Parameter Tuning Using Gaussian Processes

Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor modelling. Hence, it is necessary to acquire the “best” parameter values for a particular algorithm before building the model. The “best” model not only reflects the “real” function and is well fitted to existing points, but also gives good performance when making predictions for new points with previously unseen values. A number of methods exist that have been proposed to optimize parameter values. The basic idea of all such methods is a trial-and-error process whereas the work presented in this thesis employs Gaussian process (GP) regression to optimize the parameter values of a given machine learning algorithm. In this thesis, we consider the optimization of only two-parameter learning algorithms. All the possible parameter values are specified in a 2-dimensional grid in this work. To avoid brute-force search, Gaussian Process Optimization (GPO) makes use of “expected improvement” to pick useful points rather than validating every point of the grid step by step. The point with the highest expected improvement is evaluated using cross-validation and the resulting data point is added to the training set for the Gaussian process model. This process is repeated until a stopping criterion is met. The final model is built using the learning algorithm based on the best parameter values identified in this process. In order to test the effectiveness of this optimization method on regression and classification problems, we use it to optimize parameters of some well-known machine learning algorithms, such as decision tree learning, support vector machines and boosting with trees. Through the analysis of experimental results obtained on datasets from the UCI repository, we find that the GPO algorithm yields competitive performance compared with a brute-force approach, while exhibiting a distinct advantage in terms of training time and number of cross-validation runs. Overall, the GPO method is a promising method for the optimization of parameter values in machine learning

Experimental and Computational Characterizations of Native Ligaments, Tendons, and Engineered 3-D Bone-Ligament-Bone Constructs in the Knee.

Ligaments and tendons are soft tissues that support muscle and bone structures in the body. The incidence of ligament and tendon rupture in the US has increased drastically in recent years, with the incidence of knee ligament rupture among children becoming a more dire concern. A common approach to anterior cruciate ligament (ACL) reconstruction uses a portion of the patellar tendon (PT) from the patient or a cadaver as a graft to replace the torn ACL. The current approach has several limitations including graft availability, risk of rejection, increased morbidity and, more importantly, unmatched biomechanical properties of the native ACL. These limitations have led to an increased urgency for engineered replacement tissues for ACL reconstruction. An engineered graft was developed by differentiating bone marrow stromal cells in vitro into bone and ligament and co-culturing their self-generated extracellular matrices to form a bone-ligament-bone (BLB) construct. The efficacy of this graft as an ACL replacement was evaluated at 6- and 9-months post surgery. ACL replacement was performed in sheep and morphological, biomechanical, and computational assessments were used to facilitate comparison of BLB constructs with the commonly used PT graft. A 6-month recovery showed that the BLB constructs adapted in vivo and based on histological and mechanical analysis, developed quickly to improve mechanical properties. By 9-months, the morphology of the BLB further developed. The geometric stiffness of the BLB constructs increased dramatically after 9-months of recovery and attained 60% of that of the contralateral ACL. More importantly, the analysis demonstrated the BLB also has inhomogeneous, non-linear viscoelastic properties that are characteristic of the native ACL. The engineered BLB developed a deformation pattern that was similar to the native ACL while the deformation of the PT autograft remained uniform even after 9-months of in vivo recovery. A computational model of these tissues was also constructed to examine the altered biomechanics of the knee as a result of PT autografts vs. tissue-engineered strategies. From these analyses, the engineered BLB construct showed great potential for ACL reconstruction demonstrated by its similarity to the native ACL in terms of morphology and inhomogeneous and viscoelastic biomechanical properties.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/94100/1/jinjinma_1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/94100/2/jinjinma_3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/94100/3/jinjinma_2.pd

Networks are Slacking Off: Understanding Generalization Problem in Image Deraining

Deep deraining networks, while successful in laboratory benchmarks, consistently encounter substantial generalization issues when deployed in real-world applications. A prevailing perspective in deep learning encourages the use of highly complex training data, with the expectation that a richer image content knowledge will facilitate overcoming the generalization problem. However, through comprehensive and systematic experimentation, we discovered that this strategy does not enhance the generalization capability of these networks. On the contrary, it exacerbates the tendency of networks to overfit to specific degradations. Our experiments reveal that better generalization in a deraining network can be achieved by simplifying the complexity of the training data. This is due to the networks are slacking off during training, that is, learning the least complex elements in the image content and degradation to minimize training loss. When the complexity of the background image is less than that of the rain streaks, the network will prioritize the reconstruction of the background, thereby avoiding overfitting to the rain patterns and resulting in improved generalization performance. Our research not only offers a valuable perspective and methodology for better understanding the generalization problem in low-level vision tasks, but also displays promising practical potential

Deep saliency detection-based pedestrian detection with multispectral multi-scale features fusion network

In recent years, there has been increased interest in multispectral pedestrian detection using visible and infrared image pairs. This is due to the complementary visual information provided by these modalities, which enhances the robustness and reliability of pedestrian detection systems. However, current research in multispectral pedestrian detection faces the challenge of effectively integrating different modalities to reduce miss rates in the system. This article presents an improved method for multispectral pedestrian detection. The method utilises a saliency detection technique to modify the infrared image and obtain an infrared-enhanced map with clear pedestrian features. Subsequently, a multiscale image features fusion network is designed to efficiently fuse visible and IR-enhanced maps. Finally, the fusion network is supervised by three loss functions for illumination perception, light intensity, and texture information in conjunction with the light perception sub-network. The experimental results demonstrate that the proposed method improves the logarithmic mean miss rate for the three main subgroups (all day, day and night) to 3.12%, 3.06%, and 4.13% respectively, at “reasonable” settings. This is an improvement over the traditional method, which achieved rates of 3.11%, 2.77%, and 2.56% respectively, thus demonstrating the effectiveness of the proposed method

Ameliorative Effect and Underlying Mechanisms of Total Triterpenoids from Psidium guajava Linn (Myrtaceae) Leaf on High-Fat Streptozotocin-induced Diabetic Peripheral Neuropathy in Rats

Purpose: To investigate whether the total triterpenoids extracted from Psidium Guajava leaves (TTPGL) attenuate the development of diabetic peripheral neuropathy in rats by regulating the NF-κB pathway of the inflammatory process and its signaling mediators.Methods: All the Sprague Dawley rats used were maintained in a clean environment on a 12 h light/12h dark cycle. High-fat feeding and intraperitoneal injection of 40 mg/kg streptozotocin (STZ) were used to induce diabetes in the rats. The rats were randomly divided into 5 groups: diabetic mellitus (DM) group; TTPGL - 30 group, TTPGL - 60 group and TTPGL - 120 group treated by intragastric administration (i.g) with 30, 100 and 120 mg/kg/day TTPGL, respectively. The well-established drug, rosiglitazone (RSG, 3 mg/k/d, i.g.), was used as positive control. Normal rats served as control group. Nerve conduction velocity and sensitive tests were measured on weeks 1, 4 and 8. After 8 weeks administration, expression of pro-inflammatory molecules (TNF - α, IL - 6 and iNOS) and tissue proteins (Akt, IKKα, and NF – κB - p65) were evaluated to assess biochemical changes.Results: Compared to DM group, TTPGL (especially 120 mg / kg dose) treatment improved (p < 0.05) physical functions and provided neuronal protection in high - fat/streptozotocin - induced peripheral neuropathy rats. We found that the expressions of several pro - inflammatory factors such as tumor necrosis factor - α (TNF - α), IL - 6 and inducible nitric oxide synthase (iNOS) were highly suppressed (p < 0.05 or p < 0.01) by TTPGL in sciatic nerve. Mechanism analysis indicated that the ameliorative effect of TTPGL, in part, is through suppression of the expression of pro - inflammatory cytokines by NF - κB pathway mediation.Conclusion: TTPGL offers a potential therapeutic approach for the treatment of diabetic peripheral neuropathy.Keywords: Triterpenoids, Psidium Guajava, Diabetic peripheral neuropathy, Pro inflammatory cytokines, NF-κB pathwa

Removal of Hsf4 leads to cataract development in mice through down-regulation of γS-crystallin and Bfsp expression

<p>Abstract</p> <p>Background</p> <p>Heat-shock transcription factor 4 (HSF4) mutations are associated with autosomal dominant lamellar cataract and Marner cataract. Disruptions of the <it>Hsf4 </it>gene cause lens defects in mice, indicating a requirement for HSF4 in fiber cell differentiation during lens development. However, neither the relationship between HSF4 and crystallins nor the detailed mechanism of maintenance of lens transparency by HSF4 is fully understood.</p> <p>Results</p> <p>In an attempt to determine how the underlying biomedical and physiological mechanisms resulting from loss of HSF4 contribute to cataract formation, we generated an <it>Hsf4 </it>knockout mouse model. We showed that the <it>Hsf4 </it>knockout mouse (<it>Hsf4</it>^-/-) partially mimics the human cataract caused by HSF4 mutations. Q-PCR analysis revealed down-regulation of several cataract-relevant genes, including <it>γS-crystallin (Crygs) </it>and lens-specific beaded filament proteins 1 and 2 (<it>Bfsp1 </it>and <it>Bfsp2</it>), in the lens of the <it>Hsf4</it>^-/-mouse. Transcription activity analysis using the dual-luciferase system suggested that these cataract-relevant genes are the direct downstream targets of HSF4. The effect of HSF4 on <it>γS-crystallin </it>is exemplified by the cataractogenesis seen in the <it>Hsf4</it>^-/-,<it>rncat </it>intercross. The 2D electrophoretic analysis of whole-lens lysates revealed a different expression pattern in 8-week-old <it>Hsf4</it>^-/-mice compared with their wild-type counterparts, including the loss of some αA-crystallin modifications and reduced expression of γ-crystallin proteins.</p> <p>Conclusion</p> <p>Our results indicate that HSF4 is sufficiently important to lens development and disruption of the <it>Hsf4 </it>gene leads to cataracts via at least three pathways: 1) down-regulation of <it>γ-crystallin</it>, particularly <it>γS-crystallin</it>; 2) decreased lens beaded filament expression; and 3) loss of post-translational modification of αA-crystallin.</p

PCR-Based Seamless Genome Editing with High Efficiency and Fidelity in <i>Escherichia coli</i>

Efficiency and fidelity are the key obstacles for genome editing toolboxes. In the present study, a PCR-based tandem repeat assisted genome editing (TRAGE) method with high efficiency and fidelity was developed. The design of TRAGE is based on the mechanism of repair of spontaneous double-strand breakage (DSB) via replication fork reactivation. First, cat-sacB cassette flanked by tandem repeat sequence was integrated into target site in chromosome assisted by Red enzymes. Then, for the excision of the cat-sacB cassette, only subculturing is needed. The developed method was successfully applied for seamlessly deleting, substituting and inserting targeted genes using PCR products. The effects of different manipulations including sucrose addition time, subculture times in LB with sucrose and stages of inoculation on the efficiency were investigated. With our recommended procedure, seamless excision of cat-sacB cassette can be realized in 48 h efficiently. We believe that the developed method has great potential for seamless genome editing in E. coli

Development and validation of LC-ESI-MS method for the quantification of dapoxetine in rat plasma

A sensitive and simple liquid chromatography/electrospray mass spectrometry (LC-ESI-MS) method for determination of dapoxetine in rat plasma using one-step protein precipitation was developed and validated. After addition of midazolam as internal standard (IS), protein precipitation by acetonitrile was used in sample preparation. Chromatographically separation was achieved on an SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1 % formic acid as the mobile phase with gradient elution. Electrospray ionization (ESI) source was applied and operated in positive ion mode; selected ion monitoring (SIM) mode was used to quantification using target fragment ions m/z 306 for dapoxetine and m/z 326 for the IS. Calibration plots were linear over the range of 5-1000 ng/mL for dapoxetine in rat plasma. Lower limit of quantification (LLOQ) for dapoxetine was 5 ng/mL. Mean recovery of dapoxetine from plasma was in the range of 92.4-98.6 %. CV of intra-day and inter-day precision were both less than 15 %. This method is simple and sensitive enough to be used in pharmacokinetic research for determination of dapoxetine in rat plasma.Colegio de Farmacéuticos de la Provincia de Buenos Aire