Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment of Large and Complicated Scalp Defects with Free Flap Transfer

Background Large scalp defects, especially those complicated by calvarial defects, titanium mesh exposure, or cerebrospinal fluid (CSF) leak, pose a challenge for the neurosurgeon and plastic surgeon. Here, we describe our experience of reconstructing the complex scalp defect with free flap transfer. Methods From October 2012 to September 2017, 8 patients underwent free flap transfer for the reconstruction of the scalp or complicated scalp and calvarial defects. Five patients presented with scalp tumor and the other 3 patients with scalp necrosis or ulceration (2 patients with titanium plate exposure). Seven anterolateral thigh flaps and one radial forearm flap were harvested and employed. The clinical data, including defect characteristics, flap type, complications, and outcomes, were recorded and analyzed. Results Five patients were pathologically diagnosed with malignant tumor, and 3 of them were given further radiotherapy. For the 2 patients with exposure of titanium plate, no titanium plate was removed. For the patient with scalp necrosis after decompressive craniectomy accompanied by CSF leakage, the CSF leak was stopped after reconstruction. The size of the flaps ranged from 3 to 14 cm in width and 4 to 18 cm in length. No flap failure occurred in these cases. From follow-up to the present, no ulceration or necrosis occurred. Conclusions Free flap transfer is an ideal method for the reconstruction of large, complicated scalp defects with a one-stage operation. The anterolateral thigh flap is favored because of its durability, adjustability, water tightness, and infection prevention.
Prevalence and Antibiogram of Acinetobacter spp. Isolated from Various Clinical Samples in a Tertiary Care Hospital, Bathinda. -

Introduction: Acinetobacter, once considered as an opportunistic pathogen has recently emerged as an important nosocomial pathogen. An increase in antibiotic resistance among isolates of the organism during recent years has made these infections difficult to treat. Material & methods: The study was conducted to determine prevalence and antibiotic susceptibility pattern of Acinetobacterspp isolated from various clinical samples collected from patients admitted in Intensive care unit and various wards of the hospital over a period of one year (July 2015 to June 2015). Results: Out of 950 clinical samples, 460 (48.4%) yielded significant growth and out of these positive cultures; 47 (10.2%) Acinetobacterspp were isolated. Majority of isolates (68.1%) were obtained from Intensive Care Unit. Maximum sensitivity of Acinetobacterspp was seen towards polymyxin B (97.8%), colistin (95.8%) followed by imipenem (57.4%) and meropenem (44.6%). Thirty six (76.6%) isolates were found to be multidrug resistant. Conclusion: To avoid resistance, antibiotics should be used judiciously and empirical therapy should be determined for each hospital according to the resistance rates of the hospital.
Collective vibrations of confined levitating droplets

We report a new type of fluid-based driven dissipative oscillator system consisting of a lattice of millimetric fluid droplets bouncing on a vertically vibrating liquid bath and bound within an annular ring. We characterize the system behavior as it is energized through a progressive increase in the bath's vibrational acceleration. Depending on the number of drops, the onset of motion of the lattice may take the form of either out-of-phase oscillations or a striking solitary wave-like instability. Theoretical modeling demonstrates that these behaviors may be attributed to different bifurcations at the onset of instability. The results presented here demonstrate the potential and utility of the walking droplet system as a platform for investigating wave-mediated, inertial, non-equilibrium particle dynamics at the macroscale.
From the pseudogap metal to the Fermi liquid using ancilla qubits

We propose a new parton theory of the hole-doped cuprates, describing the evolution from the pseudogap metal with small Fermi surfaces to the conventional Fermi liquid with a large Fermi surface. We introduce 2 ancilla qubits per square lattice site, and employ them to obtain a variational wavefunction of a fractionalized Fermi liquid for the pseudogap metal state. We propose a multi-layer Hamiltonion for the cuprates, with the electrons residing in the 'physical' layer, and the ancilla qubits in two 'hidden' layers: the hidden layers can be decoupled from the physical layer by a canonical transformation which leaves the hidden layers in a trivial gapped state. This Hamiltonian yields an emergent gauge theory which describes not only the fractionalized Fermi liquid, but also the conventional Fermi liquid, and possible exotic intermediate phases and critical points. The fractionalized Fermi liquid has hole pockets with quasiparticle weight which is large only on "Fermi arcs", and fermionic spinon excitations which carry charges of the emergent gauge fields.
Conversational Contextual Bandit: Algorithm and Application

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses a critical issue in applications like recommender systems, since users may need to provide feedbacks on a lot of uninterested items. To accelerate the learning speed, we generalize contextual bandit to conversational contextual bandit. Conversational contextual bandit leverages not only behavioral feedbacks on arms (e.g., articles in news recommendation), but also occasional conversational feedbacks on key-terms from the user. Here, a key-term can relate to a subset of arms, for example, a category of articles in news recommendation. We then design the Conversational UCB algorithm (ConUCB) to address two challenges in conversational contextual bandit: (1) which key-terms to select to conduct conversation, (2) how to leverage conversational feedbacks to accelerate the speed of bandit learning. We theoretically prove that ConUCB can achieve a smaller regret upper bound than the traditional contextual bandit algorithm LinUCB, which implies a faster learning speed. Experiments on synthetic data, as well as real datasets from Yelp and Toutiao, demonstrate the efficacy of the ConUCB algorithm.
Proactive Allocation as Defense for Malicious Co-residency in Sliced 5G Core Networks

Malicious co-residency in virtualized networks poses a real threat. The next-generation mobile networks heavily rely on virtualized infrastructure, and network slicing has emerged as a key enabler to support different virtualized services and applications in the 5G network. However, allocating network slices efficiently while providing a minimum guaranteed level of service as well as providing defense against the threat of malicious co-residency in a mobile core is challenging. To address this question, in our previous work, we proposed an optimization model to allocate slices. In this work, we analyze the defense against the malicious co-residency using our optimization-based allocation.
Phase-Aware Speech Enhancement with a Recurrent Two Stage Net work

We propose a neural network-based speech enhancement (SE) method called the phase-aware recurrent two stage network (rTSN). The rTSN is an extension of our previously proposed two stage network (TSN) framework. This TSN framework was equipped with a boosting strategy (BS) that initially estimates the multiple base predictions (MBPs) from a prior neural network (pri-NN) and then the MBPs are aggregated by a posterior neural network (post-NN) to obtain the final prediction. The TSN outperformed various state-of-the-art methods; however, it adopted the simple deep neural network as pri-NN. We have found that the pri-NN affects the performance (in perceptual quality), more than post-NN; therefore we adopted the long short-term memory recurrent neural network (LSTM-RNN) as pri-NN to boost the context information usage within speech signals. Further, the TSN framework did not consider the phase reconstruction, though phase information affected the perceptual quality. Therefore, we proposed to adopt the phase reconstruction method based on the Griffin-Lim algorithm. Finally, we evaluated rTSN with baselines such as TSN in perceptual quality related metrics as well as the phone recognition error rate.
On the Fairness of Randomized Trials for Recommendation With Heterogeneous Demographics and Beyond

Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk. A general approach to correct MNAR bias is performing small Randomized Controlled Trials (RCTs), where an additional uniform policy is employed to randomly assign items to each user. In this work, we concentrate on the fairness of RCTs under both homogeneous and heterogeneous demographics, especially analyzing the bias for the least favorable group on the latter setting. Considering RCTs' limitations, we propose a novel Counterfactual Robust Risk Minimization (CRRM) framework, which is totally free of expensive RCTs, and derive its theoretical generalization error bound. At last, empirical experiments are performed on synthetic tasks and real-world data sets, substantiating our method's superiority both in fairness and generalization.
A Proposal of Deep Learning Training Data Generator for Image Recognition

- At present, image recognition technologies utilizing the deep learning are developing rapidly, and some cases have been reported in which their recognition accuracy has exceeded the human vision. On the other hand, in order to conduct deep learning, it is necessary to accumulate a large amount of training data. And, this often becomes the obstacles to applying the deep learning to actual business systems. So, in this study, we pro-pose a method to generate the images of training data automatically by using computer graphics (CG). For example, in inventory management of parts, target objects are composed only of inventory shelves and parts. That is, although it is diﬃcult to accumulate a large number of their actual images, it is expected that these images can be easily generated by using CG. However, while it becomes easy to prepare a large amount of the training data by this method, there may be a deviation between the actual images and generated CG images. Therefore, (cid:12)rstly, we evaluate the in(cid:13)uence of this deviation on the deep learning by using plural kinds of CG images. Next, we conduct experiments using training data in which a part of the CG images are replaced with the actual images, and we evaluate the improvement of the accuracy corresponding to the ratio of actual images. As a result, we show that by mixing a certain ratio of actual images, eﬀective training data for the deep learning can be generated with CG images.
Graphene Effusion-based Gas Sensor

Porous, atomically thin graphene membranes have interesting properties for filtration and sieving applications because they can accommodate small pore sizes, while maintaining high permeability. These membranes are therefore receiving much attention for novel gas and water purification applications. Here we show that the atomic thickness and high resonance frequency of porous graphene membranes enables an effusion based gas sensing method that distinguishes gases based on their molecular mass. Graphene membranes are used to pump gases through nanopores using optothermal forces. By monitoring the time delay between the actuation force and the membrane mechanical motion, the permeation time-constants of various gases are shown to be significantly different. The measured linear relation between the effusion time constant and the square root of the molecular mass provides a method for sensing gases based on their molecular mass. The presented microscopic effusion based gas sensor can provide a small, low-power alternative for large, high-power, mass-spectrometry and optical spectrometry based gas sensing methods.
A note on the strong Feller property of diffusion processes

In this note we prove the strong Feller property of a strong Markov quasi diffusion process corresponding to an elliptic operator with merely bounded measurable coefficients. We also prove Holder continuity of harmonic functions associated with the quasi diffusion process and Harnack inequality. As an application, we show that for such diffusion processes the probabilistic definition of a regular boundary point coincides with the 'analytic' one. The parabolic counterparts of these results are presented as well. The proofs are adaptations of arguments from \cite{KrS_79} and \cite{Kr_18}.
Local null controllability of a fluid–rigid body interaction problem with Navier slip boundary conditions

The aim of this work is to show the local null controllability of a fluid–solid interaction system by using a distributed control located in the fluid. The fluid is modeled by the incompressible Navier–Stokes system with Navier slip boundary conditions and the rigid body is governed by the Newton laws. Our main result yields that we can drive the velocities of the fluid and of the structure to 0 and we can control exactly the position of the rigid body. One important ingredient consists in a new Carleman estimate for a linear fluid–rigid body system with Navier boundary conditions. This work is done without imposing any geometrical conditions on the rigid body.
Perfecting the Crime Machine

This study explores using different machine learning techniques and workflows to predict crime related statistics, specifically crime type in Philadelphia. We use crime location and time as main features, extract different features from the two features that our raw data has, and build models that would work with large number of class labels. We use different techniques to extract various features including combining unsupervised learning techniques and try to predict the crime type. Some of the models that we use are Support Vector Machines, Decision Trees, Random Forest, K-Nearest Neighbors. We report that the Random Forest as the best performing model to predict crime type with an error log loss of 2.3120.
A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost

An increasing amount of users’ sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users’ information. The privacy parameter bounds the information about the data set leaked by the noisy output. Oftentimes, a data set needs to be used for answering multiple queries, so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of privacy budget spending, which should not exceed the given limit of privacy budget. Moreover, if a query has been answered before and is asked again on the same data set, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design an algorithm to reuse previous noisy responses if the same query is asked repeatedly. In particular, considering that different requests of the same query may have different privacy requirements, our algorithm can set the optimal reuse fraction of the old noisy response and add new noise to minimize the accumulated privacy cost. Furthermore, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. As a result, the owner of the data set will have full knowledge about how the data set has been used and be confident that no new privacy cost will be incurred for answering queries once the specified privacy budget is exhausted.
Cognitive Radio Networks: An Information Theoretic Perspective

Information-theoretic limits of {\it cognitive radio} networks have been under exploration for more than a decade. Although such limits are unknown for many networks, including the simplest case with two pairs of transmitter-receiver, there are several cases for which the capacity limits are obtained either exactly or up to a constant gap. The goal of this chapter is to provide insights into the nature of transmission techniques associated with optimal communication when cognitive radio technology is used. %, with an emphasis on 5G networks. Outlining the state of the art in the information-theoretic analysis of different cognitive systems, we highlight the salient features/points of the capacity-achieving or capacity-approaching strategies that should be considered in wireless network design paradigms based on this technology. In particular, we emphasize the interaction of cognitive radio with emerging technologies for 5G networks.
Silica Integrated Spectrometer Using Highly-Chirped Bragg Gratings

An integrated spectrometer was fabricated using blazed chirped Bragg gratings in a planar silica platform. The grating diffracts light from a waveguide and creates a wavelength dependent focus in a manner similar to a bulk diffraction grating spectrometer. Devices were fabricated with grating period chirp rates in excess of 100 nm/mm, achieving a focal length of 5.5 mm. Correction of coma aberrations resulted in a device with a FWHM resolution of 1.8 nm and operating with a 160 nm bandwidth centered at 1550 nm.
Distributed Sensing With Orthogonal Multiple Access: To Code or not to Code?

We consider the estimation distortion of a distributed sensing system with finite number of sensor nodes, in which the nodes observe a common phenomenon and transmit their observations to a fusion center over orthogonal channels. In particular, we investigate whether the coded scheme (separate source-channel coding) outperforms the uncoded scheme (joint source-channel coding) or not. To this end, we explicitly derive the estimation distortion of a coded heterogeneous sensing system with diverse node and channel configurations. Based on this result, we show that in a homogeneous sensing system with identical node and channel configurations, the coded scheme outperforms the uncoded scheme if the number of nodes is <inline-formula><tex-math notation="LaTeX">$K=1$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">$K=2$</tex-math></inline-formula>. For homogenous sensing systems with <inline-formula><tex-math notation="LaTeX">$K\geq 3$</tex-math></inline-formula> nodes and general heterogeneous sensing systems, we also present explicit conditions for the coded scheme to perform better than the uncoded scheme. Furthermore, we propose to minimize the estimation distortion of heterogeneous sensing systems with hybrid coding, i.e., some nodes use the coded scheme and other nodes use the uncoded scheme. To determine the optimal hybrid coding policy, we develop three greedy algorithms, in which the pure greedy algorithm minimizes distortion greedily, the group greedy algorithm improves performance by using a group of potential sub-polices, and the sorted greedy algorithm reduces computational complexity by using a pre-solved iteration order. Our numerical and Monte Carlo results show that the proposed algorithms closely approach the optimal policy in terms average estimation distortion.
Projectively coresolved Gorenstein flat and ding projective modules

Abstract We give necessary and sufficient conditions in order for the class of projectively coresolved Gorenstein flat modules, (respectively that of projectively coresolved Gorenstein flat modules, ), to coincide with the class of Ding projective modules ( We show that if and only if every Ding projective module is Gorenstein flat. This is the case if the ring R is coherent for example. We include an example to show that the coherence is a sufficient, but not a necessary condition in order to have We also show that over any ring R of finite weak Gorenstein global dimension (this condition is also sufficient, but not necessary). We prove that if the class of Ding projective modules, is covering then the ring R is perfect. And we show that, over a coherent ring R, the converse also holds. We also give necessary and sufficient conditions in order to have where is the class of Gorenstein projective modules.
Note on the parametrized black hole quasinormal ringdown formalism

The parametrized black hole quasinormal ringdown formalism is useful to compute quasinormal mode (QNM) frequencies if a master equation for the gravitational perturbation around a black hole has a small deviation from the Regge-Wheeler or Zerilli equation. In this formalism, the deviation of QNM frequency from general relativity can be calculated by small deviation parameters and model independent coefficients. In this paper, we derive recursion relations for the model independent coefficients. Using these relations, the higher order coefficients are written only by the lower order coefficients. Thus, we only need the lower order coefficients when we numerically compute the model independent coefficients.
Krasovskii and Shifted Passivity-Based Control

In this article, our objective is to develop novel passivity-based control techniques by introducing a new passivity concept named Krasovskii passivity. As a preliminary step, we investigate the properties of Krasovskii passive systems and establish relations among four relevant passivity concepts including Krasovskii passivity. Then, we develop novel dynamic controllers based on Krasovskii passivity and based on shifted passivity.
LiteMORT: A memory efficient gradient boosting tree system on adaptive compact distributions

Gradient boosted decision trees (GBDT) is the leading algorithm for many commercial and academic data applications. We give a deep analysis of this algorithm, especially the histogram technique, which is a basis for the regulized distribution with compact support. We present three new modifications. 1) Share memory technique to reduce memory usage. In many cases, it only need the data source itself and no extra memory. 2) Implicit merging for "merge overflow problem". "merge overflow" means that merge some small datasets to huge datasets, which are too huge to be solved. By implicit merging, we just need the original small datasets to train the GBDT model. 3) Adaptive resize algorithm of histogram bins to improve accuracy. Experiments on two large Kaggle competitions verified our methods. They use much less memory than LightGBM and have higher accuracy. We have implemented these algorithms in an open-source package LiteMORT. The source codes are available at this https URL
What's happened in MOOC Posts Analysis, Knowledge Tracing and Peer Feedbacks? A Review

Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of papers and resources so used are released in this https URL. The coverage statistics and review matrix of the survey are as shown in Figure 1 & Table 1 respectively. Acronym expansions are added in the Appendix Section 4.1.