L2T-DLN: Learning to Teach with Dynamic Loss Network

With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teacher model 1) merely determines the loss function based on the present states of the student model, i.e., disregards the experience of the teacher; 2) only utilizes the states of the student model, e.g., training iteration number and loss/accuracy from training/validation sets, while ignoring the states of the loss function. In this paper, we first formulate the loss adjustment as a temporal task by designing a teacher model with memory units, and, therefore, enables the student learning to be guided by the experience of the teacher model. Then, with a dynamic loss network, we can additionally use the states of the loss to assist the teacher learning in enhancing the interactions between the teacher and the student model. Extensive experiments demonstrate our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, objective detection, and semantic segmentation scenarios

More Severe Manifestations and Poorer Short-Term Prognosis of Ganglioside-Associated Guillain-Barré Syndrome in Northeast China

Ganglioside as a neurotrophic drug has been hitherto widely used in China, although Guillain-Barré syndrome (GBS) following intravenous ganglioside treatment was reported in Europe several decades ago. We identified 7 patients who developed GBS after intravenous use of gangliosides (ganglioside+ group) and compared their clinical data with those of 77 non-ganglioside-associated GBS patients (ganglioside− group) in 2013, aiming at gaining the distinct features of ganglioside-associated GBS. Although the mean age, protein levels in cerebrospinal fluid (CSF) and frequency of cranial nerve involvement were similar between the two groups, the Hughes Functional Grading Scale (HFGS) score and the Medical Research Council (MRC) sum score at nadir significantly differed (4.9±0.4 vs 3.6±1.0; 7.7±5.5 vs 36.9±14.5, both p<0.001), indicating a higher disease severity of ganglioside-associated GBS. A higher ratio of patients with ganglioside-associated GBS required mechanical ventilation (85.7% vs 15.6%, p<0.01). The short-term prognosis of ganglioside-associated GBS, as measured by the HFGS score and the MRC sum score at discharge, was poorer (4.3±0.5 vs 2.8±1.1; 17.3±12.9 vs 46.0±13.9, both p<0.001). All the patients in the ganglioside+ group presented an axonal form of GBS, namely acute motor axonal neuropathy (AMAN). When compared with the AMAN patients in the ganglioside− group, more severe functional deficits at nadir and poorer recovery after standard treatment were still prominent in ganglioside-associated GBS. Anti-GM1 and anti-GT1a antibodies were detectable in patients with AMAN while not in patients with the demyelinating subtype of GBS. The concentrations of these antibodies in patients with AMAN were insignificantly different between the ganglioside+ and ganglioside− groups. In sum, ganglioside-associated GBS may be a devastating side effect of intravenous use of gangliosides, which usually manifests a more severe clinical course and poorer outcome

Driving force and electrolyte effects on the kinetics of bimolecular electron-transfer reactions of ruthenium ammine complexes

N

Nuclear magnetic resonance and mass spectrometry based metabolomics

Metabolomics is the systematic study of the biochemical changes occurring in a living system as studied by the measurement of multiple metabolites in parallel. As two of the most important tools in this field, nuclear magnetic resonance (NMR) and mass spectrometry (MS) provide relatively comprehensive measurements of metabolic profiles in biological systems. In addition, multivariate data analyses, when combined with NMR and MS, provide enormous possibilities for metabolomics research beyond simple data reduction methods. Various unsupervised and supervised statistical methods create robust mathematical models to detect significant differences between groups of samples that are due to perturbation caused by diseases, toxins, therapy or even diet. Key metabolites can be identified from the statistical results and then validated as biomarker candidates. In this dissertation, NMR, MS and their combination with multivariate analyses are used to detect important diseases such as inborn errors of metabolism (IEM) and several cancers. Newly-developed analytical techniques involving ambient sample MS were used in metabolomics-based research and proven to be powerful methods for profiling. The addition of NMR-based metabolomics improved the statistical analysis. Important metabolites were identified using these advanced techniques and various multivariate statistical methods. Based on the identified biomarker candidates, intrinsic disease-related mechanisms were evaluated and suggested for further studies. In particular, emerging technologies in metabolomics discussed in this thesis are shown to be effective, opening a number of potential avenues for further development

Trust, influence, and convergence of behavior in social networks

I propose a social learning framework where agents repeatedly take the weighted average of all agents' current opinions in forming their own for the next period. They also update the influence weights that they place on each other. It is proven that both opinions and the influence weights are convergent. In the steady state, opinions reach consensus and influence weights are distributed evenly. Convergence occurs with an extended model as well, which indicates the tremendous influential power possessed by a minority group. Computer simulations of the updating processes provide supportive evidence.Social networks Learning Consensus Simulation

Kinetic and mechanistic studies on cytochrome P450-related high-valent porphyrin-iron-oxo species.

Kinetic and mechanistic studies on cytochrome P450-related high-valent porphyrin-iron-oxo species

Laboratory Testing of Silica Sol Grout in Coal Measure Mudstones

The effectiveness of silica sol grout on mudstones is reported in this paper. Using X-ray diffraction (XRD), the study investigates how the silica sol grout modifies mudstone mineralogy. Micropore sizes and mechanical properties of the mudstone before and after grouting with four different materials were determined with a surface area/porosity analyser and by uniaxial compression. Tests show that, after grouting, up to 50% of the mesopore volumes can be filled with grout, the dominant pore diameter decreases from 100 nm to 10 nm, and the sealing capacity is increased. Uniaxial compression tests of silica sol grouted samples shows that their elastic modulus is 21%–38% and their uniaxial compressive strength is 16%–54% of the non-grouted samples. Peak strain, however, is greater by 150%–270%. After grouting, the sample failure mode changes from brittle to ductile. This paper provides an experimental test of anti-seepage and strengthening properties of silica sol

Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix

Coal-measure mudstone is a typical dual-porosity media, and grouting in a matrix system is dominantly controlled by the imbibition effect for silica sol. This paper studies the imbibition effect using mudstone in the Huaibei mining area and silica sol as grouting material as an example. Groutability, driving force, and diffusion difficulty affecting the imbibition effect were tested by a mercury porosimeter, nanoparticle size analyzer, optical contact-angle measuring device, surface tension meter, and rotary viscosity meter. After finely grinding a mudstone sample, a pressureless imbibition process was conducted through nuclear magnetic resonance equipment for 216 h to study colloid spontaneous migration and phase characteristics. Results show that silica sol absorption rate follows a power function and that the spectrograms of T2 are distributed in a triple peak pattern, with a tendency to move to the right of vertex time. The paper lays a theoretical and experimental foundation for field grouting in the coal mine