Discovering Higher-Order Interactions Through Neural Information Decomposition

If regularity in data takes the form of higher-order functions among groups of variables, models which are biased towards lower-order functions may easily mistake the data for noise. To distinguish whether this is the case, one must be able to quantify the contribution of different orders of dependence to the total information. Recent work in information theory attempts to do this through measures of multivariate mutual information (MMI) and information decomposition (ID). Despite substantial theoretical progress, practical issues related to tractability and learnability of higher-order functions are still largely unaddressed. In this work, we introduce a new approach to information decomposition—termed Neural Information Decomposition (NID)—which is both theoretically grounded, and can be efficiently estimated in practice using neural networks. We show on synthetic data that NID can learn to distinguish higher-order functions from noise, while many unsupervised probability models cannot. Additionally, we demonstrate the usefulness of this framework as a tool for exploring biological and artificial neural networks.     

Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.     

Determining Causal Skeletons with Information Theory

Modeling a causal association as arising from a communication process between cause and effect, simplifies the discovery of causal skeletons. The communication channels enabling these communication processes, are fully characterized by stochastic tensors, and therefore allow us to use linear algebra. This tensor-based approach reduces the dimensionality of the data needed to test for conditional independence, e.g., for systems comprising three variables, pair-wise determined tensors suffice to infer the causal skeleton. The only thing needed is a minor extension to information theory, namely the concept of path information.     

Survival of Virus Particles in Water Droplets: Hydrophobic Forces and Landauer’s Principle

Many small biological objects, such as viruses, survive in a water environment and cannot remain active in dry air without condensation of water vapor. From a physical point of view, these objects belong to the mesoscale, where small thermal fluctuations with the characteristic kinetic energy of k_BT (where k_B is the Boltzmann’s constant and T is the absolute temperature) play a significant role. The self-assembly of viruses, including protein folding and the formation of a protein capsid and lipid bilayer membrane, is controlled by hydrophobic forces (i.e., the repulsing forces between hydrophobic particles and regions of molecules) in a water environment. Hydrophobic forces are entropic, and they are driven by a system’s tendency to attain the maximum disordered state. On the other hand, in information systems, entropic forces are responsible for erasing information, if the energy barrier between two states of a switch is on the order of k_BT, which is referred to as Landauer’s principle. We treated hydrophobic interactions responsible for the self-assembly of viruses as an information-processing mechanism. We further showed a similarity of these submicron-scale processes with the self-assembly in colloidal crystals, droplet clusters, and liquid marbles.     

Comparison of ultrasound and microwave assisted extraction of diosgenin from Trigonella foenum graceum seed

From the recent market trend, there is a huge demand for the bioactive compounds from various food matrices that could be capable enough to combat the emerging health effects in day-to-day life. Fenugreek is a well-known spice from ancient times for its medicinal and health benefits. In the present study, two methods of green extraction microwave (MAE) and ultrasound (UAE) assisted were studied in regard of extraction of fenugreek diosgenin. In this study, solvent type (acetone, ethanol, hexane and petroleum ether), solvent concentration (40, 60, 80 and 100%) and treatment time (1.5, 3.0, 4.5 and 6.0 min and 30, 40, 50 and 60 min for MAE and UAE method respectively) was varied to observe the effect of these parameters over extract yield and diosgenin content. The results of this study revealed that treatment time, type of solvent and its concentration and method adopted for extraction of diosgenin has significant effect. In relation with better yield extract and diosgenin content, the yield of fenugreek seed extract was 7.83% with MAE and 21.48% with UAE of fenugreek seed powder at 80% ethanol concentration at 6 and 60 min respectively. The content of diosgenin was observed in fenugreek seed powder extract was 35.50 mg/100 g in MAE and 40.37 mg/100 g in UAE with 80% ethanol concentration at 6 and 60 min respectively. The overall range of yield of fenugreek extract was varied from 1.04% to 32.48% and diosgenin content was 15.82 mg/100 g to 40.37 mg/100 g of fenugreek seed powder including both extraction methods. This study revealed that UAE would impose better ways for preparing fenugreek extract and observing diosgenin content from fenugreek seeds.     

Influence of ultrasound-assisted tumbling on NaCl transport and the quality of pork

The present study aimed to investigate the impact of ultrasound-assisted tumbling (UAT; 20 kHz, 100, 300, 500 and 700 W) with different treatment time (30, 60, 90 and 120 min) on the diffusion and distribution of NaCl as well as the change of pork texture properties during curing. Results showed that in comparison with the single tumbling (ST), the NaCl content and the NaCl diffusion coefficient were increased along with UAT treatment (P < 0.05). The scanning electron microscopy and the energy dispersive X-ray analysis showed that UAT treatment changed the microstructure of pork which may facilitate the NaCl dispersion homogeneously. In addition, the moderate UAT treatment of 300 W with 60 min could significantly improve the tumbling yield, water-holding capacity and textural properties of pork compared with the ST treatment (P < 0.05). Meanwhile, in comparison with the ST group, protein extraction was considerably increased after UAT (300 and 500 W) treated for 120 min (P < 0.05). Our study demonstrated that UAT treatment could effectively promote the penetration and distribution of NaCl and improve pork meat quality via facilitating the extraction of meat protein.     

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.     

Adding Prior Information in FWI through Relative Entropy

Full waveform inversion is an advantageous technique for obtaining high-resolution subsurface information. In the petroleum industry, mainly in reservoir characterisation, it is common to use information from wells as previous information to decrease the ambiguity of the obtained results. For this, we propose adding a relative entropy term to the formalism of the full waveform inversion. In this context, entropy will be just a nomenclature for regularisation and will have the role of helping the converge to the global minimum. The application of entropy in inverse problems usually involves formulating the problem, so that it is possible to use statistical concepts. To avoid this step, we propose a deterministic application to the full waveform inversion. We will discuss some aspects of relative entropy and show three different ways of using them to add prior information through entropy in the inverse problem. We use a dynamic weighting scheme to add prior information through entropy. The idea is that the prior information can help to find the path of the global minimum at the beginning of the inversion process. In all cases, the prior information can be incorporated very quickly into the full waveform inversion and lead the inversion to the desired solution. When we include the logarithmic weighting that constitutes entropy to the inverse problem, we will suppress the low-intensity ripples and sharpen the point events. Thus, the addition of entropy relative to full waveform inversion can provide a result with better resolution. In regions where salt is present in the BP 2004 model, we obtained a significant improvement by adding prior information through the relative entropy for synthetic data. We will show that the prior information added through entropy in full-waveform inversion formalism will prove to be a way to avoid local minimums.     

Quantifying nonclassicality of multimode bosonic fields via skew information

We quantify the nonclassicality of multimode bosonic field states by adopting an information-theoretic approach involving the Wigner-Yanase skew information. The fundamental properties of the quantifier such as convexity, superadditivity, monotonicity, and conservation relations are revealed. The quantifier is illustrated by a variety of typical examples, and applications to the quantification of nonclassical correlations are discussed. Various extensions are indicated.     

信息化课堂教学设计--以“刚体定轴转动的角动量及角动量守恒定律”为例

遵循“学生主体、教师主导”的教育教学理念,借助信息化教学工具,合理有效地设计好每一个教学环节.采用演示、体验、探究、问题导向和分组讨论等教学手段和方法,让学生在课堂中自主发现问题、分析问题并解决问题,实现“学生做中学、教师做中教”,大大提升教学效果.