Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Roux-En-Y Gastric Bypass Surgery on Neurohormonal and Gastrointestinal Physiology: Insights for Future Weight Loss Efforts

Roux-en-Y Gastric Bypass (RYGB), is an effective weight loss intervention for patients failing conventional nonsurgical methods. Despite its popularity, only a subset of patients undergo RYGB due to its cumbersome nature. Furthermore, patients may experience nutritional deficiencies or weight regain after RYGB. This review will delineate the less known impact of RYGB on neurohormonal and gastrointestinal physiology involved in weight loss. Understanding these alterations will contribute to the development of future novel investigations targeting viable weight loss strategies.
Neural mechanisms of social learning in the female mouse

Social interactions are often powerful drivers of learning. In female mice, mating creates a long-lasting sensory memory for the pheromones of the stud male that alters neuroendocrine responses to his chemosignals for many weeks. The cellular and synaptic correlates of pheromonal learning, however, remain unclear. We examined local circuit changes in the accessory olfactory bulb (AOB) using targeted ex vivo recordings of mating-activated neurons tagged with a fluorescent reporter. Imprinting led to striking plasticity in the intrinsic membrane excitability of projection neurons (mitral cells, MCs) that dramatically curtailed their responsiveness, suggesting a novel cellular substrate for pheromonal learning. Plasticity was selectively expressed in the MC ensembles activated by the stud male, consistent with formation of memories for specific individuals. Finally, MC excitability gained atypical activity-dependence whose slow dynamics strongly attenuated firing on timescales of several minutes. This unusual form of AOB plasticity may act to filter sustained or repetitive sensory signals. DOI: http://dx.doi. org/10.7554/eLife.25421. 001
Scalable Deep Learning Logo Detection

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL^2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset "WebLogo-2M" by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SL^2 method over the state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning approaches.
Efficient and Deep Person Re-identification Using Multi-level Similarity

Person Re-Identification (ReID) requires comparing two images of person captured under different conditions. Existing work based on neural networks often computes the similarity of feature maps from one single convolutional layer. In this work, we propose an efficient, end-to-end fully convolutional Siamese network that computes the similarities at multiple levels. We demonstrate that multi-level similarity can improve the accuracy considerably using low-complexity network structures in ReID problem. Specifically, first, we use several convolutional layers to extract the features of two input images. Then, we propose Convolution Similarity Network to compute the similarity score maps for the inputs. We use spatial transformer networks (STNs) to determine spatial attention. We propose to apply efficient depth-wise convolution to compute the similarity. The proposed Convolution Similarity Networks can be inserted into different convolutional layers to extract visual similarities at different levels. Furthermore, we use an improved ranking loss to further improve the performance. Our work is the first to propose to compute visual similarities at low, middle and high levels for ReID. With extensive experiments and analysis, we demonstrate that our system, compact yet effective, can achieve competitive results with much smaller model size and computational complexity.
Leveraging Intel SGX to Create a Nondisclosure Cryptographic library

Enforcing integrity and confidentiality of users' application code and data is a challenging mission that any software developer working on an online production grade service is facing. Since cryptology is not a widely understood subject, people on the cutting edge of research and industry are always seeking for new technologies to naturally expand the security of their programs and systems. Intel Software Guard Extension (Intel SGX) is an Intel technology for developers who are looking to protect their software binaries from plausible attacks using hardware instructions. The Intel SGX puts sensitive code and data into CPU-hardened protected regions called enclaves. In this project we leverage the Intel SGX to produce a secure cryptographic library which keeps the generated keys inside an enclave restricting use and dissemination of confidential cryptographic keys. Using enclaves to store the keys we maintain a small Trusted Computing Base (TCB) where we also perform computation on temporary buffers to and from untrusted application code. As a proof of concept, we implemented hashes and symmetric encryption algorithms inside the enclave where we stored hashes, Initialization Vectors (IVs) and random keys and open sourced the code (this https URL).
Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics

ABSTRACT Cellular automata (CA) have been efficiently used to express the complexity and dynamics of cities at different scales. However, those large-scale simulation models typically use only binary values to represent urbanization states without considering mixed types within a cell. They also ignore differences among the cells in terms of their temporal evolution characteristics at different urbanization stages. This study establishes a gradient CA for solving such problems while considering development differences among the cells. The impervious surface area data was used to detect the urbanization states and temporal evolution trends of the grid cells. Transition rules were determined with the incorporation of urban development theory expressed as an S-shaped curve. China was selected as the case study area to validate the performance of the gradient CA for a national simulation. A comparison was also made to a traditional binary logistic-CA. The results demonstrated that the gradient CA achieved higher accuracies in terms of both spatial patterns and quantitative assessment indices. The simulation pattern derived from the gradient CA can better reflect the local disparity and temporal characteristics of urban dynamics. A national urban expansion for 2050 was also simulated, and is expected to provide important data for ecological assessments.
Operator splitting for partial differential equations with Burgers nonlinearity

We provide a new analytical approach to operator splitting for equations of the type $u_t=Au+u u_x$ where $A$ is a linear differential operator such that the equation is well-posed. Particular examples include the viscous Burgers' equation, the Korteweg-de Vries (KdV) equation, the Benney-Lin equation, and the Kawahara equation. We show that the Strang splitting method converges with the expected rate if the initial data are sufficiently regular. In particular, for the KdV equation we obtain second-order convergence in $H^r$ for initial data in $H^{r+5}$ with arbitrary $r\ge 1$.
Vibration Analysis of U-Shaped Beam Electrothermal Microactuators

This paper presents an analytical model for the vibration analysis of a U-shaped electrothermal microactuators. A U-shaped electrothermal microactuator has a unique geometrical structure which consist of, interconnected multiple thick and thin beam segments. In this proposed model, both the longitudinal and lateral deflections of each beam segment are incorporated. To facilitate the modelling process, a thick beam is replaced by the thin beam segment using the artificial Young's modulus and density so that the deformation and inertia force of the thin beam can be used to equivalently describe that of the thick beam segment. The relationship of the force and deflections in both longitudinal and lateral directions at connected points between adjacent beam segments is formulated with continuity conditions. The frequency equation is then established subjected to two both boundary and continuity conditions. Solving the frequency equation yields the natural frequencies of the U-shaped beam actuator. The analytical results are compared with the finiteelement simulation results using ANSYS software. A good agreement is observed between analytical and simulation data, and hence the proposed vibration analysis model is verified. This model provides the first insight study on the vibration analysis of U-shaped electrothermal actuators, which is essentially crucial to dynamic system modelling, control, and dimension optimizations.
Memory Indexing and its Use in Automated Debugging

Execution comparison examines different executions generated by different program versions, different inputs, or by perturbations. It has a wide range of applications in debugging, regression testing, program comprehension, and security. Meaningful comparison demands that executions are aligned before they are compared, otherwise the resulting differences do not reflect semantic differences. Prior work has focused on aligning executions along the control flow dimension. In this paper, we observe that the memory dimension is also critical and propose a novel solution to align memory locations across different executions. We introduce a canonical representation for memory locations and pointer values called memory indexing. Aligned memory locations across runs share the same index. We formally define the semantics of memory indexing and present a cost-effective design. We also show that memory indexing overcomes an important challenge in automated debugging by enabling robust state replacement across runs.
Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems

Many real-world networks are known to exhibit facts that counter our knowledge prescribed by the theories on network creation and communication patterns. A common prerequisite in network analysis is that information on nodes and links will be complete because network topologies are extremely sensitive to missing information of this kind. Therefore, many real-world networks that fail to meet this criterion under random sampling may be discarded. In this paper we offer a framework for interpreting the missing observations in network data under the hypothesis that these observations are not missing at random. We demonstrate the methodology with a case study of a financial trade network, where the awareness of agents to the data collection procedure by a self-interested observer may result in strategic revealing or withholding of information. The non-random missingness has been overlooked despite the possibility of this being an important feature of the processes by which the network is generated. The analysis demonstrates that strategic information withholding may be a valid general phenomenon in complex systems. The evidence is sufficient to support the existence of an influential observer and to offer a compelling dynamic mechanism for the creation of the network.
On the need for monitor unit calculations as part of a beam commissioning methodology for a radiation treatment planning system

This paper illustrates the need for validating the calculation of monitor units as part of the process of commissioning a photon beam model in a radiation treatment planning system. Examples are provided in which this validation identified subtle errors, either in the dose model or in the implementation of the dose algorithm. These errors would not have been detected if the commissioning process only compared relative dose distributions. A set of beam configurations, with varying field sizes, source‐to‐skin distances, wedges, and blocking, were established to validate monitor unit calculations for two different beam models in two different radiation treatment planning systems. Monitor units calculated using the treatment planning systems were compared with monitor units calculated from point dose calculations from tissue‐maximum ratio (TMR) tables. When discrepancies occurred, the dose models and the code were analyzed to identify the causes of the discrepancies. Discrepancies in monitor unit calculations were both significant (up to 5%) and systematic. Analysis of the dose computation software found: (1) a coordinate system transformation error, (2) mishandling of dose‐spread arrays, (3) differences between dose calculations in the commissioning software and the planning software, and (4) shortcomings in modeling of head scatter. Corrections were made in the beam calculation software or in the data sets to overcome these discrepancies. Consequently, we recommend incorporating validation of monitor unit calculations as part of a photon beam commissioning process. PACS number(s): 87.53. –j, 87.66. –a
Survey and Analysis of Kernel and Userspace Tracers on Linux

As applications and operating systems are becoming more complex, the last decade has seen the rise of many tracing tools all across the software stack. This article presents a hands-on comparison of modern tracers on Linux systems, both in user space and kernel space. The authors implement microbenchmarks that not only quantify the overhead of different tracers, but also sample fine-grained metrics that unveil insights into the tracers’ internals and show the cause of each tracer’s overhead. Internal design choices and implementation particularities are discussed, which helps us to understand the challenges of developing tracers. Furthermore, this analysis aims to help users choose and configure their tracers based on their specific requirements to reduce their overhead and get the most of out of them.
Guided Imagery Improves Mood, Fatigue, and Quality of Life in Individuals With Multiple Sclerosis: An Exploratory Efficacy Trial of Healing Light Guided Imagery

Multiple sclerosis is a disabling and progressive neurological disease that has significant negative effects on health-related quality of life. This exploratory efficacy study examined the effects of Healing Light Guided Imagery (HLGI), a novel variant of guided imagery, compared with a wait-list control in patients with relapsing-remitting multiple sclerosis. Changes in the Beck Depression Inventory, Fatigue Severity Scale, and Multiple Sclerosis Quality of Life instrument (physical and mental components) were compared between groups. Patients who completed HLGI (N = 9) showed significant reductions in depressed mood (P < .05) and fatigue (P < .01) and showed significant gains in physical (P = .01) and mental (P < .01) quality of life compared with journaling (N = 8). Our results suggest that HLGI can improve self-reported physical and mental well-being in patients with relapsing-remitting multiple sclerosis. Further research is needed to study the effectiveness of this therapy, as well as its mind-body mechanisms of action.
Encephalitis induced by a newly discovered ruminant rhadinovirus in a free-living Formosan sambar deer (Rusa unicolor swinhoei)

We documented a case of a free-living Formosan sambar deer (Rusa unicolor swinhoei) infected with a newly discovered ruminant Rhadinovirus (RuRv). Non-purulent encephalitis was the primary histological lesion of the sambar deer. We conducted nested PCR to screen for herpesvirus using generic primers targeting the DNA polymerase gene. In addition, we found that DNA polymerase gene of the sambar deer RuRv was present in the macrophage distributed in the Virchow Robin space with histopathologic lesions by chromogenic in-situ hybridization (CISH). The phylogenetic analysis indicated a high similarity between the viral sequence isolated from fallow deer and our case. This result suggests the possibility of cross-species transmission from other exotic Cervidae reservoir to the Formosan sambar deer.
Integrated Discovery of Location Prediction Rules in Mobile Environment

Pattern-based prediction is one of the widely used approaches to predict the future location of the users in a mobile environment. Currently, pattern-based prediction is performed in two sequential steps: discovering a set of sequential frequent patterns, followed by generating the prediction rules. However, existing methods cannot forecast locations where their support is less than the threshold. Therefore, some useful patterns with low support cannot be discovered which leads to the reduction in the prediction power. This problem mainly comes from applying a two-step sequential approach. This paper discusses this problem and proposes a novel integrated framework for generating the pattern-based prediction rules. It divides database such that each location has a separate partition. Then at each partition, it directly discovers the prediction rules for the corresponding location through applying a local support threshold. To our best knowledge, this is the first work which integrates the mining and prediction steps instead of applying the sequential approach. Through experimental evaluation considering different conditions, our proposed technique demonstrates more accurate and efficient results than the sequential forecasting scheme.
Log4Perf: Suggesting Logging Locations for Web-based Systems' Performance Monitoring

Performance assurance activities are an essential step in the release cycle of software systems. Logs have become one of the most important sources of information that is used to monitor, understand and improve software performance. However, developers often face the challenge of making logging decisions, i.e., neither logging too little and logging too much is desirable. Although prior research has proposed techniques to assist in logging decisions, those automated logging guidance techniques are rather general, without considering a particular goal, such as monitoring software performance. In this paper, we present Log4Perf, an automated approach that provides suggestions of where to insert logging statement with the goal of monitoring web-based systems» software performance. In particular, our approach builds and manipulates a statistical performance model to identify the locations in the source code that statistically significantly influences software performance. To evaluate Log4Perf, we conduct case studies on open source system, i.e., CloudStore and OpenMRS, and one large-scale commercial system. Our evaluation results show that Log4Perf can build well-fit statistical performance models, indicating that such models can be leveraged to investigate the influence of locations in the source code on performance. Also, the suggested logging locations are often small and simple methods that do not have logging statements and that are not performance hotspots, making our approach an ideal complement to traditional approaches that are based on software metrics or performance hotspots. Log4Perf is integrated into the release engineering process of the commercial software to provide logging suggestions on a regular basis.
The contribution of gastroenteropancreatic appetite hormones to protein‐induced satiety

We studied the hormonal mediation of the satiating effect of protein by a randomized, double‐blind, crossover meal‐test study testing three similar meals with varying protein content. Twentyfive men (BMI: 25.9 kg/m2) were exposed to isocaloric meals with fixed 30E% fat and protein adjusted at the expense of carbohydrate: normal (NP) = 14 E%; medium‐high (MHP) = 25 E% (MHP); high (HP) = 50 E% protein. Protein increased satiety and fullness dose‐dependently compared to NP: 7 and 6% greater for MHP, 16 and 19% greater for HP (P<0.001) respectively. Protein also increased GLP‐1 dose‐dependently: 10% for MHP (3.4±2.4, P=0.02), 20% for HP (6.9±2.4, P<0.0001); PYY by 7% for MHP (P=0.0002) and by 14% for HP (P<0.0001). Glucagon was increased by 47% (P<0.0001) for MHP and by 116% (P<0.0001) for HP. HP increased insulin and CCK, and decreased ghrelin and GIP, compared to NP (P<0.05). There was an interaction between GLP‐1 and PYY 3–36 (P=0.03) as a determinant of satiety, besides the additive effect of GLP‐1 (P=0.006) on composite appetite score. We conclude that protein dose‐dependently increases satiety and that postprandial changes in circulating GLP‐1, PYY and glucagon are partly responsible for the appetite suppressant effect of protein.
2010 Isolates : Serializability Enforcement for Concurrent

There has been much recent interest in exploring higher-level concurrency control abstractions such as software transactional memory (STM) to alleviate the complexity of reasoning about interactions among concurrent threads of control. Isolation and atomicity are the two critical properties provided by an STM that guarantee serializability of concurrent actions. Isolation ensures that transactions execute without interference from effects performed by other transactions, and atomicity guarantees that intermediate effects performed by a transaction are not seen by other concurrently executing transactions. While these properties have been primarily designed with shared memory in mind, there has been recent work (5; 6) that explores how atomicity could be leveraged to increase the expressivity of message-passing abstractions such as the first-class synchronous events found in Concurrent ML (CML) (17). Notably, these proposals do not enforce isolation of concurrently executing events, and thus cannot be used to enforce transactional execution of CML programs. In this paper, we consider the introduction of a new event combinator that addresses this significant limitation. An isolate is a combinator that allows a complex event to execute in isolation with other concurrently executing events (including other isolates). By doing so, it enables the integration of a true transactional semantics into a CML-style concurrency model, enabling reasoning about CML programs in terms of serializable event orderings. Incorporating isolation into CML poses a number of challenging problems, however, whose solutions form the focus of this paper.
Building a Semi-Supervised Dataset to Train Journalistic Relevance Detection Models

Annotated data is one of the most important components for supervised learning tasks. To ensure the reliability of the models, this data is usually labeled by several human annotators through volunteering or using Crowdsourcing platforms. However, such approaches are unfeasible (regarding time and cost) in datasets with an enormous number of entries, which in the specific case of journalistic relevance detection in social media posts, is necessary due to the wide scope of topics that can be considered relevant. Therefore, with the goal of building a relevance detection model, we propose an architecture to build a large scale annotated dataset regarding the journalistic relevance of Twitter posts (i.e. tweets). This methodology is based on the predictability of the content in Twitter accounts. Next, we used the retrieved dataset and build relevance detection models, combining text, entities, and sentiment features. Finally, we validated the best model through a smaller manually annotated dataset with posts from Facebook and Twitter. The F1-measure achieved in the validation dataset was 63% which is still far from excellent. However, given the characteristics of the validation data, these results are encouraging since 1) our model is not affected by content from other social networks and 2) our validation dataset was restrained to a specific time interval and specific keywords (which can affect the performance of the model).