Miracle-dz/UniFAR-FTUs huggingface dataset:
Unified Route Planning for Shared Mobility: An Insertion-based Framework

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, previous route planning solutions fail to exploit the appearance patterns of future requests hidden in historical data for optimization. In this paper, we present a unified formulation of route planning called URPSM. It has a well-defined parameterized objective function which eliminates the contradicted objectives in previous studies and enables flexible multi-objective route planning for shared mobility. We propose two insertion-based frameworks to solve the URPSM problem. The first is built upon the plain-insertion widely used in prior studies, which processes online requests only, whereas the second relies on a new insertion operator called prophet-insertion that handles both online and predicted requests. Novel dynamic programming algorithms are designed to accelerate both insertions to only linear time. Theoretical analysis shows that no online algorithm can have a constant competitive ratio for the URPSM problem under the competitive analysis model, yet our prophet-insertion-based framework can achieve a constant optimality ratio under the instance-optimality model. Extensive experimental results on real datasets show that our insertion-based solutions outperform the state-of-the-art algorithms in both effectiveness and efficiency by a large margin (e.g., up to 30 \( \times \) more effective in the objective and up to 20 \( \times \) faster).
Depth and Thermal Images in Face Detection - A Detailed Comparison Between Image Modalities

Face detection is a well-known issue in image processing, and numerous studies are present in this field. A prominent part of the work is devoted to RGB images, leaving depth and thermal data with less interest. However, in some conditions like low-light areas where face detection is needed, non-RGB sensors might perform better. Also, mounting an additional RGB camera could be challenging or not possible, considering privacy concerns. In this work, current deep learning methodologies are employed to train depth and thermal detection models. The training is done using combined publicly available data that is processed by us for this purpose in order to create necessary annotations for a learning process. The resulting models are validated on a new trimodal dataset collected for this experiments purpose. It contains images captured with RGB, depth, and thermal sensors. Various scenes with single and multiple faces appearances can be found. The results show that non-RGB solutions can be applied in practice with highly robust accuracy and their efficiency is close to RGB detectors. However, their performance depends on the environment and that circumstances are described later in this article.
Program Vulnerability Mining System based on Symbolic Execution

In recent years, vulnerability mining is a hot topic. Existing vulnerability mining methods of binary program are mainly based on fuzzy testing. These methods discover vulnerabilities by continuously inputting random data into the target program, in order to trigger the binary program's errors. However, the vulnerability output of these methods mainly depends on the input random data. The coverage rate of program execution path is low in these methods. In order to improve the coverage rate of program execution path and discover more unknown vulnerabilities, this paper presents an automatic vulnerability mining system based on symbolic execution. This system discovers vulnerabilities by performing constraint check on each execution path of the binary program. This system contains five modules, and each module is responsible for mining a type of vulnerabilities. This system has been developed based on the Python's Angr module. An experiment based on test programs is conducted to evaluate this system. The experimental results validate the effectiveness of this system. The experiment results also show that this system can discover most of the binary program's execution paths.
Energy Management and Power Control of a Hybrid Active Wind Generator for Distributed Power Generation and Grid Integration

Classical wind energy conversion systems are usually passive generators. The generated power does not depend on the grid requirement but entirely on the fluctuant wind condition. A dc-coupled wind/hydrogen/super capacitor hybrid power system is studied in this paper. The purpose of the control system is to coordinate these different sources, particularly their power exchange, in order to make controllable the generated power. As a result, an active wind generator can be built to provide some ancillary services to the grid. The control system should be adapted to integrate the power management strategies. Two power management strategies are presented and compared experimentally. We found that the “source-following” strategy has better performances on the grid power regulation than the “grid-following” strategy.
Stock Volatility Forecast Base on Comparative Learning and Autoencoder Framework

Volatility is an important indicator of derivatives pricing, financial risk measurement, and market panic sentiment measurement. A reasonable prediction of volatility is of great significance to market participants and regulators. This article proposes a new volatility forecast model. We use comparative learning and autoencoders to improve the accuracy and robustness of the model. Reduce the instability of financial data due to noise. And this article expands traditional machine learning research methods. The traditional model is compared with other deep learning models. Our model has made very competitive progress in accuracy and loss compared to other models.
TSDroid: A Novel Android Malware Detection Framework Based on Temporal & Spatial Metrics in IoMT

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.
An Overview of Machine Learning Methods used in Fatigue Driving Detection

Fatigue driving has always been an important factor causing traffic accidents in China and the world. Fatigue driving identification has a good prospect in engineering application and behavior analysis and other fields. Based on the collected literature, this paper first discusses the concept, significance, and key points in the study of fatigue driving. Then, the method of fatigue driving detection based on deep learning and model building is discussed. After introducing the data set, the results of these methods are analyzed and compared, and the possibility of their application is discussed. Finally, this paper introduces the prospect and summary of this research.
Volitional generation of reproducible energy-efficient temporal patterns

One of the extraordinary characteristics of the biological brain is its low energy expense to implement a variety of biological functions and intelligence compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as the contributor for the brain to run with a low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how the brain learns to generate it have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm, by learning which two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1). Moreover, most neurons that were not directly assigned for controlling the BMI did not boost their excitability, demonstrating an overall energy-efficiency manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting a cohesive rather than dissociable processing underlie the refinement of energy-efficient temporal patterns.
Plasma membrane topography governs the three-dimensional dynamic localization of IgM B cell receptor clusters

B lymphocytes recognize bacterial or viral antigens via different classes of the B cell antigen receptor (BCR). Protrusive structures termed microvilli cover lymphocyte surfaces and are thought to perform sensory functions in screening antigen-bearing surfaces. Here, we have studied the cell surface features of Ramos B cells and the spatiotemporal organization of the IgM-BCR using lattice light sheet microscopy in combination with tailored custom-built 4D image analysis. Ramos B cell surfaces were found to form dynamic networks of elevated ridges bridging individual microvilli. A proportion of membrane-localized IgM-BCR was found in clusters, which were associated with the ridges and the microvilli. The dynamic ridge network organization and the IgM-BCR cluster mobility were linked and both were controlled by Arp2/3 complex activity. Our results suggest that topographical features of the cell surface govern the distribution and dynamic localization of IgM-BCR clusters to facilitate antigen screening.
Evolution and antiviral activity of a human protein of retroviral origin

Endogenous retroviruses are abundant components of mammalian genomes descended from ancient germline infections. In several mammals, the envelope proteins encoded by these elements protect against exogenous viruses, but this activity has not been documented in human. We report that our genome harbors a large pool of envelope-derived sequences with the potential to restrict retroviral infection. To further test this, we characterize in detail the envelope-derived protein, Suppressyn. We found that Suppressyn is expressed in preimplantation embryos and developing placenta using its ancestral retroviral promoter. Restriction assays in cell culture show that Suppressyn, and its hominoid orthologs, can restrict infection by extant mammalian type D retroviruses. Our data support a generalizable model of retroviral envelope cooption for host immunity and genome defense. Summary We found that the human genome expresses a vast pool of envelope sequences of retroviral origin and provide proof of principle that such proteins can restrict zoonotic viruses.
A magnetic pulse does not affect free-flight navigation behaviour of a medium-distance songbird migrant in spring

Current evidence suggests that migratory animals extract map information from the geomagnetic field for true navigation. The sensory basis underlying this feat is elusive, but presumably involves magnetic particles. A common experimental manipulation procedure consists of pre-treating animals with a magnetic pulse. This aims at re-magnetising particles to alter the internal representation of the external field prior to a navigation task. While pulsing provoked deflected bearings in laboratory experiments, analogous studies with free-flying songbirds yielded inconsistent results. Here, we pulsed European robins (Erithacus rubecula), being medium-distance migrants, at an offshore stopover site during spring migration and monitored their free-flight behaviour with a regional-scale tracking system. We found no pulse effect on departure probability, nocturnal departure timing, or departure direction, in agreement with results on a long-distance migrant released at the same site in autumn. This necessitates a reassessment of the importance of geomagnetic maps for migratory decisions for free-flying birds. Summary statement Magnetic pulse pre-treatment disturbs geomagnetic map usage of birds in lab environments. However, our free-flying birds show no effect, suggesting geomagnetic map information is less important in the natural environment.
Off-Target Integron Activity Leads to Rapid Plasmid Compensatory Evolution in Response to Antibiotic Selection Pressure

Tackling the public health challenge created by antibiotic resistance requires understanding the mechanisms driving its evolution. Mobile integrons are widespread genetic platforms heavily involved in the spread of antibiotic resistance. ABSTRACT Integrons are mobile genetic elements that have played an important role in the dissemination of antibiotic resistance. Under stress, the integron can generate combinatorial variation in resistance cassette expression by cassette reshuffling, accelerating the evolution of resistance. However, the flexibility of the integron integrase site recognition motif hints at potential off-target effects of the integrase on the rest of the genome that may have important evolutionary consequences. Here, we test this hypothesis by selecting for increased-piperacillin-resistance populations of Pseudomonas aeruginosa with a mobile integron containing a difficult-to-mobilize β-lactamase cassette to minimize the potential for adaptive cassette reshuffling. We found that integron activity can decrease the overall survival rate but also improve the fitness of the surviving populations. Off-target inversions mediated by the integron accelerated plasmid adaptation by disrupting costly conjugative genes otherwise mutated in control populations lacking a functional integrase. Plasmids containing integron-mediated inversions were associated with lower plasmid costs and higher stability than plasmids carrying mutations albeit at the cost of a reduced conjugative ability. These findings highlight the potential for integrons to create structural variation that can drive bacterial evolution, and they provide an interesting example showing how antibiotic pressure can drive the loss of conjugative genes. IMPORTANCE Tackling the public health challenge created by antibiotic resistance requires understanding the mechanisms driving its evolution. Mobile integrons are widespread genetic platforms heavily involved in the spread of antibiotic resistance. Through the action of the integrase enzyme, integrons allow bacteria to capture, excise, and shuffle antibiotic resistance gene cassettes. This integrase enzyme is characterized by its ability to recognize a wide range of recombination sites, which allows it to easily capture diverse resistance cassettes but which may also lead to off-target reactions with the rest of the genome. Using experimental evolution, we tested the off-target impact of integron activity. We found that integrons increased the fitness of the surviving bacteria through extensive genomic rearrangements of the plasmids carrying the integrons, reducing their ability to spread horizontally. These results show that integrons not only accelerate resistance evolution but also can generate extensive structural variation, driving bacterial evolution beyond antibiotic resistance.
Divergent transcriptional response to thermal stress among life stages could constrain coral adaptation to climate change

The ability for adaptation to keep pace with environmental change largely depends on how efficiently selection can act on heritable genetic variation. Complex life cycles may either promote or constrain adaptation depending on the integration or independence of fitness-related traits over development. Reef-building corals exhibit complex life cycles and are sensitive to increasing temperatures, highlighting the need to understand the heritable potential of the thermal stress response and how it is regulated over development. Here we used tag-based RNA-seq to profile global gene expression in inshore and offshore P. astreoides adults and their offspring recruits in response to a 16-day heat stress, and larvae from separate families in response to a 4-day heat stress, to test whether gene expression patterns differentiating adult populations, and potentially underlying differences in thermal tolerance, persist in thermally naive life stages. Host developmental stage had a major effect on both host and symbiont expression, despite symbionts being directly inherited from parent colonies, and modulated the response to thermal stress, suggesting the holobiont response to selection varies across life stages. Populations also exhibited origin-specific treatment responses, but the magnitude of the response differed among populations and life stages. Inshore parents and their juvenile offspring exhibited a more robust response to heat stress compared to offshore-origin corals, indicating expression plasticity may be heritable. However, larval populations exhibited the opposite response, possibly due to stage-specific differences or exposure duration. Overall, this study shows that putatively adaptive regulatory variation can be heritable, but the identity of thermally responsive genes are stage-specific, which will have major implications for predicting the evolutionary response of corals in a changing environment.
Inferring sources of suboptimality in perceptual decision making using a causal inference task

Perceptual decision-making has been extensively modeled using the ideal observer framework. However, a range of deviations from optimality demand an extension of this framework to characterize the different sources of suboptimality. Prior work has mostly formalized these sources by adding biases and variability in the context of specific process models but are hard to generalize to more complex tasks. Here, we formalize suboptimalities as part of the brain’s probabilistic model of the task. Data from a traditional binary discrimination task cannot separate between different kinds of biases, or between sensory noise and approximate computations. We showed that this was possible using a recently developed causal inference task in which observers discriminated auditory cues in the presence of choice-uninformative visual cues. An extension of the task with different stimulus durations provided evidence for an increase in the precision of the computations with stimulus duration, separate from a decrease in observation noise.
NSF/αSNAP2-mediated cis-SNARE complex disassembly precedes membrane fusion generating the cell plate during Arabidopsis cytokinesis

Eukaryotic membrane fusion requires trans-SNARE complexes bridging the gap between adjacent membranes (Jahn and Scheller, 2006). Fusion between a transport vesicle and its target membrane transforms the trans-into a cis-SNARE complex. The latter interacts with the hexameric AAA+-ATPase NSF and its co-factor αSNAP, forming a 20S complex (Zhou et al., 2015; Zhao and Brunner, 2016). ATPase activity disassembles the SNARE complex into Qa-SNARE, which folds back onto itself, and its partners (Huang et al., 2019; Kim et al., 2021). Fusion of identical membranes has a different sequence of events (Baker and Hughson, 2016). The fusion partners each have cis-SNARE complexes to be broken up by NSF and αSNAP. The Qa-SNARE monomers are then stabilized by interaction with Sec1-type regulators (SM proteins) to form trans-SNARE complexes, as shown for the yeast vacuole (Baker et al., 2015). Membrane fusion in Arabidopsis cytokinesis is formally akin to vacuolar fusion (Müller and Jürgens, 2016). Membrane vesicles fuse with one another to form the partitioning membrane known as cell plate. Cis-SNARE complexes of cytokinesis-specific Qa-SNARE KNOLLE and its SNARE partners are assembled at the ER and delivered by traffic via Golgi/TGN to the cell division plane (Karnahl and Park et al., 2017). SM protein KEULE is required for the formation of trans-SNARE complexes between adjacent membrane vesicles (Park et al., 2012). Here, we identify the missing NSF-type AAA+-ATPase and its adaptor αSNAP2 required for disassembly of KNOLLE cis-SNARE complexes. In addition, we show that NSF is also required for other trafficking pathways and interacts with the respective Q-SNAREs. In conclusion, the SNARE complex disassembly machinery is conserved in plants and plays a unique essential role in cytokinesis.
Phorbolester-activated Munc13-1 and ubMunc13-2 exert opposing effects on dense-core vesicle secretion

Munc13 proteins are priming factors for SNARE-dependent exocytosis, which are activated by diacylglycerol (DAG)-binding to their C1-domain. Several Munc13 paralogs exist, but their differential roles are not well understood. We studied the interdependence of phorbolesters (DAG mimics) with Munc13-1 and ubMunc13-2 in mouse adrenal chromaffin cells. Although expression of either Munc13-1 or ubMunc13-2 stimulated secretion, phorbolester was only stimulatory for secretion when ubMunc13-2 expression dominated, but inhibitory when Munc13-1 dominated. Accordingly, phorbolester stimulated secretion in wildtype cells, or cells overexpressing ubMunc13-2, but inhibited secretion in Munc13-2/Unc13b knockout (KO) cells or in cells overexpressing Munc13-1. Phorbolester was more stimulatory in the Munc13-1/Unc13a KO than in WT littermates, showing that endogenous Munc13-1 limits the effects of phorbolester. Imaging showed that ubMunc13-2, but not Munc13-1, traffics to the plasma membrane with a time-course matching Ca2+-dependent secretion, and trafficking is independent of synaptotagmin-7 (syt7). However, in the absence of syt7, phorbolester became inhibitory for both Munc13-1 and ubMunc13-2 driven secretion, indicating that stimulatory phorbolester x Munc13-2 interaction depends obligatorily on functional pairing with syt7. Overall, DAG/phorbolester, ubMunc13-2 and syt7 form a stimulatory triad for dense-core vesicle priming.
BMAL1 loss in oligodendroglial lineage cells dysregulates myelination and sleep

Myelination depends on maintenance of oligodendrocytes that arise from oligodendrocyte precursor cells (OPCs). We show that the dynamic nature of oligodendroglia and myelination are regulated by the circadian transcription factor BMAL1. Bmal1 knockdown in OPCs during development – but not adulthood – decreases OPC proliferation, whereas BMAL1 regulates OPC morphology throughout life. OPC-specific Bmal1 deficiency impairs remyelination in an age-dependent manner, suggesting that age-associated decrements in circadian regulation of oligodendroglia may contribute to the deficient remyelination potential in demyelinating diseases like multiple sclerosis (MS). This oligodendroglial dysregulation and dysmyelination increase sleep fragmentation in OPC-specific Bmal1 knockout mice, and sleep fragmentation is causally associated with MS. These findings have broad mechanistic and therapeutic implications for numerous brain disorders that include both myelin and sleep phenotypes. One-Sentence Summary BMAL1 regulates the homeostatic maintenance of oligodendroglia and myelin, that subsequently controls sleep architecture.