Efficient Testing Algorithm Research Articles

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing & Evaluation (NDT & E) approach to monitor the health
of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical
analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight
to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP)
sample. The proposed approach models the observed data covariance into combination of temporal signal covariance
and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by
the presence of heterogeneity in the experimental data obtained from GFRP sample. This novel method is based on the
coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the
negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used
statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect
detection with the proposed technique.

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution \(\mu\) , an \(\varepsilon \gt 0\) , and access to sampling oracle(s) for a hidden distribution \(\pi\) , the goal in identity testing is to distinguish whether the two distributions \(\mu\) and \(\pi\) are identical or are at least \(\varepsilon\) -far apart. When there is only access to full samples from the hidden distribution \(\pi\) , it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various “conditional” sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the \(\mathsf{Coordinate\ Oracle}\) , and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an \(n\) -dimensional visible distribution \(\mu\) , then there is an efficient identity testing algorithm for any hidden distribution \(\pi\) using \(\widetilde{O}(n/\varepsilon)\) queries to the \(\mathsf{Coordinate\ Oracle}\) . Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of \(n\) -dimensional distributions, specifically sparse antiferromagnetic Ising models over \(\{+1,-1\}^{n}\) , we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless \(\mathsf{RP}=\mathsf{NP}\) . We complement our results with a matching \(\Omega(n/\varepsilon)\) statistical lower bound for the sample complexity of identity testing in the \(\mathsf{Coordinate\ Oracle}\) model.

Drawing Simultaneously Embedded Graphs with Few Bends

We study the problem of drawing simultaneously embedded graphs with few bends. We show that for any simultaneous embedding with fixed edges (SEFE) of two graphs, there exists a corresponding drawing realizing this embedding such that common edges are drawn as straight-line segments and each exclusive edge has a constant number of bends. If the common graph is biconnected and induced, a straight-line drawing exists. This yields the first efficient testing algorithm for simultaneous geometric embedding (SGE) for a non-trivial class of graphs.

Rigid block and hole graphs with a single block

A block and hole graph is obtained from the graph of a plane triangulation by removing the interiors of some discs, defined by their boundary cycles, and then rigidifying the vertex sets of some of these cycles by adding new vertices and edges. These rigid parts form the so-called blocks, while the remaining cycles define the holes. Cruickshank, Kitson, and Power proved that a block and hole graph G with a single block is generically minimally rigid in R3 if and only if G is (3,6)-sparse and has 3|V(G)|−6 edges. This result implies that there is an efficient algorithm for testing whether such a graph is rigid, provided it has exactly 3|V(G)|−6 edges.In this paper we extend these results to block and hole graphs G with a single block and an arbitrary number of edges. The extension is based on a new formula for the degrees of freedom of such a graph. It also enables us to find a smallest set of new edges whose addition makes G rigid. We also point out that there is an underlying matroid which can be defined by (3,6)-sparsity.

The efficient design of Nested Group Testing algorithms for disease identification in clustered data

Group testing study designs have been used since the 1940s to reduce screening costs for uncommon diseases; for rare diseases, all cases are identifiable with substantially fewer tests than the population size. Substantial research has identified efficient designs under this paradigm. However, little work has focused on the important problem of disease screening among clustered data, such as geographic heterogeneity in HIV prevalence. We evaluated designs where we first estimate disease prevalence and then apply efficient group testing algorithms using these estimates. Specifically, we evaluate prevalence using individual testing on a fixed-size subset of each cluster and use these prevalence estimates to choose group sizes that minimize the corresponding estimated average number of tests per subject. We compare designs where we estimate cluster-specific prevalences as well as a common prevalence across clusters, use different group testing algorithms, construct groups from individuals within and in different clusters, and consider misclassification. For diseases with low prevalence, our results suggest that accounting for clustering is unnecessary. However, for diseases with higher prevalence and sizeable between-cluster heterogeneity, accounting for clustering in study design and implementation improves efficiency. We consider the practical aspects of our design recommendations with two examples with strong clustering effects: (1) Identification of HIV carriers in the US population and (2) Laboratory screening of anti-cancer compounds using cell lines.

Development of efficient testing algorithm for hydraulic valve group

Based on the principle of flexible testing technology, the test software is designed hierarchically and modularized. By calling the optimization algorithm of the test process, the automatic combination of the test process and the package call of the basic unit program of the test, the optimal sequencing and flexible configuration of the valve group test process are realized, and the efficiency of the automatic test scheme of the valve group is effectively improved. The test results show that the optimized test scheme can reduce the test time by 14.8% ∼ 34.6% and the test energy consumption by 9.0% ∼ 11.2%.

Genetic Analysis in a Taiwanese Cohort of 750 Index Patients with Clinically Diagnosed Familial Hypercholesterolemia.

Aim: Familial hypercholesterolemia (FH) is underdiagnosed in most countries. The genetic heterogeneity of FH requires an algorithm to efficiently integrate genetic testing into clinical practice. We aimed to report the spectrum of genetic mutations from patients with clinically diagnosed FH in Taiwan. Methods: Patients with LDL-C＞190 mg/dL or those with probable or definite FH according to the Taiwan Lipid Guidelines underwent genetic testing. Samples from 750 index patients from the Taiwan FH registry were screened using custom-made mass spectrometry, followed by targeted next generation sequencing (NGS) and/or multiplex ligation-dependent probe amplification (MLPA) if found negative. Results: The mean age of the patients was 52.4±15.1 years and 40.9% were male. Mutations were detected in 445 patients (59.3%). The distribution of mutations was as follows:LDLR (n=395),APOB (n=58),PCSK9 (n=0), andABCG5 (n=3). The most common mutations wereAPOB c.10579 C＞T (p.R3527W) (12.6%),LDLR c.986 G＞A (p.C329Y) (11.5%), andLDLR c.1747 C＞T (p.H583Y) (10.8%).LDLR c.1187-10 G＞A (IVS 8-10) andAPOB c.10580 G＞A (p.R3527Q) were detected using targeted NGS in Taiwan for the first time. Four novel mutations were identified, includingLDLR c.1060＋2 T＞C (IVS 7＋2),LDLR c.1139 A＞C (p.E380A),LDLR c.1322 T＞C (p.A431T)＋c.1867 A＞G (p.I623V), and ABCG5 c.1337 G＞A (p.R447Q). Conclusion: LDLR andAPOB, but notPCSK9, mutations were the major genetic causes of FH. Four novel mutations inLDLR orABCG5 were identified. This genetic screening method using mass spectrometry, targeted NGS, and MLPA analysis provided an efficient algorithm for genetic testing for clinically diagnosed FH in Taiwan.

Performance and operational feasibility of antigen and antibody rapid diagnostic tests for COVID-19 in symptomatic and asymptomatic patients in Cameroon: a clinical, prospective, diagnostic accuracy study

BackgroundReal-time PCR is recommended to detect SARS-CoV-2 infection. However, PCR availability is restricted in most countries. Rapid diagnostic tests are considered acceptable alternatives, but data are lacking on their performance. We assessed the performance of four antibody-based rapid diagnostic tests and one antigen-based rapid diagnostic test for detecting SARS-CoV-2 infection in the community in Cameroon.MethodsIn this clinical, prospective, diagnostic accuracy study, we enrolled individuals aged at least 21 years who were either symptomatic and suspected of having COVID-19 or asymptomatic and presented for screening. We tested peripheral blood for SARS-CoV-2 antibodies using the Innovita (Biological Technology; Beijing, China), Wondfo (Guangzhou Wondfo Biotech; Guangzhou, China), SD Biosensor (SD Biosensor; Gyeonggi-do, South Korea), and Runkun tests (Runkun Pharmaceutical; Hunan, China), and nasopharyngeal swabs for SARS-CoV-2 antigen using the SD Biosensor test. Antigen rapid diagnostic tests were compared with Abbott PCR testing (Abbott; Abbott Park, IL, USA), and antibody rapid diagnostic tests were compared with Biomerieux immunoassays (Biomerieux; Marcy l'Etoile, France). We retrospectively tested two diagnostic algorithms that incorporated rapid diagnostic tests for symptomatic and asymptomatic patients using simulation modelling.Findings1195 participants were enrolled in the study. 347 (29%) tested SARS-CoV-2 PCR-positive, 223 (19%) rapid diagnostic test antigen-positive, and 478 (40%) rapid diagnostic test antibody-positive. Antigen-based rapid diagnostic test sensitivity was 80·0% (95% CI 71·0–88·0) in the first 7 days after symptom onset, but antibody-based rapid diagnostic tests had only 26·8% sensitivity (18·3–36·8). Antibody rapid diagnostic test sensitivity increased to 76·4% (70·1–82·0) 14 days after symptom onset. Among asymptomatic participants, the sensitivity of antigen-based and antibody-based rapid diagnostic tests were 37·0% (27·0–48·0) and 50·7% (42·2–59·1), respectively. Cohen's κ showed substantial agreement between Wondfo antibody rapid diagnostic test and gold-standard ELISA (κ=0·76; sensitivity 0·98) and between Biosensor and ELISA (κ=0·60; sensitivity 0·94). Innovita (κ=0·47; sensitivity 0·93) and Runkun (κ=0·43; sensitivity 0·76) showed moderate agreement. An antigen-based retrospective algorithm applied to symptomatic patients showed 94·0% sensitivity and 91·0% specificity in the first 7 days after symptom onset. For asymptomatic participants, the algorithm showed a sensitivity of 34% (95% CI 23·0–44·0) and a specificity of 92·0% (88·0–96·0).InterpretationRapid diagnostic tests had good overall sensitivity for diagnosing SARS-CoV-2 infection. Rapid diagnostic tests could be incorporated into efficient testing algorithms as an alternative to PCR to decrease diagnostic delays and onward viral transmission.FundingMédecins Sans Frontières WACA and Médecins Sans Frontières OCG.TranslationsFor the French and Spanish translations of the abstract see Supplementary Materials section.

Informative array testing with multiplex assays

High-volume testing of clinical specimens for sexually transmitted diseases is performed frequently by a process known as group testing. This algorithmic process involves testing portions of specimens from separate individuals together as one unit (or "group") to detect diseases. Retesting is performed on groups that test positively in order to differentiate between positive and negative individual specimens. The overall goal is to use the least number of tests possible across all individuals without sacrificing diagnostic accuracy. One of the most efficient group testing algorithms is array testing. In its simplest form, specimens are arranged into a grid-like structure so that row and column groups can be formed. Positive-testing rows/columns indicate which specimens to retest. With the growing use of multiplex assays, the increasing number of diseases tested by these assays, and the availability of subject-specific risk information, opportunities exist to make this testing process even more efficient. We propose specific specimen arrangements within an array that can reduce the number of retests needed when compared with other array testing algorithms. We examine how to calculate operating characteristics, including the expected number of tests and the SD for the number of tests, and then subsequently find a best arrangement. Our methods are illustrated for chlamydia and gonorrhea detection with the Aptima Combo 2 Assay. We also provide R functions to make our research accessible to laboratories.

Efficient Group ID-Based Encryption With Equality Test Against Insider Attack

Abstract ID-based encryption with equality test (IBEET) allows a tester to compare ciphertexts encrypted under different public keys for checking whether they contain the same message. In this paper, we first introduce group mechanism into IBEET and propose a new primitive, namely group ID-based encryption with equality test (G-IBEET). With the group mechanism: (1) group administrator can authorize a tester to make comparison between ciphertexts of group users, but it cannot compare their ciphertexts with any ciphertext of any user who is not in the group. Such group granularity authorization can make IBEET that adapts to group scenario; (2) for the group granularity authorization, only one trapdoor, named group trapdoor, should be issued to the tester, which can greatly reduce the cost of computation, transmission and storage of trapdoors in traditional IBEET schemes; (3) G-IBEET can resist the insider attack launched by the authorized tester, which is an open problem in IBEET. We give definitions for G-IBEET and propose a concrete construction with an efficient test algorithm. We then give its security analysis in the random oracle model.

BinomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

BackgroundIn this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the “P > > N” high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions.ResultsIn both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers’ main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone.ConclusionbinomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers’ main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.

An efficient algorithm for finding all solutions of nonlinear equations using parallelogram LP test

This paper presents an efficient algorithm for finding all solutions of nonlinear equations using linear programming. This algorithm is based on a simple test (called the LP test) for nonexistence of a solution to a system of nonlinear equations in a given region. In the conventional LP test, a system of nonlinear equations is formulated as a linear programming problem by surrounding component nonlinear functions by rectangles. In the proposed algorithm, we first use rectangles, and when the nonlinearity of functions becomes weak, we switch to parallelograms. It is shown that we can use the dual simplex method throughout the algorithm by applying the variable transformation to the oblique coordinate system, by which the LP test becomes more efficient. Moreover, since polygons with proper sizes are used, the LP test becomes more powerful. By numerical examples, it is shown that the proposed algorithm is more efficient than the conventional algorithms using rectangles only or parallelograms only. We also consider the special case where component nonlinear functions are locally convex and monotone, and propose an efficient LP test algorithm using rectangles and triangles.

State identification sequences from the splitting tree

Context: Software testing based on finite-state machines. Objective:Improving the performance of existing testing methods by construction of more efficient separating sequences, so that states entered by a system under test can be identified in a much shorter span of time. Method: This paper proposes an efficient way to construct separating sequences for subsets of states for any deterministic finite-state machine. It extends an existing algorithm that builds an adaptive distinguishing sequence (ADS) from a splitting tree to machines that do not possess an ADS. Our extension to this construction algorithm allows one not only to construct a separating sequence for any subset of states but also form sets of separating sequences, such as harmonized state identifiers (HSI) and incomplete adaptive distinguishing sequences, that are used by efficient testing and learning algorithms. Results: The experiments confirm that the length and number of test sequences produced by testing methods that use HSIs constructed by our extension is significantly improved. Conclusion:By constructing more efficient separating sequences the performance of existing test methods significantly improves.

Testing Bayesian Networks

This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks – the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node. The value at any particular node is conditionally independent of all the other non-descendant nodes once its parents are fixed. Specifically, we study the properties of identity testing and closeness testing of Bayesian networks. Our main contribution is the first non-trivial efficient testing algorithms for these problems and corresponding information-theoretic lower bounds. For a wide range of parameter settings, our testing algorithms have sample complexity sublinear in the dimension and are sample-optimal, up to constant factors.

March test algorithm for unlinked static reduced three-cell coupling faults in random-access memories

A memory fault model regarding the unlinked static three-cell coupling faults in n × 1 random-access memories is discussed. This model is an extension of the well-known model of unlinked static two-cell coupling faults. Because this model of three-cell coupling is limited to the physically neighbouring memory cells, it can also be considered a neighbourhood pattern-sensitive model. An efficient march test algorithm able to cover this reduced model of three-cell coupling is presented in this letter.

P14.117 Cost efficient test algorithm for molecular subgrouping of medulloblastomas for day-to-day practice in resource limited countries

Abstract INTRODUCTION Molecular classification of medulloblastomas(MB) is prognostically and therapeutically relevant and helps in better risk-stratification. Translation of this subgrouping to routine practice still remains a challenge. The most pathologist-accessible techniques for molecular subgrouping include immunohistochemistry(IHC), fluorescent in-situ hybridization(FISH) and Nanostring assay. Objectives:(1)Molecular subgrouping of MBs by IHC and FISH, and Nanostring Assay (2)To compare their efficacy against sequencing and DNA methylation, and cost for applicability in resource-constrained centers METHODS Ninety-five cases of MB with adequate tissue were included. Molecular subgrouping was performed by IHC for β-catenin, GAB1, YAP1, and p53; FISH for MYC amplification, and sequencing for CTNNB1, and by Nanostring Assay on the same set of MBs. Further, a subset of cases were subjected to 850k DNA methylation array. RESULTS IHC+FISH classified MBs into 15.8% WNT, 16.8% SHH, and 67.4% non-WNT/non-SHH subgroups; with MYC amplification identified in 20.3% cases of non-WNT/non-SHH. A single showed diffuse strong p53 positivity among the SHH subgroup. Nanostring successfully classified 91.5% MBs into 25.3% WNT, 17.2% SHH, 23% Group3 and 34.5% Group4. However, Nanostring assay failure was seen in eight cases, all of which were >8-years-old formalin-fixed paraffin-embedded tissue blocks. Concordant subgroup assignment was noted in 88.5% cases, while subgroup switching was seen in 11.5% cases. Both methods showed prognostic correlation. Among the 5 discrepant cases, which switched to WNT subgroup by Nanostring, only 2 were found to have CTNNB1 mutation. Methylation profiling performed on discordant cases revealed 1 out of 4 extra WNT identified by Nanostring to be WNT, others aligned with IHC subgroups; extra SHH by Nanostring turned out to be SHH by methylation. CONCLUSIONS Both IHC supplemented by FISH and Nanostring are robust methods for molecular subgrouping, albeit with few disadvantages. IHC cannot differentiate between Groups 3 and 4, while Nanostring cannot classify older-archived tumors, and is not available at most centres. WNT subgroup with the best prognosis is best detected by IHC or IHC followed by sequencing for confirmation. Nanostring Assay is better suitable to separate Group 3, the worst prognostic group from Group 4. Thus, both the methods complement each other and can be used in concert for high confidence allotment of molecular subgroups in clinical practice. The cost of IHC plus Nanostring will almost be the same as IHC plus FISH. We recommend a cost-efficient algorithmic approach using histopathological subtype and IHC as the first step followed by Nanostring or FISH, wherever suitable.

The density Turan problem for 3-uniform linear hypertrees. An efficient testing algorithm

Let \(\mathcal{T}=(V,\mathcal{E})\) be a 3-uniform linear hypertree. We consider a blow-up hypergraph \(\mathcal{B}[\mathcal{T}]\). We are interested in the following problem. We have to decide whether there exists a blow-up hypergraph \(\mathcal{B}[\mathcal{T}]\) of the hypertree \(\mathcal{T}\), with hyperedge densities satisfying some conditions, such that the hypertree \(\mathcal{T}\) does not appear in a blow-up hypergraph as a transversal. We present an efficient algorithm to decide whether a given set of hyperedge densities ensures the existence of a 3-uniform linear hypertree \(\mathcal{T}\) in a blow-up hypergraph \(\mathcal{B}[\mathcal{T}]\).

Introducing Primality Testing Algorithm with an Implementation on 64 bits RSA Encryption Using Verilog

A new structure to develop 64-bit RSA encryption engine on FPGA is being presented in this paper that can be used as a standard device in the secured communication system. The RSA algorithm has three parts i.e. key generation, encryption and decryption. This procedure also requires random generation of prime numbers, therefore, we are proposing an efficient fast Primality testing algorithm to meet the requirement for generating the key in RSA algorithm. We use right-to-left-binary method for the exponent calculation. This reduces the number of cycles enhancing the performance of the system and reducing the area usage of the FPGA. These blocks are coded in Verilog and are synthesized and simulated in Xilinx 13.2 design suit.

Introducing Primality Testing Algorithm with an Implementation on 64 bits RSA Encryption Using Verilog

A new structure to develop 64-bit RSA encryption engine on FPGA is being presented in this paper that can be used as a standard device in the secured communication system. The RSA algorithm has three parts i.e. key generation, encryption and decryption. This procedure also requires random generation of prime numbers, therefore, we are proposing an efficient fast Primality testing algorithm to meet the requirement for generating the key in RSA algorithm. We use right-to-left-binary method for the exponent calculation. This reduces the number of cycles enhancing the performance of the system and reducing the area usage of the FPGA. These blocks are coded in Verilog and are synthesized and simulated in Xilinx 13.2 design suit.

3 Introducing Primality Testing Algorithm with an Implementation on 64 bits RSA Encryption Using Verilog

A new structure to develop 64-bit RSA encryption engine on FPGA is being presented in this paper that can be used as a standard device in the secured communication system. The RSA algorithm has three parts i.e. key generation, encryption and decryption. This procedure also requires random generation of prime numbers, therefore, we are proposing an efficient fast Primality testing algorithm to meet the requirement for generating the key in RSA algorithm. We use right-to-left-binary method for the exponent calculation. This reduces the number of cycles enhancing the performance of the system and reducing the area usage of the FPGA. These blocks are coded in Verilog and are synthesized and simulated in Xilinx 13.2 design suit.