Miracle-dz/UniFAR-FTUs huggingface dataset:
Log-concave polynomials, I: Entropy and a deterministic approximation algorithm for counting bases of matroids

We give a deterministic polynomial time $2^{O(r)}$-approximation algorithm for the number of bases of a given matroid of rank $r$ and the number of common bases of any two matroids of rank $r$. To the best of our knowledge, this is the first nontrivial deterministic approximation algorithm that works for arbitrary matroids. Based on a lower bound of Azar, Broder, and Frieze [ABF94] this is almost the best possible result assuming oracle access to independent sets of the matroid. There are two main ingredients in our result: For the first, we build upon recent results of Adiprasito, Huh, and Katz [AHK15] and Huh and Wang [HW17] on combinatorial hodge theory to derive a connection between matroids and log-concave polynomials. We expect that several new applications in approximation algorithms will be derived from this connection in future. Formally, we prove that the multivariate generating polynomial of the bases of any matroid is log-concave as a function over the positive orthant. For the second ingredient, we develop a general framework for approximate counting in discrete problems, based on convex optimization. The connection goes through subadditivity of the entropy. For matroids, we prove that an approximate superadditivity of the entropy holds by relying on the log-concavity of the corresponding polynomials.
Semantic Segmentation with Scarce Data

Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain. Coarsely annotated data provides an interesting alternative as it is usually substantially more cheap. In this work, we present a method to leverage coarsely annotated data along with fine supervision to produce better segmentation results than would be obtained when training using only the fine data. We validate our approach by simulating a scarce data setting with less than 200 low resolution images from the Cityscapes dataset and show that our method substantially outperforms solely training on the fine annotation data by an average of 15.52% mIoU and outperforms the coarse mask by an average of 5.28% mIoU.
Pharmacodynamics of the Glutamate Receptor Antagonists in the Rat Barrel Cortex

Epipial application is one of the approaches for drug delivery into the cortex. However, passive diffusion of epipially applied drugs through the cortical depth may be slow, and different drug concentrations may be achieved at different rates across the cortical depth. Here, we explored the pharmacodynamics of the inhibitory effects of epipially applied ionotropic glutamate receptor antagonists CNQX and dAPV on sensory-evoked and spontaneous activity across layers of the cortical barrel column in urethane-anesthetized rats. The inhibitory effects of CNQX and dAPV were observed at concentrations that were an order higher than in slices in vitro, and they slowly developed from the cortical surface to depth after epipial application. The level of the inhibitory effects also followed the surface-to-depth gradient, with full inhibition of sensory evoked potentials (SEPs) in the supragranular layers and L4 and only partial inhibition in L5 and L6. During epipial CNQX and dAPV application, spontaneous activity and the late component of multiple unit activity (MUA) during sensory-evoked responses were suppressed faster than the short-latency MUA component. Despite complete suppression of SEPs in L4, sensory-evoked short-latency multiunit responses in L4 persisted, and they were suppressed by further addition of lidocaine suggesting that spikes in thalamocortical axons contribute ∼20% to early multiunit responses. Epipial CNQX and dAPV also completely suppressed sensory-evoked very fast (∼500 Hz) oscillations and spontaneous slow wave activity in L2/3 and L4. However, delta oscillations persisted in L5/6. Thus, CNQX and dAPV exert inhibitory actions on cortical activity during epipial application at much higher concentrations than in vitro, and the pharmacodynamics of their inhibitory effects is characterized by the surface-to-depth gradients in the rate of development and the level of inhibition of sensory-evoked and spontaneous cortical activity.
A Flexible Suction Based Grasp Tool and Associated Grasp Strategies for Handling Meat

In this paper, we present a suction cup based gripper tool for grasping pork bellies. The tool is attached to a 6DOF robot arm in order to test different grasp strategies. The task of grasping pork bellies is difficult since the objects are highly flexible. Due to the deformations, an airtight connection can form between the meat and the object below it. During the lift, this can lead to a vacuum which sometimes causes the grasp to fail. Thus it is important that the tool and the grasp strategy is able to minimize the effects of this vacuum in order to generate a stable grasp. Furthermore, there is a high variation in the meat products and the initial placement of the products. Thus the tool has to be sufficiently flexible to handle the different cases robustly.
Isolation and Characterization of the Flavonol Regulator CcMYB12 From the Globe Artichoke [Cynara cardunculus var. scolymus (L.) Fiori]

Flavonoids are a well-studied group of secondary metabolites, belonging to the phenylpropanoid pathway. Flavonoids are known to exhibit health promoting effects such as antioxidant capacities, anti-cancer and anti-inflammatory activity. Globe artichoke is an important source of bioactive phenolic compounds, including flavonoids. To study the regulation of their biosynthesis, a R2R3-MYB transcription factor, CcMYB12, was isolated from artichoke leaves. Phylogenetic analysis showed that this protein belongs to the MYB subgroup 7 (flavonol-specific MYB), which includes Arabidopsis AtMYB12, grapevine VvMYBF1, and tomato SlMYB12. CcMYB12 transcripts were detected specifically in artichoke immature inflorescence and young leaves and overlapped with the profiles of flavonol biosynthetic genes. Electrophoretic mobility shift assays (EMSAs) revealed that recombinant CcMYB12 protein is able to bind to ACII element, a DNA binding site ubiquitously present in the promoters of genes encoding flavonol biosynthetic enzymes. In transgenic Arabidopsis plants, the overexpression of CcMYB12 activated the expression of endogenous flavonol biosynthesis genes, leading to an increase of flavonol accumulation and a decrease of anthocyanins in leaves. Likewise, in transgenic tobacco petals and leaves, the overexpression of CcMYB12 decreased anthocyanin levels and increased flavonols.
Computationally-Robust and Efficient Prioritized Whole-Body Controller with Contact Constraints

In this paper, we devise methods for the multiobjective control of humanoid robots, a.k. a. prioritized whole-body controllers, that achieve efficiency and robustness in the algorithmic computations. We use a form of whole-body controllers that is very general via incorporating centroidal momentum dynamics, operational task priorities, contact reaction forces, and internal force constraints. First, we achieve efficiency by solving a quadratic program that only involves the floating base dynamics and the reaction forces. Second, we achieve computational robustness by relaxing task accelerations such that they comply with friction cone constraints. Finally, we incorporate methods for smooth contact transitions to enhance the control of dynamic locomotion behaviors. The proposed methods are demonstrated both in simulation and in real experiments using a passive-ankle bipedal robot.
Venga!: climbing in mixed reality

Scary climbing situations can appear when climbing on big walls. Some stones may be slippery due to humidity or smoothness and distances between belaying bolts can be large whereby a deep fall is risked. In such situations, the stress level increases, climbing performance decreases and an ascent might even be aborted. Furthermore, hazardous accidents can happen when the concentration is disturbed by stress and fear. Moreover, a beginner has to overcome some hurdles such as learning movements and belaying techniques as well as overcoming acrophobia or fear of falling down. We try to facilitate these hurdles with augmentation of the human in Mixed Reality (MR) superhuman sport climbing. In this paper, we present the development of a MR climbing system that tackles these hurdles by allowing a user to climb in safe height at ground level in reality while seeing his or her own body visualised in a much more demanding situation (e.g. in the middle of a big wall) in a Virtual Environment (VE) through a HMD. Additionally an audience can be integrated into the VE via a collaboration with an operator of a virtual drone. The drone operator has the ability to provide additional feedback, e.g. point to a certain stone with a virtual laser and can communicate with the climber via microphone. Hence, if a climber gets stuck in a climbing route, the drone operator can suggest the next move in the VE.
Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations

Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from this http URL annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.
Improving sentiment classification from high volatility financial news

Previous research1 found a positive correlation between the number of mentions of a company and transaction volume of the company's stock. Given huge amount of online news articles everyday, it would be useful if we can discover the sentiment polarity from financial news for market trend analysis. Generalpurpose sentiment lexicon such as SentiWordNet usually does not work well in specific domains. Machine learning from corpus in a domain can help create a domain-specific lexicon, which can usually improve the effects of sentiment analysis. But we need appropriate amount of quality training data to learn features in different domains. In this paper, we propose to extract significant terms from news at high volatility market for sentiment classification. First, high impact financial news articles are determined by high market price variations. Then, significant terms are learned based on the distribution of terms in news articles. Finally, these terms are used to augment the generalpurpose sentiment lexicon to improve sentiment classification. The experimental results on major financial news media showed that our proposed method can effectively classify news sentiments at highly varied markets. Augmentation of SentiWordNet by significant terms can greatly improve the classification performance. Further investigation is needed to evaluate the performance of the proposed approach in larger scale.
Randomized greedy algorithms for covering problems

Greedy algorithms provide a fast and often also effective solution to many combinatorial optimization problems. However, it is well known that they sometimes lead to low quality solutions on certain instances. In this paper, we explore the use of randomness in greedy algorithms for the minimum vertex cover and dominating set problem and compare the resulting performance against their deterministic counterpart. Our algorithms are based on a parameter y which allows to explore the spectrum between uniform and deterministic greedy selection in the steps of the algorithm and our theoretical and experimental investigations point out the benefits of incorporating randomness into greedy algorithms for the two considered combinatorial optimization problems.
KAP1 facilitates reinstatement of heterochromatin after DNA replication

Abstract During cell division, maintenance of chromatin features from the parental genome requires their proper establishment on its newly synthetized copy. The loss of epigenetic marks within heterochromatin, typically enriched in repetitive elements, endangers genome stability and permits chromosomal rearrangements via recombination. However, how histone modifications associated with heterochromatin are maintained across mitosis remains poorly understood. KAP1 is known to act as a scaffold for a repressor complex that mediates local heterochromatin formation, and was previously demonstrated to play an important role during DNA repair. Accordingly, we investigated a putative role for this protein in the replication of heterochromatic regions. We first found that KAP1 associates with several DNA replication factors including PCNA, MCM3 and MCM6. We then observed that these interactions are promoted by KAP1 phosphorylation on serine 473 during S phase. Finally, we could demonstrate that KAP1 forms a complex with PCNA and the histone-lysine methyltransferase Suv39h1 to reinstate heterochromatin after DNA replication.
The Design and Evaluation of the Cloud-based Learning Components with the Use of the Systems of Computer Mathematics

In the article the problems of the systems of computer mathematics use as a tool for the students learning and research activities support are investigated. The promising ways of providing access to the mathematical software in the university learning and research environment are considered. The special aspects of pedagogical applications of these systems to support operations research study in the process of bachelors of informatics training are defined. The design and evaluation of the cloud-based learning components with the use of the systems of computer mathematics (on the example of Maxima system) as enchasing the investigative approach to learning of engineering and mathematics disciplines and increasing the pedagogical outcomes is justified. The set of psychological and pedagogical and also technological criteria of evaluation is substantiated. The results of pedagogical experiment are provided. The analysis and evaluation of existing experience of mathematical software use both in local and cloud-based settings is proposed.
Segmentation of affected skin lesion with blind deconvolution and L*a*b colour space

Skin diseases have been increased in the recent decades and if detected earlier, can be treated effectively. Segmentation of affected skin lesion using dermoscopy or dermatoscopic images for cancer diagnosis has received attention from the community of image processing. This research work proposed a simple yet effective method in Dermoscopy image segmentation for the affected skin lesion. In order to remove undesirable pixels for better segmentation of images, blind deconvolution (BD) is used. After applying BD, the resultant RGB image is transformed into L*a*b colour channel. The 'a' channel from L*a*b is extracted as a feature value for the segmentation of the affected region. In order to yield better segmented skin lesion from the 'a' channel image, Otsu's thresholding with Morphology is used as a post-processing technique. A series of experimental results achieved an accuracy of 95% using 150 dermoscopy images.
Experimental Investigation of Natural Fiber Reinforced Polymers

The potential usage of virgin Low density polyethelyne (LDPE) reinforced with different concentrations (2%, 5% and 6% by weight) of treated rice straw with different lengths (2 mm, 4 mm and 6 mm) is investigated to produce high value products that have technical and environmental demand. The two treatment methods used for rice straw are alkali and acidic treatments of rice straw. The removal of impurities and waxy substances from fiber surface avoid creation of rougher topography after treatment and improves the quality of fiber, also content of hemi cellulose and lignin decrease so increase effectiveness of fiber due to dispersing of fiber in matrix. The reinforcing material is embedded in the matrix material to enhance tensile and flexural behaviors of the synthesized composite. The result of investigating these two mechanical properties, using statistical analysis & design of experiments, showed an enhancement in the mechaniccal properties of the virgin polymer composite compared to the virgin polymer. The flexural stress of the composite increased three times the virgin flexural stress, while the tensile stress increased eight times the original tensile stress.
Recovering runtime architecture models and managing their complexity using dynamic information and composite structures

Program comprehension during software maintenance is a difficult task, hence the need to support it by recovering the as-built architecture of the system to be maintained. In this paper, we propose a method to recover runtime architecture models of object-oriented systems. The method combines static and dynamic analysis to recover an Object Graph (OG) and uses two techniques to manage the complexity of this graph.
Multi-objective black-box test case selection for cost-effectively testing simulation models

In many domains, engineers build simulation models (e.g., Simulink) before developing code to simulate the behavior of complex systems (e.g., Cyber-Physical Systems). Those models are commonly heavy to simulate which makes it difficult to execute the entire test suite. Furthermore, it is often difficult to measure white-box coverage of test cases when employing such models. In addition, the historical data related to failures might not be available. This paper proposes a cost-effective approach for test case selection that relies on black-box data related to inputs and outputs of the system. The approach defines in total five effectiveness measures and one cost measure followed by deriving in total 15 objective combinations and integrating them within Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). We empirically evaluated our approach with all these 15 combinations using four case studies by employing mutation testing to assess the fault revealing capability. The results demonstrated that our approach managed to improve Random Search by 26% on average in terms of the Hypervolume quality indicator.
MovieExplorer: building an interactive exploration tool from ratings and latent taste spaces

This 1 paper introduces and evaluates MovieExplorer, an interactive exploration tool designed to use the data available in a traditional ratings-based recommender system to provide an interactive interface more suited to user exploration and fulfillment of short-term recommendation needs. A field deployment with 1,950 users showed that users found the tool useful for a variety of exploration and short-term recommendation tasks, even preferring it to existing interfaces for several tasks. Experimentation with several design features found that the actual user navigation algorithms were significant (user satisfaction was lower for algorithms with faked controls) and that offering positive and negative feedback options led to increased feedback and user retention.
Designing with and for Preschoolers: A Method to Observe Tangible Interactions with Spatial Manipulatives

To date, the developmental needs and abilities of children under 4 years old have been insufficiently taken into account in the early stages of interaction design. This paper addresses this gap in the research by exploring how children between the ages of 26 and 43 months interact with spatial manipulatives. To this end, we modified intervention techniques for early spatial learning found in cognitive developmental studies and combined these with design methods used in Child-Tangible Interaction (CTI). From the former we borrowed the Preschool Embedded Figures Test (PEFT), and from the latter a storytelling approach incorporated into structured tasks with hands-on tools. In this paper, we first discuss related work on early spatial learning and CTI methods. Then, we describe a case study conducted with 14 parent-child dyads. Finally, we present the results, which offer insight into young children's mental rotation skills, different rotation action strategies and parental input requirements. Our findings contribute to design methods to elicit age specific knowledge about young children's hands-on learning, and set forth techniques and design considerations for evidence-based CTI to scaffold early spatial thinking skills.
Are adolescent elite athletes less psychologically distressed than controls? A cross-sectional study of 966 Norwegian adolescents

Introduction Psychological distress is increasing among adolescents and clusters with other mental health problems such as eating problems. The aim of this study was to investigate the prevalence of psychological distress among young elite athletes and age-matched controls and whether prevalence figures may be attributed to perfectionism and eating problems. Methods First-year athletes from all Norwegian elite sport high schools (n=711) and 500 students from randomly selected ordinary high schools were eligible for this cross-sectional study. In total, 611 athletes and 355 student controls provided self-report data about psychological distress, perfectionism, and eating problems (ie, body dissatisfaction and a drive for thinness), as well as their physical training/activity. Results A significantly higher proportion of controls scored above the cutoff point for marked psychological distress. Physical activity above the recommended levels for this age group predicted psychological distress among the controls, while the opposite was found in the student elite athlete sample. In both samples, perfectionistic concerns, ie, concern over mistakes, predicted overall psychological distress. However, among elite athletes, perfectionistic concerns were particularly associated with clinically significant psychological distress. Moreover, the impact of eating problems was negligible. Conclusion Results from this study highlight the need to target the maladaptive perfectionistic concerns to prevent psychological distress among young athletes as well as among their age-matched nonathlete counterparts.
Accelerate deep Q-network learning by n-step backup

Reinforcement learning is notorious for being difficult to train and for its prolonged training time. In this paper, we propose a new n-step backup methods to accelerate deep Q-network learning, which results in faster and better convergence. Although it is proven that n-step backup does not perform well in off-policy experience replay, we obtain improved performance by discovering and solving a number of problems. Specifically, we reduce the bias through n -step backup while avoiding the accompanied larger variance and higher computation overhead. Secondly, we attack the deteriorated overestimation problem in n-step backup, while enjoying faster propagation. We evaluate the performance of our approach in the Arcade Learning Environment.