kernel and an optimal graph Laplacian simultaneously, which is of significant difference from existing methods. The associated optimization problem is solved efficiently by an alternating iteration procedure. We implement experiments over four real world data sets to demonstrate the benefits of the proposed method.

Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search.

We propose an efficient approach for activity detection in video that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem. Offline, we learn a binary classifier for an activity category using positive video exemplars that are "trimmed" in time to the activity of interest. Then, given a novel untrimmed video sequence, we decompose it into a 3D array of space-time nodes, which are weighted based on the extent to which their component features support the learned activity model. To perform detection, we then directly localize instances of the activity by solving for the maximum-weight connected subgraph in the test video's space-time graph. We show that this detection strategy permits an efficient branch-and-cut solution for the best-scoring-and possibly non-cubically shaped-portion of the video for a given activity classifier. The upshot is a fast method that can search a broader space of space-time region candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on four datasets, and we show its speed and accuracy advantages over multiple existing search strategies.

Orbital transfer vehicle (OTV) system sizing study for manned GEO satellite servicing

ABSTRACTIn an effort to quantify the feasibility of candidate space architectures for astronauts servicing Geosynchronous Earth Orbit (GEO) satellites, a conceptual assessment of architecture-concept and operations-technology combinations has been performed. The focus has been the development of a system with the capability to transfer payload to and from geostationary orbit. Two primary concepts of operations have been selected: (a) Direct insertion/re-entry (Concept of Operations 1 – CONOP 1); (b) Launch to low-earth orbit at Kennedy Space Center inclination angle with an orbital transfer to/from geostationary orbit (Concept of Operations 2 – CONOP 2). The study concludes that a capsule and de-orbit propulsion module system sized for the geostationary satellite servicing mission is feasible for a direct insertion/re-entry concept of operation CONOP 1. Vehicles sized for CONOP 2 show overall total mass savings when utilising the aero-assisted orbital transfer vehicle de-orbit propulsion module options compared to the pure propulsive baseline cases. Overall, the consideration of technical, operational and cost factors determine if either the aero-assisted orbital transfer vehicle concepts or the re-usable/expendable ascent/de-orbit propulsion modules is the preferred option.

Scaling up Superoptimization

Developing a code optimizer is challenging, especially for new, idiosyncratic ISAs. Superoptimization can, in principle, discover machine-specific optimizations automatically by searching the space of all instruction sequences. If we can increase the size of code fragments a superoptimizer can optimize, we will be able to discover more optimizations. We develop LENS, a search algorithm that increases the size of code a superoptimizer can synthesize by rapidly pruning away invalid candidate programs. Pruning is achieved by selectively refining the abstraction under which candidates are considered equivalent, only in the promising part of the candidate space. LENS also uses a bidirectional search strategy to prune the candidate space from both forward and backward directions. These pruning strategies allow LENS to solve twice as many benchmarks as existing enumerative search algorithms, while LENS is about 11-times faster. Additionally, we increase the effective size of the superoptimized fragments by relaxing the correctness condition using contexts (surrounding code). Finally, we combine LENS with complementary search techniques into a cooperative superoptimizer, which exploits the stochastic search to make random jumps in a large candidate space, and a symbolic (SAT-solver-based) search to synthesize arbitrary constants. While existing superoptimizers consistently solve 9--16 out of 32 benchmarks, the cooperative superoptimizer solves 29 benchmarks. It can synthesize code fragments that are up to 82% faster than code generated by gcc -O3 from WiBench and MiBench.

Scaling up Superoptimization

Developing a code optimizer is challenging, especially for new, idiosyncratic ISAs. Superoptimization can, in principle, discover machine-specific optimizations automatically by searching the space of all instruction sequences. If we can increase the size of code fragments a superoptimizer can optimize, we will be able to discover more optimizations. We develop LENS, a search algorithm that increases the size of code a superoptimizer can synthesize by rapidly pruning away invalid candidate programs. Pruning is achieved by selectively refining the abstraction under which candidates are considered equivalent, only in the promising part of the candidate space. LENS also uses a bidirectional search strategy to prune the candidate space from both forward and backward directions. These pruning strategies allow LENS to solve twice as many benchmarks as existing enumerative search algorithms, while LENS is about 11-times faster. Additionally, we increase the effective size of the superoptimized fragments by relaxing the correctness condition using contexts (surrounding code). Finally, we combine LENS with complementary search techniques into a cooperative superoptimizer, which exploits the stochastic search to make random jumps in a large candidate space, and a symbolic (SAT-solver-based) search to synthesize arbitrary constants. While existing superoptimizers consistently solve 9--16 out of 32 benchmarks, the cooperative superoptimizer solves 29 benchmarks. It can synthesize code fragments that are up to 82% faster than code generated by gcc -O3 from WiBench and MiBench.