Shocks in sand flowing in a silo

We study the formation of shocks on the surface of a granular material draining through an orifice at the bottom of a quasi-two dimensional silo. At high flow rates, the surface is observed to deviate strongly from a smooth linear inclined profile giving way to a sharp discontinuity in the height of the surface near the bottom of the incline, the typical response of a choking flow such as encountered in a hydraulic jump in a Newtonian fluid like water. We present experimental results that characterize the conditions for the existence of such a jump, describe its structure and give an explanation for its occurrence.Comment: 5 pages, 7 figure

Fermion zero modes in the vortex background of a Chern-Simons-Higgs theory with a hidden sector

In this paper we study a $2+1$ dimensional system in which fermions are coupled to the self-dual topological vortex in $U(1) \times U(1)$ Chern-Simons theory, where both $U(1)$ gauge symmetries are spontaneously broken. We consider two Abelian Higgs scalars with visible and hidden sectors coupled to a fermionic field through three interaction Lagrangians, where one of them violates the fermion number. Using a fine tuning procedure, we could obtain the number of the fermionic zero modes which is equal to the absolute value of the sum of the vortex numbers in the visible and hidden sectors.Comment: 10 page

Automated Analysis of Intracranial Aneurysm Morphology and Dynamics from CTA Data

The worst headache of his life, a sudden onset of severe headache accompanied by nausea, blurred vision, stiff neck and loss of consciousness, happened when he was simply at home doing daily activities. He had no symptoms before it happened and after he was taken to the hospital, he was diagnosed with aneurysmal subarachnoid hemorrhage. Cerebrovascular diseases, mainly stroke, are the second leading causes of death worldwide according to the WHO. Approximately 5% to 15% of stroke cases have aneurismal origin with a 30-day mortality rate of 45%. Among the survivors 30% has moderate-to-severe disabilities . No less than an estimated 2% of the population has an intracranial aneurysm but fortunately only a few of them rupture, with an annual estimated risk of 0.7% . Research shows that most aneurysms are small and 50% to 80% of them do not rupture during the course of a person’s life. Between 10% to 30% of the patients have multiple aneurysms

Covert Communication over Classical-Quantum Channels

The square root law (SRL) is the fundamental limit of covert communication over classical memoryless channels (with a classical adversary) and quantum lossy-noisy bosonic channels (with a quantum-powerful adversary). The SRL states that $\mathcal{O}(\sqrt{n})$ covert bits, but no more, can be reliably transmitted in $n$ channel uses with $\mathcal{O}(\sqrt{n})$ bits of secret pre-shared between the communicating parties. Here we investigate covert communication over general memoryless classical-quantum (cq) channels with fixed finite-size input alphabets, and show that the SRL governs covert communications in typical scenarios. %This demonstrates that the SRL is achievable over any quantum communications channel using a product-state transmission strategy, where the transmitted symbols in every channel use are drawn from a fixed finite-size alphabet. We characterize the optimal constants in front of $\sqrt{n}$ for the reliably communicated covert bits, as well as for the number of the pre-shared secret bits consumed. We assume a quantum-powerful adversary that can perform an arbitrary joint (entangling) measurement on all $n$ channel uses. However, we analyze the legitimate receiver that is able to employ a joint measurement as well as one that is restricted to performing a sequence of measurements on each of $n$ channel uses (product measurement). We also evaluate the scenarios where covert communication is not governed by the SRL

Practical segmentation methods for logical and geometric layout analysis to Improve scanned PDF accessibility to vision impaired

The use of electronic documents has rapidly increased in recent decades and the PDF is one the most commonly used electronic document formats. A scanned PDF is an image and does not actually contain any text. For the vision–impaired user who is dependent upon a screen reader to access this information, this format is not useful. Thus addressing PDF accessibility through assistive technology has now become an important concern. PDF layout analysis provides precious formatting information that supports PDF component classification. This classification facilitates the tag generation. Accurate tagging produces a searchable and navigable scanned PDF document. This paper describes several practical segmentation methods which are easy to implement and efficient for PDF layout analysis so that the scanned PDF document can be navigated or searched using assistive technologies

A method to provide high volume transaction outputs accessibility to vision Impaired using layout analysis

The Documents in the financial services, insurance, utilities, and government sectors typically require a high volume of PDF documents to be generated which are stored for presentment or archived for legal purposes. As high volume transactional output (HVTO) demands put increasing pressure on online presentment capabilities, accessibility has become a growing concern. In particular, access to these files proposes significant challenges when these documents are presented to visually impaired people using assistive technologies (i.e. screen readers). Since it is rare that all recipients are prepared to accept electronic delivery of their documents, a large portion of the documents is still printed as PDFs. In an online billing system, bills are sent to customers’ email accounts as attached PDF files or HTML links. These bills in the most cases are neither accessible through assistive technologies nor useable by vision-impaired customers. This paper provides a method for HVTO documents automatic transformation to an accessible and navigable Mark-up format such as XML or Digital Accessible Information System (DAISY)

Comparative Analysis of the Chemical Composition of Juniperus Excelsa Ssp. Polycarpos Bark and Wood Extracts

In the present study, extracts from the bark and wood of Juniperus excelsa ssp. polycarpos were obtained with acetone solvent. Chemical composition were analyzed and compared by gas chromatography-mass spectrometry (GC-MS). The results showed that the major components identified in the bark acetone extract as trimethylsilyl (TMS) derivatives were β-d-glucofuranose, 1,2,3,5,6-pentakis-O-(TMS) (19.97%), followed by pimaric acid TMS (18.89%), d-mannopyranose, 1,2,3,4,6-pentakis-O-(TMS) (13.90%), d-fructose, 1,3,4,5,6-pentakis-O-(TMS) (12.37%). The major components identified in the wood acetone extract as trimethylsilyl (TMS) derivatives were pimaric acid TMS (24.56%), followed by α-d-glucopyranoside, 1,3,4,6-tetrakis-O-(TMS)-β-d-fructofuranosyl 2,3,4,6-tetrakis-O-(TMS) (21.39%), β-d-galactopyranose, 1,2,3,4,6-pentakis-O-(TMS) (12.10%), d-glucose, 2,3,4,5,6-pentakis-O-(TMS) (9.97%), trifluoromethyl-bis-(TMS)methyl ketone (9.32%). One of the more important components identified both in the bark and wood extracts was pimaric acid TMS. Cedrol as the essential oil was found in the acetone wood extract (0.72%)

Beta oscillations following performance feedback predict subsequent recall of task-relevant information

Reward delivery in reinforcement learning tasks elicits increased beta power in the human EEG over frontal areas of the scalp but it is unclear whether these 20-30 Hz oscillations directly facilitate reward learning. We previously proposed that frontal beta is not specific to reward processing but rather reflects the role of prefrontal cortex in maintaining and transferring task-related information to other brain areas. To test this proposal, we had subjects perform a reinforcement learning task followed by a memory recall task in which subjects were asked to recall stimuli associated either with reward feedback (Reward Recall condition) or error feedback (Error Recall condition). We trained a classifier on post-feedback beta power in the Reward Recall condition to discriminate trials associated with reward feedback from those associated with error feedback and then tested the classifier on post-feedback beta power in the Error Recall condition. Crucially, the model classified error-related beta in the Error Recall condition as reward-related. The model also predicted stimulus recall from post-feedback beta power irrespective of feedback valence and task condition. These results indicate that post-feedback beta power is not specific to reward processing but rather reflects a more general task-related process

An integrated neural network algorithm for optimum performance assessment of auto industry with multiple outputs and corrupted data and noise

In the real world encountering with noisy and corrupted data is unavoidable. Auto industry sector (AIS) as a one of the significant industry encounters with noisy and corrupted data regarding to its rapid development. Therefore, developing the performance assessment in this situation is so helpful for this industry. As Data envelopment Analysis (DEA) could not deal with noisy and corrupted data, the alternative method(s) is very important. As one of excellent and promising feature of artificial neural networks (ANNs) are theirs flexibility and robustness in noisy situation, they are a good alternative. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques for efficiency assessment in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores of auto industry in various countries, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of AIS on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Another feature of proposed algorithm is its ability to calculate efficiency for multiple outputs. An example using real data is presented for illustrative purposes. In the application to the auto industries, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. To test the robustness of the efficiency results of the proposed method, the ability of proposed ANN algorithm in dealing with noisy and corrupted data is compared with Data Envelopment Analysis (DEA). Results of the robustness check show that the proposed algorithm is much more robust to the noise and corruption in input data than DEA