Mining Object Parts from CNNs via Active Question-Answering

Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows. We allow the computer to actively identify objects, whose neural patterns cannot be explained by the current AOG. Then, the computer asks human about the unexplained objects, and uses the answers to automatically discover certain CNN patterns corresponding to the missing knowledge. We incrementally grow the AOG to encode new knowledge discovered during the active-learning process. In experiments, our method exhibits high learning efficiency. Our method uses about 1/6-1/3 of the part annotations for training, but achieves similar or better part-localization performance than fast-RCNN methods.Comment: Published in CVPR 201

Tickborne Rickettsial Diseases: Epidemiological studies in China

Rickettsial diseases are vector-borne zoonoses caused by obligate intracellular bacteria within the order Rickettsiales, which was previously described as short, Gram-negative rod bacteria that retained basic fuchsin when stained by the method of Gimenez. As development in molecular technologies, the taxonomy of the fastidious bacterial species in the order Rickettsiales has been modifi ed (Dumler et al. 2001), and certain agents such as Coxiella burnetii, the pathogen of Q fever have recently been removed from this order (Raoult & Roux 1997). Although specialists in the fi eld of rickettsiology frequently disagree over species defi nitions, the taxa as well as names of species or subspecies based on polyphasic taxonomic studies by integrating phenotypic and phylogenetic data (Fournier et al. 2003) are currently accepted and used in this thesis. Three groups of diseases are usually classifi ed as rickettsial diseases. These include (i) rickettsioses caused by the spotted fever group (SFG) and the typhus group rickettsiae of the genus Rickettsia within the family Rickettsiaceae, (ii) ehrlichioses and anaplasmoses due to microorganisms within the family Anaplasmataceae, and (iii) scrub typhus caused by Orientia tsutsugamushi (Raoult & Roux 1997; Dumler et al. 2001; Hechemy et al. 2003; Watt & Parola 2003). Among these rickettsial diseases, scrub typhus is transmitted by trombiculid mites (Watt & Parola 2003), and cat fl ea typhus (also called fl ea-borne spotted fever) due to Rickettsia felis is transmitted by fl ea (Adams et al. 1990; Higgins et al. 1996). Tickborne rickettsial diseases are caused by two groups of intracellular bacteria belonging to the order Rickettsiales, i.e. the SFG of the genus Rickettsia within the family Rickettsiaceae and several genera of Anaplasma and Ehrlichia groups within the family Anaplasmataceae. These pathogens infect and proliferate in the organs of ticks, particularly in the salivary glands, and can be transmitted to animal hosts during feeding (Parola & Raoult 2001). Because each tick species favours particular optimal environmental conditions and biotopes, the geographic distribution of the ticks is usually restricted to a specifi c area (small or large) and tickborne rickettsial diseases are natural focus diseases. This is particularly true for most of the spotted fever rickettsiae, which are maintained in ticks through transstadial (from larvae to nymphs to adults) and transovarial (from one generation to the next through ovaries) transmissions (Raoult & Roux 1997). Ticks are not insects but Arachnids, a class of Arthropods, which also include

Interpreting CNN Knowledge via an Explanatory Graph

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part through different images. We transfer part patterns in the explanatory graph to the task of part localization, and our method significantly outperforms other approaches.Comment: in AAAI 201

Approximations for Equilibrium Problems and Nonexpansive Semigroups

We introduce a new iterative method for finding a common element of the set of solutions of an equilibrium problem and the set of all common fixed points of a nonexpansive semigroup and prove the strong convergence theorem in Hilbert spaces. Our result extends the recent result of Zegeye and Shahzad (2013). In the last part of the paper, by the way, we point out that there is a slight flaw in the proof of the main result in Shehu's paper (2012) and perfect the proof

Weak topological insulators induced by the inter-layer coupling: A first-principles study of stacked Bi2TeI

Based on first-principles calculations, we predict Bi2TeI, a stoichiometric compound synthesized, to be a weak topological insulator (TI) in layered subvalent bismuth telluroiodides. Within a bulk energy gap of 80 meV, two Dirac-cone-like topological surface states exist on the side surface perpendicular to BiTeI layer plane. These Dirac cones are relatively isotropic due to the strong inter-layer coupling, distinguished from those of previously reported weak TI candidates. Moreover, with chemically stable cladding layers, the BiTeI-Bi2-BiTeI sandwiched structure is a robust quantum spin Hall system, which can be obtained by simply cleaving the bulk Bi2TeI.Comment: 4.5 pages, 4 figure

Wavelet packet decomposition-based fault diagnosis scheme for SRM drives with a single current sensor

Power converters are a key, but vulnerable component in switched reluctance motor (SRM) drives. In this paper, a new fault diagnosis scheme for SRM converters is proposed based on the wavelet packet decomposition (WPD) with a dc-link current sensor. Open- and short-circuit faults of the power switches in an asymmetrical half-bridge converter are analyzed in details. In order to obtain the fault signature from the phase currents, two pulse-width modulation signals with phase shift are injected into the lower-switches of the converter to extract the excitation current, and the WPD algorithm is then applied to the detected currents for fault diagnosis. Moreover, a discrete degree of the wavelet packet node energy is chosen as the fault coefficient. The converter faults can be diagnosed and located directly by determining the changes in the discrete degree from the detected currents. The proposed scheme requires only one current sensor in the dc link, while conventional methods need one sensor for each phase or additional detection circuits. The experimental results on a 750-W three-phase SRM are presented to confirm the effectiveness of the proposed fault diagnosis scheme

Iterative Methods for Equilibrium Problems and Monotone Inclusion Problems in Hilbert Spaces

We introduce a new iterative method for finding a common element of the set of solutions of an equilibrium problem and the set of zeros of the sum of maximal monotone operators, and we obtain strong convergence theorems in Hilbert spaces. We also apply our results to the variational inequality and convex minimization problems. Our results extend and improve the recent result of Takahashi et al. (2012)