Conditional error variance in the WISC-IV

Measurement error at different ability levels in the WISC-IV was studied to empirically test the conditional error variance hypothesis. Graduate students in clinical psychology at a Midwestern university scored fictitious WISC-IV Vocabulary subtests constructed to yield actual scaled scores of 4, 10, and 16. Classical measurement theory assumes error rate will be constant across the three conditions. Modern test theories (Item Response Theory), however, predict that the precision of a measurement instrument will change as a function of the examinee\u27s ability level. Data supported the conditional error variance hypothesis. Scorers made significantly more errors in the low- and high-abilitylevel conditions than they did in the average ability condition. Implications of these findings for intelligence testing are discussed

Neural networks would \u27vote\u27 according to Borda\u27s Rule

Can neural networks learn to select an alternative based on a systematic aggregation of conflicting individual preferences (i.e. a `voting rule\u27)? And if so, which voting rule best describes their behavior? We show that a prominent neural network can be trained to respect two fundamental principles of voting theory, the unanimity principle and the Pareto property. Building on this positive result, we train the neural network on profiles of ballots possessing a Condorcet winner, a unique Borda winner, and a unique plurality winner, respectively. We investigate which social outcome the trained neural network chooses, and find that among a number of popular voting rules its behavior mimics most closely the Borda rule. Indeed, the neural network chooses the Borda winner most often, no matter on which voting rule it was trained. Neural networks thus seem to give a surprisingly clear-cut answer to one of the most fundamental and controversial problems in voting theory: the determination of the most salient election method

Model Predictive Control of CMSMPR Crystalliser

One of the most critical components of the chemical industry in terms of crystallisation is the pharmaceutical sector. Most medicine components are expensive and require complex processes for their production, so producing waste is highly inefficient. Another concern is the high-quality standards for most pharmaceutical products. Therefore, optimising the crystallisation process is critical from a quality perspective, with the main concerns being the product's crystal structure and particle diameter distribution. Regardless efficient control in batch processes such as crystallisation is a difficult task due to the inherently nonlinear behaviour of the system. Using a priori model of the system as the basis for nonlinear model predictive control could provide a useful tool for handling the crystallisation process, mitigating the effects of disturbance and noise and ensuring appropriate product quality. In this work, we wish to showcase the possibility of controlling a crystallisation process using model predictive control to enable the production of crystal products with desired particle diameter distribution and crystalline product average size. The method is shown using citric acid as a model substance in a case study of a continuous crystallisation procedure in a stirred tank reactor. The crystalliser model includes an energy balance, so the system's behaviour depends on the cooling rate and residence time. Accordingly, the control problem can be formulated as multiple inputs and multiple outputs (MIMO) system. Moreover, the two controlled (average particle size and crystal size dispersion) variables are not easily detached from each other. So, the traditional controlling strategies, for example, the decoupling controller, is challenging to apply. The MPC (model predictive control) as an advanced control algorithm can be a solution to this

First report of Serratia marcescens from oleander in Hungary

Oleander (Nerium oleander L.) is a popular woody ornamental plant, often used for decorating public areas, terraces and gardens. Many diseases may decrease in the ornamental value of these plantings. Between 2018 and 2020, plant pathogenic bacteria of oleander were examined, and many samples of infected plants were collected from different sites in Hungary. Two non-pigmented Serratia marcescens isolates were identified from oleander by classical and molecular methods. The isolates caused necrotic lesions on oleander leaves. Serratia marcescens is known as an opportunistic mammal or plant pathogen, but non-pathogenic strains are known to be useful biological control agents or plant growth-promoting bacteria. This is the first report of the plant pathogen S. marcescens from oleander, and the first identification of the bacterium in Hungary.

Ökológiai gazdálkodás melléklet - 2012. június - július

Az Őstermelő című lap ökológiai gazdálkodás mellékletét az ÖMKi és a Hungária Öko Garancia munkatársai szerkesztették. E szám mellékletében az alábbi témákról olvashatnak: - Biogazdálkodási együttműködés Hejőszalontán - Az ökológiai kiegyenlítő felületek alkalmazásával aktívan részt vehetünk az élővilág megőrzésében - Magyarországon járt a Brazil Mezőgazdasági Kutató Vállalat (Embrapa) delegációja - A talajmikrobák mezőgazdasági jelentősége – Ködöböcz László ÖMKi ösztöndíjas kutatása - Szempontok vetésforgó tervezéséhez ökológiai gazdálkodásba

MBW: Multi-view Bootstrapping in the Wild

Labeling articulated objects in unconstrained settings have a wide variety of applications including entertainment, neuroscience, psychology, ethology, and many fields of medicine. Large offline labeled datasets do not exist for all but the most common articulated object categories (e.g., humans). Hand labeling these landmarks within a video sequence is a laborious task. Learned landmark detectors can help, but can be error-prone when trained from only a few examples. Multi-camera systems that train fine-grained detectors have shown significant promise in detecting such errors, allowing for self-supervised solutions that only need a small percentage of the video sequence to be hand-labeled. The approach, however, is based on calibrated cameras and rigid geometry, making it expensive, difficult to manage, and impractical in real-world scenarios. In this paper, we address these bottlenecks by combining a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates from videos with only two or three uncalibrated, handheld cameras. With just a few annotations (representing 1-2% of the frames), we are able to produce 2D results comparable to state-of-the-art fully supervised methods, along with 3D reconstructions that are impossible with other existing approaches. Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo. We release the codebase for MBW as well as this challenging zoo dataset consisting image frames of tail-end distribution categories with their corresponding 2D, 3D labels generated from minimal human intervention.Comment: NeurIPS 2022 conference. Project webpage and code: https://github.com/mosamdabhi/MB

The 2nd 3D Face Alignment In The Wild Challenge (3DFAW-video): Dense Reconstruction From Video

3D face alignment approaches have strong advantages over 2D with respect to representational power and robustness to illumination and pose. Over the past few years, a number of research groups have made rapid advances in dense 3D alignment from 2D video and obtained impressive results. How these various methods compare is relatively unknown. Previous benchmarks addressed sparse 3D alignment and single image 3D reconstruction. No commonly accepted evaluation protocol exists for dense 3D face reconstruction from video with which to compare them. The 2nd 3D Face Alignment in the Wild from Videos (3DFAW-Video) Challenge extends the previous 3DFAW 2016 competition to the estimation of dense 3D facial structure from video. It presented a new large corpora of profile-to-profile face videos recorded under different imaging conditions and annotated with corresponding high-resolution 3D ground truth meshes. In this paper we outline the evaluation protocol, the data used, and the results. 3DFAW-Video is to be held in conjunction with the 2019 International Conference on Computer Vision, in Seoul, Korea