Novel Tactile-SIFT Descriptor for Object Shape Recognition

Using a tactile array sensor to recognize an object often requires multiple touches at different positions. This process is prone to move or rotate the object, which inevitably increases difficulty in object recognition. To cope with the unknown object movement, this paper proposes a new tactile-SIFT descriptor to extract features in view of gradients in the tactile image to represent objects, to allow the features being invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each of which is represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects. The proposed method has been validated by classifying 18 real objects with data from an off-the-shelf tactile sensor. The parameters of the tactile-SIFT descriptor, including the dimension size dn and the number of subpatches sp, are studied. It is found that the optimal performance is obtained using an 8-D descriptor with three subpatches, taking both the classification accuracy and time efficiency into consideration. By employing tactile-SIFT, a recognition rate of 91.33% has been achieved with a dictionary size of 50 clusters using only 15 touches

Challenges and Opportunities for Information Systems Research During and After Coronavirus

The COVID-19 pandemic has affected world-wide business and management immensely. Furthermore, challenges and opportunities for information systems research during and after coronavirus have emerged. To better understand the impacts of COVID-19 pandemic in the field of information systems (IS), and also as a part of the 2020 AIS SIG-ISAP Workshop on Information Systems in Asia Pacific (ISAP) being held prior ICIS-2020, we organized a panel to address this important issue with three distinguished information systems researchers. The panelists identified and discussed the challenges and opportunities for information systems research during and after Coronavirus. In addition, constructive suggestions for aspiring young scholars who aim to publish their works in top-tier IS journals have been proposed, which may benefit the IS community in large

Valley-dependent gauge fields for ultracold atoms in square optical superlattices

We propose an experimental scheme to realize the valley-dependent gauge fields for ultracold fermionic atoms trapped in a state-dependent square optical lattice. Our scheme relies on two sets of Raman laser beams to engineer the hopping between adjacent sites populated by two-component fermionic atoms. One set of Raman beams are used to realize a staggered \pi-flux lattice, where low energy atoms near two inequivalent Dirac points should be described by the Dirac equation for spin-1/2 particles. Another set of laser beams with proper Rabi frequencies are added to further modulate the atomic hopping parameters. The hopping modulation will give rise to effective gauge potentials with opposite signs near the two valleys, mimicking the interesting strain-induced pseudo-gauge fields in graphene. The proposed valley-dependent gauge fields are tunable and provide a new route to realize quantum valley Hall effects and atomic valleytronics.Comment: 5+ pages, 2 figures; language polished, references and discussions added; accepted by PR

DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model. It can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model. Based on this guidance strategy, we also build a multi-scale guidance to consider both semantic and geometric alignment. Moreover, a cross-branch self-attention is added to maintain the consistency between the original image and the editing result. Our method, through an efficient design, achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging. It is worth noting that all editing and content preservation signals come from the image itself, and the model does not require fine-tuning or additional modules. Our source code will be available at https://github.com/MC-E/DragonDiffusion

PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective region localization but lack accuracy in defective region shape contour extraction; reconstruction-based methods detected defective region match well with the ground truth defective region shape contour but are noisy. To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation. In the training stage, we learn the common background as a deep image prior by a patch autoencoder (PAE) network. In the inference stage, we formulate anomaly detection as an image decomposition problem with the deep image prior and domain-specific regularizations. By adopting the proposed approach, the defective regions in the image can be accurately extracted in an unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in simulation studies and an industrial dataset in the case study

Distances and classification of amino acids for different protein secondary structures

Window profiles of amino acids in protein sequences are taken as a description of the amino acid environment. The relative entropy or Kullback-Leibler distance derived from profiles is used as a measure of dissimilarity for comparison of amino acids and secondary structure conformations. Distance matrices of amino acid pairs at different conformations are obtained, which display a non-negligible dependence of amino acid similarity on conformations. Based on the conformation specific distances clustering analysis for amino acids is conducted.Comment: 15 pages, 8 figure

Toll-like receptor 2 -196 to -174 del polymorphism influences the susceptibility of Han Chinese people to Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 2 (<it>TLR2</it>) represents a reasonable functional and positional candidate gene for Alzheimer's disease (AD) as it is located under the linkage region of AD on chromosome 4q, and functionally is involved in the microglia-mediated inflammatory response and amyloid-β clearance. The -196 to -174 del polymorphism affects the <it>TLR2 </it>gene and alters its promoter activity.</p> <p>Methods</p> <p>We recruited 800 unrelated Northern Han Chinese individuals comprising 400 late-onset AD (LOAD) patients and 400 healthy controls matched for gender and age. The -196 to -174 del polymorphism in the <it>TLR2 </it>gene was genotyped using the polymerase chain reaction (PCR) method.</p> <p>Results</p> <p>There were significant differences in genotype (P = 0.026) and allele (P = 0.009) frequencies of the -196 to -174 del polymorphism between LOAD patients and controls. The del allele was associated with an increased risk of LOAD (OR = 1.31, 95% CI = 1.07-1.60, Power = 84.9%). When these data were stratified by apolipoprotein E (<it>ApoE</it>) ε4 status, the observed association was confined to <it>ApoE </it>ε4 non-carriers. Logistic regression analysis suggested an association of LOAD with the polymorphism in a recessive model (OR = 1.64, 95% CI = 1.13-2.39, Bonferroni corrected P = 0.03).</p> <p>Conclusions</p> <p>Our data suggest that the -196 to -174 del/del genotype of <it>TLR2 </it>may increase risk of LOAD in a Northern Han Chinese population.</p

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate structure control is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and small T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, and achieve rich control and editing effects. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.Comment: Tech Report. GitHub: https://github.com/TencentARC/T2I-Adapte

Dynamical Decoupling of Qubits in Spin Bath under Periodic Quantum Control

We investigate the feasibility for the preservation of coherence and entanglement of one and two spin qubits coupled to an interacting quantum spin-1/2 chain within the dynamical decoupling (DD) scheme. The performance is examined by counting number of computing pulses that can be applied periodically with period of $T$ before qubits become decoherent, while identical decoupling pulse sequence is applied within each cycle. By considering pulses with mixed directions and finite width controlled by magnetic fields, it is shown that pulse-width accumulation degrades the performance of sequences with larger number of pulses and feasible magnetic fields in practice restrict the consideration to sequences with number of decoupling pulses being less than 10 within each cycle. Furthermore, within each cycle $T$, exact nontrivial pulse sequences are found for the first time to suppress the qubit-bath coupling to $O(T^{N+1})$ progressively with minimum number of pulses being $4,7,12$ for $N=1,2,3$. These sequences, when applied to all qubits, are shown to preserve both the entanglement and coherence. Based on time-dependent density matrix renormalization, our numerical results show that for modest magnetic fields (10-40 Tesla) available in laboratories, the overall performance is optimized when number of pulses in each cycle is 4 or 7 with pulse directions be alternating between x and z. Our results provide useful guides for the preservation of coherence and entanglement of spin qubits in solid state.Comment: 11 pages, 9 figure