Miracle-dz/UniFAR-FTUs huggingface dataset:
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition

As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research. However, approaches remain largely generic, and do not carefully consider the special properties of scenes. In this paper, inspired by the intuitive differences between scenes and objects, we propose Adi-Red, an adaptive approach to discriminative region discovery for scene recognition. Adi-Red uses a CNN classifier, which was pre-trained using only image-level scene labels, to discover discriminative image regions directly. These regions are then used as a source of features to perform scene recognition. The use of the CNN classifier makes it possible to adapt the number of discriminative regions per image using a simple, yet elegant, threshold, at relatively low computational cost. Experimental results on the scene recognition benchmark dataset SUN397 demonstrate the ability of Adi-Red to outperform the state of the art. Additional experimental analysis on the Places dataset reveals the advantages of Adi-Red, and highlight how they are specific to scenes. We attribute the effectiveness of Adi-Red to the ability of adaptive region discovery to avoid introducing noise, while also not missing out on important information.
Regulation of Phagocytosis in Macrophages by Membrane Ethanolamine Plasmalogens

Macrophages, as professional phagocytes of the immune system, possess the ability to detect and clear invading pathogens and apoptotic cells through phagocytosis. Phagocytosis involves membrane reorganization and remodeling events on the cell surface, which play an essential role in innate immunity and tissue homeostasis and the control of inflammation. In this work, we report that cells deficient in membrane ethanolamine plasmalogen demonstrate a reduced capacity to phagocytize opsonized zymosan particles. Amelioration of plasmalogen deficiency in these cells by incubation with lysoplasmalogen results in a significant augmentation of the phagocytic capacity of the cells. In parallel with these increases, restoration of plasmalogen levels in the cells also increases the number and size of lipid rafts in the membrane, reduces membrane fluidity down to levels found in cells containing normal plasmalogen levels, and improves receptor-mediated signaling. Collectively, these results suggest that membrane plasmalogen level determines characteristics of the plasma membrane such as fluidity and the formation of microdomains that are necessary for efficient signal transduction leading to optimal phagocytosis by macrophages.
Graphene Oxide/BiOCl Nanocomposite Films as Efficient Visible Light Photocatalysts

A novel graphene oxide/BiOCl (GO/BiOCl) nanocomposite film was prepared via a spread coating method. In visible-light photocatalytically degrading Rhodamine B (RhB) experiments, 2 wt% GO/BiOCl could degrade 99% of RhB within 1.5 h and the rate constant was 12.2 times higher than that of pure BiOCl. The degradation efficiency still kept at 80% even after 4 recycles, evidencing the relatively good recyclability. The enhancement was attributed to the improvement of visible light adsorption and charge separation. Holes and superoxide radicals·O2- played a major role as reactive species. The values of conduction band and valence band for GO and BiOCl were calculated and a new photocatalytic mechanism of GO/BiOCl nanocomposite was proposed.
Localized Pigmented Villonodular Synovitis of the Posterior Knee Compartment with Popliteal Vessel Compression: A Case Report of Arthroscopic Resection Using Only Anterior Knee Portals

Background Pigmented villonodular synovitis is a rare pathology causing hyperplasia of the synovium. It mostly affects young populations and most commonly the knee joint. It rarely affects the posterior compartment of the knee as the case presented in this study. Open surgery is usually used to treat this condition; however, in our case it was excised arthroscopically despite the anatomical challenges of the posterior knee compartment. Case Presentation This case presents a female patient with a complaint of posterior-region pain of her left knee post direct trauma post fall. This was directly followed by knee joint blockage for 1-week duration before presentation to the hospital. On MRI, she was found to have a multiloculated hemosiderin-containing structure of synovial origin within the femoral notch, extending beyond the joint capsule displacing the popliteal vessels. The patient underwent arthroscopic resection of the lesion, which was found to be pigmented villonodular synovitis on anatomopathological examination. On 6-month follow-up, the patient showed good clinical evolution with the absence of symptoms and back-to-normal daily activities. Conclusion This is a rare case of PVNS affecting the posterior knee joint compartment of a middle-aged woman, which was successfully excised arthroscopically, with no residual affected tissue or recurrence on 6-month follow-up.
Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function

The heterogeneity of stroke prompts the need for predictors of individual treatment response to rehabilitation therapies. We previously studied healthy subjects with EEG and identified a frontoparietal circuit in which activity predicted training-related gains in visuomotor tracking. Here we asked whether activity in this same frontoparietal circuit also predicts training-related gains in visuomotor tracking in patients with chronic hemiparetic stroke. Subjects (n = 12) underwent dense-array EEG recording at rest, then received 8 sessions of visuomotor tracking training delivered via home-based telehealth methods. Subjects showed significant training-related gains in the primary behavioral endpoint, Success Rate score on a standardized test of visuomotor tracking, increasing an average of 24.2 ± 21.9% (p = 0.003). Activity in the circuit of interest, measured as coherence (20–30 Hz) between leads overlying ipsilesional frontal (motor cortex) and parietal lobe, significantly predicted training-related gains in visuomotor tracking change, measured as change in Success Rate score (r = 0.61, p = 0.037), supporting the main study hypothesis. Results were specific to the hypothesized ipsilesional motor-parietal circuit, as coherence within other circuits did not predict training-related gains. Analyses were repeated after removing the four subjects with injury to motor or parietal areas; this increased the strength of the association between activity in the circuit of interest and training-related gains. The current study found that (1) Eight sessions of training can significantly improve performance on a visuomotor task in patients with chronic stroke, (2) this improvement can be realized using home-based telehealth methods, (3) an EEG-based measure of frontoparietal circuit function predicts training-related behavioral gains arising from that circuit, as hypothesized and with specificity, and (4) incorporating measures of both neural function and neural injury improves prediction of stroke rehabilitation therapy effects.
AMPK blunts chronic heart failure by inhibiting autophagy

AMP-activated protein kinase (AMPK), a serine/threonine protein kinase, has been shown to exert a protective effect against cardiac hypertrophy and heart failure. Our previous reports have demonstrated that AMPK can inhibit cardiac hypertrophy and block the development of heart failure by promoting autophagy. However, other investigators have demonstrated that overactive and dysregulated autophagy may also contribute to the onset and exacerbation of heart failure. Thus, a major goal of the present investigation is to explore how AMPK regulates autophagy in heart failure. First, heart failure was induced in mice by 4 weeks of pressure overload; AMPK activation was subsequently induced by injecting 5-aminoimidazole-4-carboxamide 1-β-d-ribonucleotide (AICAR) after the establishment of chronic heart failure. We showed that AMPK activation significantly attenuated the progression of heart failure and improved cardiac function, which was accompanied by decreased autophagy levels in the failing hearts. Additionally, we demonstrated that the treatment with AICAR inhibited phosphorylation of the mammalian target of rapamycin (mTOR) complex 1 (mTORC1) downstream effectors 4E-binding protein1 (4EBP1), and ribosomal protein S6 kinase (p70S6K). A major action of AICAR was significantly to activate AKT (Ser473), the downstream substrate of mTOR complex 2 (mTORC2). In conclusion, the data suggest that AMPK improved cardiac function during the development of chronic heart failure by attenuating autophagy, potentially via mTORC2 activation and the downstream effects.
From intuitive altruism to rational deliberations - a neuroscience view on the learning process in game theory experiments

What is intuitive: pro-social or anti-social behaviour? To answer this fundamental question, recent studies analyse decision times in game theory experiments under the assumption that intuitive decisions are fast and that deliberation is slow. Lacking any knowledge of the underlying dynamics, such simplistic approach might however lead to erroneous interpretations. Here we model the cognitive basis of strategic cooperative decision making using the Drift Diffusion Model to discern between deliberation and intuition and describe the evolution of the decision making in iterated Prisoner's Dilemma experiments. We find that rational deliberation quickly becomes dominant over an initial intuitive bias towards cooperation, which is fostered by positive interactions as much as frustrated by a negative one. However, this initial pro-social tendency is resilient, as after a pause it resets to the same initial value. These results illustrate the new insight that can be achieved thanks to a quantitative modelling of human behavior.
Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.
Eyeriss v2: A Flexible and High-Performance Accelerator for Emerging Deep Neural Networks

The design of DNNs has increasingly focused on reducing the computational complexity in addition to improving accuracy. While emerging DNNs tend to have fewer weights and operations, they also reduce the amount of data reuse with more widely varying layer shapes and sizes. This leads to a diverse set of DNNs, ranging from large ones with high reuse (e.g., AlexNet) to compact ones with high bandwidth requirements (e.g., MobileNet). However, many existing DNN processors depend on certain DNN properties, e.g., a large number of channels, to achieve high performance and energy efficiency and do not have sufficient flexibility to efficiently process a diverse set of DNNs. In this work, we present Eyexam, a performance analysis framework that quantitatively identifies the sources of performance loss in DNN processors. It highlights two architectural bottlenecks in many existing designs. First, their dataflows are not flexible enough to adapt to the varying layer shapes and sizes of different DNNs. Second, their network-on-chip (NoC) can't adapt to support both high data reuse and high bandwidth scenarios. Based on this analysis, we present Eyeriss v2, a high-performance DNN accelerator that adapts to a wide range of DNNs. Eyeriss v2 has a new dataflow, called Row-Stationary Plus (RS+), that enables the spatial tiling of data from all dimensions to fully utilize the parallelism for high performance. To support RS+, it has a low-cost and scalable NoC design, called hierarchical mesh, that connects the high-bandwidth global buffer to the array of processing elements (PEs) in a two-level hierarchy. This enables high-bandwidth data delivery while still being able to harness any available data reuse. Compared with Eyeriss, Eyeriss v2 has a performance increase of 10.4x-17.9x for 256 PEs, 37.7x-71.5x for 1024 PEs, and 448.8x-1086.7x for 16384 PEs on DNNs with widely varying amounts of data reuse.
Robust Monitoring of Linear Temporal Properties

Runtime verification is commonly used to detect and, if possible, react to the violation of desired properties in safety critical systems. Also common is the use of temporal logics to specify the desired properties. However, if properties are expressed in two-valued logics, such as Linear-time Temporal Logic (LTL), monitoring them often yields insufficient results: when the property is satisfied, there is nothing we need to do; once it is violated, there may be nothing we can do. To overcome this problem, logics such as robust LTL (rLTL) allow for detecting the degree of a violation: in this way, violations may be mitigated. In this paper we solve the rLTL monitoring problem by constructing a deterministic monitoring automaton for each possible degree of violation of the specification. The automaton indicates whether: 1) the property is already satisfied with respect to that degree and thus needs no further attention, 2) is still undetermined, or 3) is already violated. Violation of a property by a small degree can then be used to trigger appropriate safety measures preventing the violation of the same property to a higher degree. As the size of the monitoring automata is doubly-exponential in the size of the rLTL formula, robustly monitoring systems comes without penalty in terms of space or time when compared to classical LTL monitoring.
Towards Neural Theorem Proving at Scale

Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rockt{\"{a}}schel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.
Inference of Crosstalk Effects between DNA Methylation and lncRNA Regulation in NSCLC

Intercellular crosstalk effects between DNA methylation and lncRNA regulation remain elusive in lung carcinoma epigenetics. We present an application toolkit MetLnc in integration and annotation for group-wise NSCLC tissue-based DNA methylation and lncRNA profiling resources, to comprehensively analyze differentially methylated loci and lncRNAs through genome-wide analysis. Together with multiple analytic functions, MetLnc acts as an efficient approach on epigenetic omics integration and interrogation. Via the benchmark with group-wise NSCLC tissue profiling and TCGA cohort resources, we study differentially methylated CpG loci and lncRNAs as meaningful clues for inferring crosstalk effects between DNA methylation and lncRNA regulation; together we conclude with investigated biomarkers for further epigenetics and clinical trial research.
Constructive Galois Connections

Abstract Galois connections are a foundational tool for structuring abstraction in semantics, and their use lies at the heart of the theory of abstract interpretation. Yet, mechanization of Galois connections using proof assistants remains limited to restricted modes of use, preventing their general application in mechanized metatheory and certified programming. This paper presents constructive Galois connections, a variant of Galois connections that is effective both on paper and in proof assistants; is complete with respect to a large subset of classical Galois connections; and enables more general reasoning principles, including the “calculational” style advocated by Cousot. To design constructive Galois connections, we identify a restricted mode of use of classical ones which is both general and amenable to mechanization in dependently typed functional programming languages. Crucial to our metatheory is the addition of monadic structure to Galois connections to control a “specification effect. ” Effectful calculations may reason classically, while pure calculations have extractable computational content. Explicitly moving between the worlds of specification and implementation is enabled by our metatheory. To validate our approach, we provide two case studies in mechanizing existing proofs from the literature: the first uses calculational abstract interpretation to design a static analyzer, and the second forms a semantic basis for gradual typing. Both mechanized proofs closely follow their original paper-and-pencil counterparts, employ reasoning principles not captured by previous mechanization approaches, support the extraction of verified algorithms, and are novel.
Asthma and Asthma Medication Are Common among Recreational Athletes Participating in Endurance Sport Competitions

Background Asthma prevalence is high among elite endurance athletes, but little is known about its prevalence among competitive recreational athletes. The aim of this study was to determine the prevalence of self-reported asthma and asthma medication use among competitive recreational endurance athletes and their association with training. Methods A web survey on asthma and medication was conducted among 38,603 adult participants of three Swedish endurance competitions (cross-country running, cross-country skiing, and swimming). Results The overall response rate was 29%. The prevalence of self-reported asthma (physician-diagnosed asthma and use of asthma medication in the last 12 months) was 12%. Among those reporting asthma, 23% used inhaled corticosteroids and long-acting beta-agonists daily. We found no association between training volume and daily use of asthma medication, except a trend in relation to short-acting beta-agonists. Independent predictors of self-reported asthma were female sex, allergic rhinitis, previous eczema, family history of asthma, cycling, and training for >5 h 50 min/week. Conclusions The prevalence of self-reported asthma among Swedish competitive recreational endurance athletes appears to be higher than that in the general Swedish population. A large proportion of recreational athletes were reported with asthma use medications, indicating an association between high physical activity and self-reported asthma among competitive recreational athletes.
Impact of human disturbance on bee pollinator communities in savanna and agricultural sites in Burkina Faso, West Africa

Abstract All over the world, pollinators are threatened by land‐use change involving degradation of seminatural habitats or conversion into agricultural land. Such disturbance often leads to lowered pollinator abundance and/or diversity, which might reduce crop yield in adjacent agricultural areas. For West Africa, changes in bee communities across disturbance gradients from savanna to agricultural land are mainly unknown. In this study, we monitored for the impact of human disturbance on bee communities in savanna and crop fields. We chose three savanna areas of varying disturbance intensity (low, medium, and high) in the South Sudanian zone of Burkina Faso, based on land‐use/land cover data via Landsat images, and selected nearby cotton and sesame fields. During 21 months covering two rainy and two dry seasons in 2014 and 2015, we captured bees using pan traps. Spatial and temporal patterns of bee species abundance, richness, evenness and community structure were assessed. In total, 35,469 bee specimens were caught on 12 savanna sites and 22 fields, comprising 97 species of 32 genera. Bee abundance was highest at intermediate disturbance in the rainy season. Species richness and evenness did not differ significantly. Bee communities at medium and highly disturbed savanna sites comprised only subsets of those at low disturbed sites. An across‐habitat spillover of bees (mostly abundant social bee species) from savanna into crop fields was observed during the rainy season when crops are mass‐flowering, whereas most savanna plants are not in bloom. Despite disturbance intensification, our findings suggest that wild bee communities can persist in anthropogenic landscapes and that some species even benefitted disproportionally. West African areas of crop production such as for cotton and sesame may serve as important food resources for bee species in times when resources in the savanna are scarce and receive at the same time considerable pollination service.