Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the results of early and elective laparoscopic cholecystectomy in Oltu State Hospital: a retrospective 3-year study

Introduction: The objective of this study was to present the experience of 1 center with early and elective laparoscopic cholecystectomy (LC), which can be performed in state hospitals but requires further specialized treatment when serious complications develop. Materials and Methods: Cases of LC performed as an elective or an emergency procedure in a 100-bed county state hospital between June 2015 and February 2018 were retrospectively reviewed. Patient demographic characteristics; the presence of systemic disease; clinical, laboratory, and radiological findings; transition to open surgery; duration of hospital stay; and complications were analyzed. Results: A total of 142 patients were included in the study, of which 115 (80.9%) were female and 27 (19.1%) were male. The mean age of the patients was 49.31 years (range: 17–75 years). LC was performed early in 35 patients (24.6%) due to acute cholecystitis. The mean duration of surgery was 49.01 minutes (range: 30–120 minutes) for an elective LC, 86.6 minutes (range: 45–180 minutes) for an early LC, and 152 minutes (range: 120–185 minutes) for open surgery. The duration of hospitalization averaged 3.4 days (range: 2–10 days) for elective patients and 4.6 days (range: 3–5 days) for emergency cases. The duration of surgery and hospital stay were extended in patients who underwent early LC or open surgery compared with elective cases. The LC cases that were converted to open surgery included 5 acute cholecystitis patients (14.2%) with difficult dissections and 1 in which the hepatocystic triangle could not be isolated. One elective LC (0.9%) was also transitioned to open surgery due to hemorrhage. Intraoperative biliary injury was encountered in only 1 patient (0.7%) who was subsequently referred to an advanced hospital center. Conclusion: LC can be reliably performed in county state hospitals when there is adequate laparoscopic experience, knowledge, equipment, and assistant health personnel available. In cases of complications, directing those patients to more advanced centers where hepatopancreato-biliary surgery is performed is thought to be beneficial regarding morbidity and mortality.
Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks

The harnessing of modern computational abilities for many-body wave-function representations is naturally placed as a prominent avenue in contemporary condensed matter physics. Specifically, highly expressive computational schemes that are able to efficiently represent the entanglement properties of many-particle systems are of interest. In the seemingly unrelated field of machine learning, deep network architectures have exhibited an unprecedented ability to tractably encompass the dependencies characterizing hard learning tasks such as image classification. However, key questions regarding deep learning architecture design still have no adequate theoretical answers. In this paper, we establish a Tensor Network (TN) based common language between the two disciplines, which allows us to offer bidirectional contributions. By showing that many-body wave-functions are structurally equivalent to mappings of ConvACs and RACs, we construct their TN equivalents, and suggest quantum entanglement measures as natural quantifiers of dependencies in such networks. Accordingly, we propose a novel entanglement based deep learning design scheme. In the other direction, we identify that an inherent re-use of information in state-of-the-art deep learning architectures is a key trait that distinguishes them from standard TNs. Therefore, we employ a TN manifestation of information re-use and construct TNs corresponding to powerful architectures such as deep recurrent and overlapping convolutional networks. This allows us to demonstrate that the entanglement scaling supported by state-of-the-art deep learning architectures matches that of MERA TN in 1D, and that they support volume law entanglement in 2D polynomially more efficiently than RBMs. We thus provide theoretical motivation to shift trending neural-network based wave-function representations closer to state-of-the-art deep learning architectures.
Background Estimation Using Principal Component Analysis Based on Limited Memory Block Krylov Subspace Optimization

Given a video of 𝑀 frames of size ℎ × 𝑤. Background components of a video are the elements matrix which relative constant over 𝑀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 𝑤 × 𝑀) into 2 dimensions one (𝑁 × 𝑀), where 𝑁 is a linear array of size ℎ × 𝑤. The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
Improved pseudorandomness for unordered branching programs through local monotonicity

We present an explicit pseudorandom generator with seed length Õ((logn)w+1) for read-once, oblivious, width w branching programs that can read their input bits in any order. This improves upon the work of Impagliazzo, Meka and Zuckerman (FOCS’12) where they required seed length n1/2+o(1). A central ingredient in our work is the following bound that we prove on the Fourier spectrum of branching programs. For any width w read-once, oblivious branching program B:{0,1}n→ {0,1}, any k ∈ {1,…,n}, [complex formula not displayed] This settles a conjecture posed by Reingold, Steinke and Vadhan (RANDOM’13). Our analysis crucially uses a notion of local monotonicity on the edge labeling of the branching program. We carry critical parts of our proof under the assumption of local monotonicity and show how to deduce our results for unrestricted branching programs.
On the Dichotomy of Debugging Behavior Among Programmers

Debugging is an inevitable activity in most software projects, often difficult and more time-consuming than expected, giving it the nickname the "dirty little secret of computer science. " Surprisingly, we have little knowledge on how software engineers debug software problems in the real world, whether they use dedicated debugging tools, and how knowledgeable they are about debugging. This study aims to shed light on these aspects by following a mixed-methods research approach. We conduct an online survey capturing how 176 developers reflect on debugging. We augment this subjective survey data with objective observations on how 458 developers use the debugger included in their integrated development environments (IDEs) by instrumenting the popular Eclipse and IntelliJ IDEs with the purpose-built plugin WatchDog 2.0. To clarify the insights and discrepancies observed in the previous steps, we followed up by conducting interviews with debugging experts and regular debugging users. Our results indicate that IDE-provided debuggers are not used as often as expected, as "printf debugging" remains a feasible choice for many programmers. Furthermore, both knowledge and use of advanced debugging features are low. These results call to strengthen hands-on debugging experience in computer science curricula and have already refined the implementation of modern IDE debuggers.
Connecting intra-family generations with ambient intelligent systems

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How Modern News Aggregators Help Development Communities Shape and Share Knowledge

Many developers rely on modern news aggregator sites such as reddit and hn to stay up to date with the latest technological developments and trends. In order to understand what motivates developers to contribute, what kind of content is shared, and how knowledge is shaped by the community, we interviewed and surveyed developers that participate on the reddit programming subreddit and we analyzed a sample of posts on both reddit and hn. We learned what kind of content is shared in these websites and developer motivations for posting, sharing, discussing, evaluating, and aggregating knowledge on these aggregators, while revealing challenges developers face in terms of how content and participant behavior is moderated. Our insights aim to improve the practices developers follow when using news aggregators, as well as guide tool makers on how to improve their tools. Our findings are also relevant to researchers that study developer communities of practice.
DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation

The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far.
Scotty: Efficient Window Aggregation for Out-of-Order Stream Processing

Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of-order processing and session windows. Out-of-order processing is a key requirement to deal with delayed tuples in case of source failures such as temporary sensor outages. Session windows are widely used to separate different periods of user activity from each other. In this paper, we present Scotty, a high throughput operator for window discretization and aggregation. Scotty splits streams into non-overlapping slices and computes partial aggregates per slice. These partial aggregates are shared among all concurrent queries with arbitrary combinations of tumbling, sliding, and session windows. Scotty introduces the first slicing technique which (1) enables stream slicing for session windows in addition to tumbling and sliding windows and (2) processes out-of-order tuples efficiently. Our technique is generally applicable to a broad group of dataflow systems which use a unified batch and stream processing model. Our experiments show that we achieve a throughput an order of magnitude higher than alternative state-of-the-art solutions.
Factors Affecting Adequacy of Prenatal Care in Suburban Women of Southeast Iran: A Cross-sectional Study

Introduction: Prenatal and delivery care should be considered as a priority for women and society’s health. Residents of the suburbs are among the groups who do not have enough access to such services. Aim: The present study aimed to determine the pregnancy care as well as factors affecting adequacy of prenatal care for suburban women in Southeast Iran. Materials and Methods: This cross-sectional study was conducted from July 2016 to December 2016. A total of 384 women were evaluated by a single stage cluster random sampling. Inclusion criteria included women living in Zahedan suburbs areas and women in their reproductive ages who gave birth. Exclusion criteria was the history of diseases affecting pregnancy and multiple pregnancies. Safe motherhood questionnaire was completed by face to face interview in order to gather data. The questionnaire includes items for assessing prenatal and delivery care. Descriptive chi-square and Spearman’s correlation coefficient tests were used to analyse data. SPSS version 20.0 software was used for analyses. A p-value <0.05 was considered significant. Results: A total of (n=372 96.9%) of subjects received prenatal care but (n=189 50.8%) of them received inadequate care. There was a statistically significant relationship between adequate care and women’s education level (p-value=0.009) marriage age (p-value =0.001) husband’s education (p-value=0.05) husband’s employment status (p=0.01) age in the first pregnancy (p-value=0.003) the time intervals among pregnancies (p-value=0.04) place of receiving care (p-value=0.004) and health care provider (p-value=0.006). Conclusion: We found that most of suburban women did not receive adequate prenatal care although free health services were available for these mothers. Individual familial features care provider and place of receiving services were found effective for the quality and productivity of such services. © 2018 Journal of Clinical and Diagnostic Research. All rights reserved.
Collision rate ansatz for quantum integrable systems

For quantum integrable systems the currents averaged with respect to a generalized Gibbs ensemble are revisited. An exact formula is known, which we call "collision rate ansatz". While there is considerable work to confirm this ansatz in various models, our approach uses the symmetry of the current-charge susceptibility matrix, which holds in great generality. Besides some technical assumptions, the main input is the availability of a self-conserved current, i.e. some current which is itself conserved. The collision rate ansatz is then derived. The argument is carried out in detail for the Lieb-Liniger model and the Heisenberg XXZ chain. The Fermi-Hubbard model is not covered, since no self-conserved current seems to exist. It is also explained how from the existence of a boost operator a self-conserved current can be deduced.
A Simple Yet Strong Pipeline for HotpotQA

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular multi-hop datasets really justify this added design complexity? Our results suggest that the answer may be no, because even our simple pipeline based on BERT, named Quark, performs surprisingly well. Specifically, on HotpotQA, Quark outperforms these models on both question answering and support identification (and achieves performance very close to a RoBERTa model). Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences independently of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences. The strong performance of Quark resurfaces the importance of carefully exploring simple model designs before using popular benchmarks to justify the value of complex techniques.
Entities as Experts: Sparse Memory Access with Entity Supervision

We focus on the problem of capturing declarative knowledge in the learned parameters of a language model. We introduce a new model, Entities as Experts (EaE), that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into sequence models, EaE's entity representations are learned directly from text. These representations capture sufficient knowledge to answer TriviaQA questions such as "Which Dr. Who villain has been played by Roger Delgado, Anthony Ainley, Eric Roberts? ". EaE outperforms a Transformer model with $30\times$ the parameters on this task. According to the Lama knowledge probes, EaE also contains more factual knowledge than a similar sized Bert. We show that associating parameters with specific entities means that EaE only needs to access a fraction of its parameters at inference time, and we show that the correct identification, and representation, of entities is essential to EaE's performance. We also argue that the discrete and independent entity representations in EaE make it more modular and interpretable than the Transformer architecture on which it is based.
Thermal conductivity of porous polycrystalline PbTe

The efficiency of thermoelectric materials is enhanced by lowering their thermal conductivity. At intermediate temperatures, PbTe is one of the most promising materials. Grain boundaries can lower its inherently low thermal conductivity by scattering and confining phonons. Using a phase-field model parametrized by molecular dynamics, we find that in the porous material the thermal conductivity of PbTe is reduced by up to 35 % at low temperatures. We observe that a phase transition at a finite density of voids governs the kinetics of impeding grain growth by Zener pinning.
The $r$-ELSV Formula via Localisation on the Moduli Space of Stable Maps with Divisible Ramification

The moduli space of stable maps with divisible ramification uses $r$-th roots of a canonical ramification section to parametrise stable maps whose ramification orders are divisible by a fixed integer $r$. In this article, a virtual fundamental class is constructed while letting domain curves have a positive genus; hence removing the restriction of the domain curves being genus zero. We apply the techniques of virtual localisation and obtain the $r$-ELSV formula as an intersection of the virtual class with a pullback via the branch morphism.
4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics

4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6–5.8% and 1.1–3.8% in the phantom data and normal volunteer data, respectively.
Datasets for Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as batch RL, refers to the setting where a policy must be learned from a dataset of previously collected data, without additional online data collection. In supervised learning, large datasets and complex deep neural networks have fueled impressive progress, but in contrast, conventional RL algorithms must collect large amounts of onpolicy data and have had little success leveraging previously collected datasets. As a result, existing RL benchmarks are not well-suited for the offline setting, making progress in this area difficult to measure. To design a benchmark tailored to offline RL, we start by outlining key properties of datasets relevant to applications of offline RL. Based on these properties, we design a set of benchmark tasks and datasets that evaluate offline RL algorithms under these conditions. Examples of such properties include: datasets generated via hand-designed controllers and human demonstrators, multi-objective datasets, where an agent can perform different tasks in the same environment, and datasets consisting of a heterogeneous mix of high-quality and low-quality trajectories. By designing the benchmark tasks and datasets to reflect properties of real-world offline RL problems, our benchmark will focus research effort on methods that drive substantial improvements not just on simulated benchmarks, but ultimately on the kinds of real-world problems where offline RL will have the largest impact.
A recurrent cycle consistency loss for progressive face-to-face synthesis

This paper addresses a major flaw of the cycle consistency loss when used to preserve the input appearance in the face-to-face synthesis domain. In particular, we show that the images generated by a network trained using this loss conceal a noise that hinders their use for further tasks. To overcome this limitation, we propose a “recurrent cycle consistency loss” which for different sequences of target attributes minimises the distance between the output images, independent of any intermediate step. We empirically validate not only that our loss enables the re-use of generated images, but that it also improves their quality. In addition, we propose the very first network that covers the task of unconstrained landmark-guided face-to-face synthesis. Contrary to previous works, our proposed approach enables the transfer of a particular set of input features to a large span of poses and expressions, whereby the target landmarks become the ground-truth points. We then evaluate the consistency of our proposed approach to synthesise faces at the target landmarks. To the best of our knowledge, we are the first to propose a loss to overcome the limitation of the cycle consistency loss, and the first to propose an “in-the-wild” landmark guided synthesis approach. Code and models for this paper can be found in https://github.com/ESanchezLozano/GANnotation.
Cyclic and Convolutional Codes With Locality

Locally recoverable (LRC) codes and their variants have been extensively studied in recent years. In this paper we focus on cyclic constructions of LRC codes and derive conditions on the zeros of the code that support the property of hierarchical locality. As a result, we obtain a general family of hierarchical LRC codes for a new range of code parameters. We also observe that our approach enables one to represent an LRC code in quasicyclic form, and use this representation to construct tail-biting convolutional LRC codes with locality. Among other results, we extend the general approach to cyclic codes with locality to multidimensional cyclic codes, yielding new families of LRC codes with availability, and construct a family of $q$ -ary cyclic hierarchical LRC codes of unbounded length.