Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN-based Multiple Manipulation Detector Using Frequency Domain Features of Image Residuals

Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of a potent detector requires knowledge of the type of manipulation, something that cannot be known (a priori). Thus, the latest effort is directed towards developing model-free (i.e., generalized) detectors capable of detecting multiple manipulation types. In this article, we propose a novel detector capable of exposing seven different manipulation types in low-resolution compressed images. Our proposed approach is based on a two-layer convolutional neural network (CNN) to extract frequency domain features of image median filtered residual that are classified using two different classifiers—softmax and extremely randomized trees. Extensive experiments demonstrate the efficacy of proposed detector over existing state-of-the-art detectors.
Descripción de un brote de enfisema y edema pulmonar agudo del bovino (EPAB) en Parral (Chile)

Se describe un brote de enfisema y edema alveolar agudo del bovino (EPAB) ocurrido en la zona centro-sur a fines del verano de 1995. Los animales Frison negro, de aproximadamente 2 anos de edad, fueron afectados despues de cambiarse de una pradera fibrosa a una suculenta de alfalfa. La mortalidad por esta causa fue de un 23%. Las principales lesiones encontradas en la necropsia fueron enfisema y edema intersticial intenso del pulmon, lo cual fue corroborado histopatologicamente, encontrandose a nivel pulmonar una hiperplasia de neumocitos tipo II y membranas hialinas. Se practico tratamiento sintomatico al inicio del cuadro, el cual no fue efectivo
Crab-tree

In recent years, the next-generation non-volatile memory (NVM) technologies have emerged with DRAM-like byte addressability and disk-like durability. Computer architects have proposed to use them to build persistent memory that blurs the conventional boundary between volatile memory and non-volatile storage. However, ARM processors, ones that are widely used in embedded computing systems, start providing architectural supports to utilize NVM since ARMv8. In this article, we consider tailoring B+-tree for NVM operated by a 64-bit ARMv8 processor. We first conduct an empirical study of performance overhead in writing and reading data for a B+-tree with an ARMv8 processor, including the time cost of cache line flushes and memory fences for crash consistency as well as the execution time of binary search compared to that of linear search. We hence identify the key weaknesses in the design of B+-tree with ARMv8 architecture. Accordingly, we develop a new B+-tree variant, namely, crash recoverable ARMv8-oriented B+-tree (Crab-tree). To insert and delete data at runtime, Crab-tree selectively chooses one of two strategies, i.e., copy on write and shifting in place, depending on which one causes less consistency cost. Crab-tree regulates a strict execution order in both strategies and recovers the tree structure in case of crashes. To further improve the performance of Crab-tree, we employ three methods to reduce software overhead, cache misses, and consistency cost, respectively. We have implemented and evaluated Crab-tree in Raspberry Pi 3 Model B+ with emulated NVM. Experiments show that Crab-tree significantly outperforms state-of-the-art B+-trees designed for persistent memory by up to 2.2× and 3.7× in write and read performances, respectively, with both consistency and scalability achieved.
Combination of traits at two developmental stages under salt stress as a measure of tolerance in a reciprocally crossed rice (Oryza sativa) population

Abstract. The rice (Oryza sativa L.) landrace Horkuch from Bangladesh maintains efficient photosynthesis and detoxification under salt stress and was therefore considered to be a useful donor for tolerance traits. Reciprocally crossed bi-parental mapping populations were generated from salt-tolerant Horkuch and high-yielding salt-sensitive variety IR29, in order to identify superior salt-tolerant high-yielding lines as donors. The present study reports on the phenotypic screening data of ∼300 F3 segregating populations from the reciprocal cross and their parental lines in seedlings and screening of a subset at maturity stage under gradual salt stress of 12 dS m–1 for seedlings and 8 dS m–1 for mature plants. Correlation, broad-sense heritability and principal component analyses for salt tolerance as well as yield-related traits were conducted in the populations at the two developmental stages. Level of salt injury was found to be correlated with traits such as filled grain weight at maturity stage and biomass-related traits at the seedling stage. This association between yield-related and survival traits helped to identify tolerant and sensitive plants, which were predicative of agronomic performance under salt stress. Moreover, use of the reciprocal-cross population showed how cytoplasmic inheritance of specific traits such as K+ concentrations can affect characteristics of donor plants. Measurement of a large number of traits and analysis of their co-inherited interrelation can therefore help identify the best performing plants under salt stress for effective breeding strategies. The data are being utilised in mapping of quantitative trait loci, and selected progenies are being used as breeding lines for producing durable salt-tolerant, high-yielding rice varieties.
Anatomy of Metadata for Data Curation

Real-world datasets often suffer from various data quality problems. Several data cleaning solutions have been proposed so far. However, data cleaning remains a manual and iterative task that requires domain and technical expertise. Exploiting metadata promises to improve the tedious process of data preparation, because data errors are detectable through metadata. This article investigates the intrinsic connection between metadata and data errors. In this work, we establish a mapping that reflects the connection between data quality issues and extractable metadata using qualitative and quantitative techniques. Additionally, we present a taxonomy based on a closed grammar that covers all existing metadata and allows the composition of novel types of metadata. We provide a case-study to show the practical application of the grammar for generating new metadata for data quality assessment.
Morpho-physiological adaptation and DNA methylation of wheat seedlings under osmotic stress

Abstract. The quality and yield of wheat (Triticum aestivum L.) are dramatically affected by drought. We used morphological and physiological characteristics and degree of DNA methylation to compare the responses of two wheat cultivars under osmotic stress, and found that the two cultivars behaved differently. Root development, leaf growth, and the accumulation of proline and soluble carbohydrate in wheat cv. AK58 all showed drought tolerance. Drought tolerance of wheat cv. XM13 was mainly improved by accumulation of proline and soluble carbohydrate. The degree of DNA methylation in wheat showed tissue specificity and increased significantly in leaf tissue with increasing osmotic stress, but decreased significantly in root tissue under mild osmotic stress. In addition, changes of DNA methylation differed between two wheat cultivars under osmotic stress, and this change was especially significant in AK58. Therefore, wheat AK58 may have stronger self-adjustment ability under osmotic stress compared with XM13, and might respond more rapidly to osmotic stress through the change of DNA methylation. This finding could be significant for revealing drought-tolerance mechanisms of plants.
The Robot Makers

This article is an ethnographic exploration of robot anthropomorphism at a robotics company. It draws on a 10-month participatory ethnography among a robotics company, an anthropologist, and a social robotics research lab. In contrast to psychological methods, this anthropological participatory ethnography integrates all stakeholders’ insights, offering holistic understandings of robots’ in situ operations throughout the fieldwork, data-sharing, interviews, and analysis. In particular, this article unravels employee social constructions of the company’s self-driving factory transport vehicles, “the robots. ” These robots are deployed across a variety of warehouses and factories in North America. Our results involve an assessment of six teams at the robotics company’s headquarters: those testing robots, those developing their hardware and software, and those working with customers. We unpack trends of anthropomorphism for each of these teams and across the company.
Binary Tree Classification of Rigid Error Detection and Correction Techniques

Due to technology scaling and harsh environments, a wide range of fault-tolerant techniques exists to deal with the error occurrences. Selecting a fault-tolerant technique is not trivial, whereas more than the necessary overhead is usually inserted during the system design. To avoid over-designing, it is necessary to have an in-depth understanding of the available design options. However, an exhaustive listing is neither possible to create nor efficient to use due to its prohibitive size. In this work, we present a top-down binary tree classification for error detection and correction techniques. At each split, the design space is clearly divided into two complementary parts using one single attribute, compared with existing classifications that use splits with multiple attributes. A leaf inherits all the attributes of its ancestors from the root to the leaf. A technique is decomposed into primitive components, each one belonging to a different leaf. The single attribute splits can be used to efficiently compare the techniques and to prune the incompatible parts of the design space during the design of a technique. This essential single attribute division of the design space is required for the improvement of the techniques and for novel contributions to the fault-tolerance domain.
The Phosphatase CSW Controls Life Span by Insulin Signaling and Metabolism Throughout Adult Life in Drosophila

Noonan syndrome and related disorders are caused by mutations in genes encoding for proteins of the RAS-ERK1/2 signaling pathway, which affect development by enhanced ERK1/2 activity. However, the mutations’ effects throughout adult life are unclear. In this study, we identify that the protein most commonly affected in Noonan syndrome, the phosphatase SHP2, known in Drosophila as corkscrew (CSW), controls life span, triglyceride levels, and metabolism without affecting ERK signaling pathway. We found that CSW loss-of-function mutations extended life span by interacting with components of the insulin signaling pathway and impairing AKT activity in adult flies. By expressing csw-RNAi in different organs, we determined that CSW extended life span by acting in organs that regulate energy availability, including gut, fat body and neurons. In contrast to that in control animals, loss of CSW leads to reduced homeostasis in metabolic rate during activity. Clinically relevant gain-of-function csw allele reduced life span, when expressed in fat body, but not in other tissues. However, overexpression of a wild-type allele did not affect life span, showing a specific effect of the gain-of-function allele independently of a gene dosage effect. We concluded that CSW normally regulates life span and that mutations in SHP2 are expected to have critical effects throughout life by insulin-dependent mechanisms in addition to the well-known RAS-ERK1/2-dependent developmental alterations.
Cytosolic Ascorbate Peroxidases Plays a Critical Role in Photosynthesis by Modulating Reactive Oxygen Species Level in Stomatal Guard Cell

Photosynthetic rate is one of the key factors limiting yield of cotton. Reactive oxygen species (ROS) generated by abiotic stress imposes numerous detrimental effects and causes tremendous loss of yield. It is worth to study whether ROS scavenging enzymes could affect yield through regulating photosynthetic rate in cotton. In this study, we created transgenic cotton with changes of endogenous ROS by overexpressing or suppressing the expression of cytosolic ascorbate peroxidases (APXs), which are hydrogen peroxide (H2O2) scavenging enzymes in plants. The suppression of cytosolic APXs by RNAi brings about a great influence on plant growth and development. Plant height and leaf size declined, and yield-related traits including single boll weight, seed weight, seed size, and lint weight dropped significantly, in IAO lines (cytosolic APX-suppressed lines). The stunted plant growth was due to the decrease of plant photosynthetic rate. The evidences showed that increased ROS level in guard cells inhibited stomatal opening and suppressed the absorption of CO2 and H2O in IAO line. The decrease of water content and the increase of water loss rate in leaf exacerbated the decline of photosynthetic rate in cytosolic APX-suppressed lines. Based on these results, it implies that cytosolic APXs as a whole play an important role in maintaining REDOX balance to regulate photosynthetic rate and yield in cotton.
Towards a Reliable Ground Truth for Drowsiness: A Complexity Analysis on the Example of Driver Fatigue

The increasing number and complexity of advanced driver assistance systems (ADAS) pave the way for fully automated driving. Automated vehicles are said to increase road safety and prevent human-made (fatal) accidents, amongst others. In the lower levels of automation, however, the driver is still responsible as a fallback authority. As a consequence, systems that reliably monitor the driver's state, especially the risk factor drowsiness, become increasingly essential to ensure the driver's ability to take over control from the vehicle on time. In research, the use of supervised machine learning for drowsiness detection is the prevalent method. As the ground truth for drowsiness is both application- and user-dependent, and no golden standard exists for its definition, measures are usually applied in the form of observer ratings. Also, in this work, observer ratings were investigated with regard to the required level of detail/complexity. To this end, video data, recorded within a simulator study (N = 30) comprised of each 45-minute manual and automated driving sessions, were evaluated by trained raters. Correlation analysis results show that - depending on the number of drowsiness levels - a comparable ground truth can be generated by reducing the rating frequency and thus the rating complexity by a factor of five. The knowledge gained can be used in future studies in this research area, the collection of a reliable and valid ground truth of drowsiness, as well as for improving the process in developing interactive drowsiness detection systems.
Plasticity of Persistent Activity and Its Constraints

Stimulus information is maintained in working memory by action potentials that persist after the stimulus is no longer physically present. The prefrontal cortex is a critical brain area that maintains such persistent activity due to an intrinsic network with unique synaptic connectivity, NMDA receptors, and interneuron types. Persistent activity can be highly plastic depending on task demands but it also appears in naïve subjects, not trained or required to perform a task at all. Here, we review what aspects of persistent activity remain constant and what factors can modify it, focusing primarily on neurophysiological results from non-human primate studies. Changes in persistent activity are constrained by anatomical location, with more ventral and more anterior prefrontal areas exhibiting the greatest capacity for plasticity, as opposed to posterior and dorsal areas, which change relatively little with training. Learning to perform a cognitive task for the first time, further practicing the task, and switching between learned tasks can modify persistent activity. The ability of the prefrontal cortex to generate persistent activity also depends on age, with changes noted between adolescence, adulthood, and old age. Mean firing rates, variability and correlation of persistent discharges, but also time-varying firing rate dynamics are altered by these factors. Plastic changes in the strength of intrinsic network connections can be revealed by the analysis of synchronous spiking between neurons. These results are essential for understanding how the prefrontal cortex mediates working memory and intelligent behavior.
Targeting Mouse Double Minute 2: Current Concepts in DNA Damage Repair and Therapeutic Approaches in Cancer

Defects in DNA damage repair may cause genome instability and cancer development. The tumor suppressor gene p53 regulates cell cycle arrest to allow time for DNA repair. The oncoprotein mouse double minute 2 (MDM2) promotes cell survival, proliferation, invasion, and therapeutic resistance in many types of cancer. The major role of MDM2 is to inhibit p53 activity and promote its degradation. In this review, we describe the influence of MDM2 on genomic instability, the role of MDM2 on releasing p53 and binding DNA repair proteins to inhibit repair, and the regulation network of MDM2 including its transcriptional modifications, protein stability, and localization following DNA damage in genome integrity maintenance and in MDM2-p53 axis control. We also discuss p53-dependent and p53 independent oncogenic function of MDM2 and the outcomes of clinical trials that have been used with clinical inhibitors targeting p53-MDM2 to treat certain cancers.
iSENSE2.0

Software engineers get questions of “how much testing is enough” on a regular basis. Existing approaches in software testing management employ experience-, risk-, or value-based analysis to prioritize and manage testing processes. However, very few is applicable to the emerging crowdtesting paradigm to cope with extremely limited information and control over unknown, online crowdworkers. In practice, deciding when to close a crowdtesting task is largely done by experience-based guesswork and frequently results in ineffective crowdtesting. More specifically, it is found that an average of 32% testing cost was wasteful spending in current crowdtesting practice. This article intends to address this challenge by introducing automated decision support for monitoring and determining appropriate time to close crowdtesting tasks. To that end, it first investigates the necessity and feasibility of close prediction of crowdtesting tasks based on an industrial dataset. Next, it proposes a close prediction approach named iSENSE2.0, which applies incremental sampling technique to process crowdtesting reports arriving in chronological order and organizes them into fixed-sized groups as dynamic inputs. Then, a duplicate tagger analyzes the duplicate status of received crowd reports, and a CRC-based (Capture-ReCapture) close estimator generates the close decision based on the dynamic bug arrival status. In addition, a coverage-based sanity checker is designed to reinforce the stability and performance of close prediction. Finally, the evaluation of iSENSE2.0 is conducted on 56,920 reports of 306 crowdtesting tasks from one of the largest crowdtesting platforms. The results show that a median of 100% bugs can be detected with 30% saved cost. The performance of iSENSE2.0 does not demonstrate significant difference with the state-of-the-art approach iSENSE, while the later one relies on the duplicate tag, which is generally considered as time-consuming and tedious to obtain.
SeqST-GAN

Citywide crowd flow data are ubiquitous nowadays, and forecasting the flow of crowds is of great importance to many real applications such as traffic management and mobility-on-demand (MOD) services. The challenges of accurately predicting urban crowd flows come from both the nonlinear spatial-temporal correlations of the crowd flow data and the complex impact of the external context factors, such as weather, holidays, and POIs. It is even more challenging for most existing one-step prediction models to make an accurate prediction across multiple future time slots. In this article, we propose a sequence-to-sequence (Seq2Seq) Generative Adversarial Nets model named SeqST-GAN to perform multi-step Spatial-Temporal crowd flow prediction of a city. Motivated by the success of GAN in video prediction, we for the first time propose an adversarial learning framework by regarding the citywide crowd flow data in successive time slots as “image frames. ” Specifically, we first use a Seq2Seq model to generate a sequence of future “frame” predictions based on previous ones. Then, by integrating the generation error with the adversary loss, SeqST-GAN can avoid the blurry prediction issue and make more accurate predictions. To incorporate the external contexts, an external-context gate module called EC-Gate is also proposed to learn region-level representations of the context features. Experiments on two large crowd flow datasets in New York demonstrate that SeqST-GAN improves the prediction performance by a large margin compared with the existing state-of-the-art.
Age-Related Changes in Pain Perception Are Associated With Altered Functional Connectivity During Resting State

Aging affects pain experience and brain functioning. However, how aging leads to changes in pain perception and brain functional connectivity has not yet been completely understood. To investigate resting-state and pain perception changes in old and young participants, this study employed region of interest (ROI) to ROI resting-state functional connectivity (rsFC) analysis of imaging data by using regions implicated in sensory and affective dimensions of pain, descending pain modulation, and the default-mode networks (DMNs). Thirty-seven older (66.86 ± 4.04 years; 16 males) and 38 younger healthy participants (20.74 ± 4.15 years; 19 males) underwent 10 min’ eyes-closed resting-state scanning. We examined the relationship between rsFC parameters with pressure pain thresholds. Older participants showed higher pain thresholds than younger. Regarding rsFC, older adults displayed increased connectivity of pain-related sensory brain regions in comparison to younger participants: increased rsFC between bilateral primary somatosensory area (SI) and anterior cingulate cortex (ACC), and between SI(L) and secondary somatosensory area (SII)-(R) and dorsolateral prefrontal cortex (PFC). Moreover, decreased connectivity in the older compared to the younger group was found among descending pain modulatory regions: between the amygdala(R) and bilateral insula(R), thalamus(R), ACC, and amygdala(L); between the amygdala(L) and insula(R) and bilateral thalamus; between ACC and bilateral insula, and between periaqueductal gray (PAG) and bilateral thalamus. Regarding the DMN, the posterior parietal cortex and lateral parietal (LP; R) were more strongly connected in the older group than in the younger group. Correlational analyses also showed that SI(L)-SII(R) rsFC was positively associated with pressure pain thresholds in older participants. In conclusion, these findings suggest a compensatory mechanism for the sensory changes that typically accompanies aging. Furthermore, older participants showed reduced functional connectivity between key nodes of the descending pain inhibitory pathway.