Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative Methods for Discovering Generic Cis-Regulatory Motifs

for providing advices and suggestions throughout my thesis proposal. I would also like to thank my friends whom have helped me in research and technical discussion: My thanks to my parents and aunt Martha, for giving me support at the critical points of my work. At last, my eternal gratitude to my wife Yumiko for her steadfastness and patience in times of difficulties, especially in taking care of our children when I was not around. Summary One of the important problems in molecular biology is to understand the mechanisms that regulate the expressions of genes. A crucial step in this challenge is the ability to identify cis-regulatory motifs, e.g. binding sites in DNA sequences. Studying them can give us important clues in unraveling regulatory interactions of genes. The prediction of such regulatory elements is a problem where computational methods offer a great hope. This thesis presents a new class of algorithms for in silico discovery of regulatory elements. Firstly, we address the problem of motif finding for generic spaced motifs. Spaced motifs, an important class of transcription factors, consists of several short segments separated by spacers of different lengths. Existing motif finding algorithms are either designed for monad motifs or have assumptions on the spacer lengths or can handle at most two segments. To address this issue, we propose a new method called SPACE. They key idea is to obtain the motif as an integration of the submotifs as defined by the frequent pattern. Our method makes use of a novel scoring technique to measure the statistical significance of generic spaced motifs. With this measure we overcome the difficulty in handling biased samples by incorporating background sequence from various species. Based on experiments on real biological datasets and Tompa's iii benchmark datasets, we show that our algorithm outperforms the existing tools for spaced motifs in both sensitivity by 20.3% and specificity by 76%. And for monads, it performs as good as other tools. Secondly, although many tools have been developed for motif finding, they vary in their definitions on what constitute a motif and in their methods for finding statistically overrepresented motifs. There is no clear way for biologist to choose the motif finder that is most suitable for their task. There is an immediate need for a more effective method that allows the biologist to make use of these diverse motif finders for finding novel transcription factor accurately. However …
Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations

Estimating the mixing density of a mixture distribution remains an interesting problem in statistics literature. Using a stochastic approximation method, Newton and Zhang (1999) introduced a fast recursive algorithm for estimating the mixing density of a mixture. Under suitably chosen weights the stochastic approximation estimator converges to the true solution. In Tokdar et. al. (2009) the consistency of this recursive estimation method was established. However, the proof of consistency of the resulting estimator used independence among observations as an assumption. Here, we extend the investigation of performance of Newton's algorithm to several dependent scenarios. We first prove that the original algorithm under certain conditions remains consistent when the observations are arising form a weakly dependent process with fixed marginal with the target mixture as the marginal density. For some of the common dependent structures where the original algorithm is no longer consistent, we provide a modification of the algorithm that generates a consistent estimator.
The number of globular clusters around the iconic UDG DF44 is as expected for dwarf galaxies

There is a growing consensus that the vast majority of ultra-diffuse galaxies (UDGs) are dwarf galaxies. However, there remain a few UDGs that seem to be special in terms of their globular cluster (GC) systems. In particular, according to some authors, certain UDGs exhibit large GC populations when compared to expectations from their stellar (or total) mass. Among these special UDGs, DF44 in the Coma cluster is one of the better-known examples. DF44 has been claimed to have a relatively high number of GCs, $N_{GC}=74^{+18}_{-18}$, for a stellar mass of only $3\times 10^8$ $M_{ \odot }$ which would indicate a much larger dark halo mass than dwarfs of similar stellar mass. In this paper we revisit this number and, contrary to previous results, find $N_{GC}=21^{+7}_{-9}$ assuming that the distribution of the GCs follows the same geometry as the galaxy. If we assume that the GCs around DF44 are distributed in a (projected) circularly symmetric way and, if we use a less strict criterion for the selection of the GCs, we find $N_{GC}=18^{+23}_{-12}$. Making use of the $M_{\rm GC} - M_{\rm halo}$ relation, this number of GCs suggests a dark matter halo mass of $M_{halo}=1.1^{+0.4}_{-0.5} \times 10^{11} M_{\odot}$, a value which is consistent with the expected total mass for DF44 based on its velocity dispersion, $\sigma=33^{+3}_{-3}$ km s$^{-1}$. We conclude that the number of GCs around DF44 is as expected for regular dwarf galaxies of similar stellar mass and DF44 is not extraordinary in this respect.
Adaptive additive classification-based loss for deep metric learning

Recent works have shown that deep metric learning algorithms can benefit from weak supervision from another input modality. This additional modality can be incorporated directly into the popular triplet-based loss function as distances. Also recently, classification loss and proxy-based metric learning have been observed to lead to faster convergence as well as better retrieval results, all the while without requiring complex and costly sampling strategies. In this paper we propose an extension to the existing adaptive margin for classification-based deep metric learning. Our extension introduces a separate margin for each negative proxy per sample. These margins are computed during training from precomputed distances of the classes in the other modality. Our results set a new state-of-the-art on both on the Amazon fashion retrieval dataset as well as on the public DeepFashion dataset. This was observed with both fastText- and BERT-based embeddings for the additional textual modality. Our results were achieved with faster convergence and lower code complexity than the prior state-of-the-art.
Prey Capture by a Generalist Predator on Flowering and Nonflowering Ambush Sites: Are Inflorescences Higher Quality Hunting Sites?

Abstract Flowers are sites of frequent visitation by pollinators and numerous other insect groups and thus could potentially represent high-quality resource patches for generalist predators that are able to exploit these aggregations of prey. In northeastern old-fields, the sit-and-wait predator Phymata pennsylvanica Handlirsch (Heteroptera: Phymatidae) occupies ambush sites on both inflorescences and nonflowering green plants. In 1998, a study was conducted to test the hypothesis that flowers are higher-quality hunting sites than nonflowering plants for these predators. Prey capture events were directly observed during daily and hourly checks on fifth-instar bugs placed on flowering or nonflowering ambush sites in the field. Prey capture rate, prey size, and food consumption rate did not differ significantly between the two site types. However, the taxonomic composition of the prey caught on the two site types was strongly divergent, with adult Diptera dominating on flowering sites and sap-feeding Homoptera dominating on nonflowering sites. These results indicate that there are times when flowering and nonflowering sites can be largely functionally equivalent for a predator in terms of prey capture success and suggest that nonflowering sites may be more important in the foraging ecology of P. pennsylvanica and other old-field ambush predators than has previously been realized. However, one implication of the existence of distinct prey communities on the two site types is that the relative quality of flowering versus nonflowering sites could potentially vary unpredictably among different locations or years depending on the particular dynamics of the dominant prey groups on each site type.
Similar effects on sediment structure and infaunal community of two competitive intertidal soft-bottom burrowing crab species

The intertidal crabs Neohelice granulata and Cyrtograpsus angulatus are common on the south-west Atlantic coast but they rarely share the same microhabitat. They are similar in size and in several life history traits which promote competition. Neohelice granulata is the dominant species in intertidal soft-sediment and salt-marsh areas from southern Brazil to northern Patagonia Argentina, where it forms extensive burrowing beds. Its burrowing activity affects sediment characteristics as well as the infaunal community. When both species coexist N. granulata constrains the distribution and modifies some population characteristics and burrowing behaviour of C. angulatus . H owever,C. angulatus live in burrows forming dense burrowing beds in soft-bottom intertidal areas where N. granulata is absent. Where both species coexist, C. angulatus rarely constructs burrows and N. granulata clearly dominate soft-sediment areas forming conspicuous burrowing beds. This suggests that these crab species could have similar ecological roles in some effects on sediments related to burrowing activities. In this study, we experimentally compare their effects on sediment characteristics and infaunal community. The results of the experiment showed that C. angulatus modify sediment water and organic matter contents and grain size–frequency distributions similarly to N. granulata . N eitherN. granulata nor C. angulatus affected the mean abundance of infaunal organisms during the experiment but their variances showed the same patterns in many cases, indicating similar effects. These results indicate that C. angulatus can modify sediment characteristics similarly to N. granulata, and has similar interactions with infaunal species.
Stable carbon isotopes as indicators of increased water use efficiency and productivity in white spruce (Picea glauca (Moench) Voss) seedlings

The relationship among water use efficiency (WUE), productivity and carbon isotopic composition (δ 13 C) in white spruce (Picea glauca (Moench) Voss) seedlings was investigated. Sixteen hundred seedlings representing 10 controlled crosses were planted in the field in individual buried sand-filled cylinders. The soil water content in the cylinders was measured using time domain reflectometry over two growing seasons and seedling water use determined by water balance. Two watering treatments were imposed : irrigation and dry land. There was significant (1.6-2.0‰) genetic variation in needle δ 13 C. Ranking of crosses in terms of δ 13 C was generally maintained over watering treatments and there was not a significant genetic versus environmental interaction. There was a positive correlation between δ 13 C and both intrinsic and long-term WUE (more positive δ 13 C with increased WUE) and between δ 13 C and productivity, suggesting a correlation due to variation in photosynthetic capacity. Root to shoot ratios did not increase in water-stressed plants, indicating that responses to drought were primarily at the level of gas exchange, rather than through morphological changes. Our results indicate that it should be possible to use δ 13 C as a surrogate for WUE and to select white spruce genotypes for high WUE without compromising yield.
A Study of Apoptosis in Harderian Gland of Infected Chickens by IBDV (Infectious Bursal Disease Virus) with Using EM (Electronic Microscope)

Gumboro disease is one of the important diseases of poultry. This disease caused severe economical losses such as mortality, retardation of growth and immunosuppressant, especially at 3-6 weeks chickens. Histopathologic lesions were appeared in harderian gland and other lymphoid tissues. Destruction of infected lymphocytes with virus and peripheral cells and depletion of lymphocytes in harderian gland were caused by necrosis and apoptosis. In this study, 50 SPF 28 days olds chickens were divided in to tow groups (control and experimental) with 25 chickens in each group. The experimental group was infected orally by 106EID50 in 1 ml of IR499 (IRAN499) virus vvIBDV (very virulent Infectious Bursal Disease Virus); in control group physiological saline solution was used. AT 4th days post infection, all birds were sacrificed and their harderian gland were taken out and prepared for EM assay and light microscopic study. By light microscopic study about numeration of apoptotic cells, statistic difference were appeared (P=0.000) between control and experimental groups. With EM, apoptotic cells were appeared by submargination of chromatin of nuclear membrane concurrent chromatin condensation in experimental group but there were not any apoptotic cells in control group. Apoptosis was appeared by attachment of virus to IgM+ receptors of LB surface and to enforce of cells to secretion of some cytokines.VP2 and 17KD were major viral proteins induced apoptosis in bursa and spleen in infected chickens. In this study and previous studies were demonstrated that IBDV affected chickens with both of necrosis and apoptosis.
Relaxed regularization for linear inverse problems

We consider regularized least-squares problems of the form $\min_{x} \frac{1}{2}\Vert Ax - b\Vert_2^2 + \mathcal{R}(Lx)$. Recently, Zheng et al., 2019, proposed an algorithm called Sparse Relaxed Regularized Regression (SR3) that employs a splitting strategy by introducing an auxiliary variable $y$ and solves $\min_{x,y} \frac{1}{2}\Vert Ax - b\Vert_2^2 + \frac{\kappa}{2}\Vert Lx - y\Vert_2^2 + \mathcal{R}(x)$. By minimizing out the variable $x$ we obtain an equivalent system $\min_{y} \frac{1}{2} \Vert F_{\kappa}y - g_{\kappa}\Vert_2^2+\mathcal{R}(y)$. In our work we view the SR3 method as a way to approximately solve the regularized problem. We analyze the conditioning of the relaxed problem in general and give an expression for the SVD of $F_{\kappa}$ as a function of $\kappa$. Furthermore, we relate the Pareto curve of the original problem to the relaxed problem and we quantify the error incurred by relaxation in terms of $\kappa$. Finally, we propose an efficient iterative method for solving the relaxed problem with inexact inner iterations. Numerical examples illustrate the approach.
A Review on Digera Muricata (l.) Mart. - A Great Versatile Medicinal Plant

Digera muricata (L.) Mart. is an annual herb, growing to 20-70 cm tall. It is an important medicinal herb belongs to the Amaranthaceae family, found as a weed throughout India. Though almost all of its parts are used in traditional systems of medicines, leaves, roots and shoots are the most important parts which are used medicinally. The present article gives an account of updated information on its phytochemical and pharmacological properties. The review reveals that wide numbers of phytochemical constituents have been isolated from the plant which possesses activities like antibacterial, antifungal, diuretic, laxative, Free radical scavengic activity, anthelmintic, and various other important medicinal properties. The crushed plant is used as mild astringent in bowel complaints. The Leaves and spikes are used as a vegetable. Flowers and seeds used in the treatment of urinary discharges. For the last few decades or so, extensive research work has been done to prove its biological activities and pharmacology of its extracts. Analysis of various fractions of the D. muricata indicated the presence of flavonoids, alkaloids, terpenoids, saponins, coumarins, tannins, cardiac glycosides and anthraquinones.
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments

Graph representation learning is gaining popularity in a wide range of applications, such as social networks analysis, computational biology, and recommender systems. However, different with positive results from many academic studies, applying graph neural networks (GNNs) in a real-world application is still challenging due to non-stationary environments. The underlying distribution of streaming data changes unexpectedly, resulting in different graph structures (a.k.a., concept drift). Therefore, it is essential to devise a robust graph learning technique so that the model does not overfit to the training graphs. In this work, we present Hop Sampling, a straightforward regularization method that can effectively prevent GNNs from overfishing. The hop sampling randomly selects the number of propagation steps rather than fixing it, and by doing so, it encourages the model to learn meaningful node representation for all intermediate propagation layers and to experience a variety of plausible graphs that are not in the training set. Particularly, we describe the use case of our method in recommender systems, a representative example of the real-world non-stationary case. We evaluated hop sampling on a large-scale real-world LINE dataset and conducted an online A/B/n test in LINE Coupon recommender systems of LINE Wallet Tab. Experimental results demonstrate that the proposed scheme improves the prediction accuracy of GNNs. We observed hop sampling provides 7.97% and 16.93% improvements for NDCG and MAP compared to non-regularized GNN models in our online service. Furthermore, models using hop sampling alleviate the oversmoothing issue in GNNs enabling a deeper model as well as more diversified representation.
What can I do here? A Theory of Affordances in Reinforcement Learning

Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the features of their environment and the actions that are feasible. Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents. In this paper, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role in this case. On one hand, they allow faster planning, by reducing the number of actions available in any given situation. On the other hand, they facilitate more efficient and precise learning of transition models from data, especially when such models require function approximation. We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances and use it to estimate transition models that are simpler and generalize better.
Homotopy Theoretic and Categorical Models of Neural Information Networks

In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure in the form of assignments of resources, either computational or metabolic or informational. The starting point for this construction is the notion of summing functors and of Segal's Gamma-spaces in homotopy theory. The main results in this paper include functorial assignments of concurrent/distributed computing architectures and associated binary codes to networks and their subsystems, a categorical form of the Hopfield network dynamics, which recovers the usual Hopfield equations when applied to a suitable category of weighted codes, a functorial assignment to networks of corresponding information structures and information cohomology, and a cohomological version of integrated information.
How to generate the tip of branching random walks evolved to large times

In a branching process, the number of particles increasesexponentially with time, which makes numerical simulations for large timesdifficult. In many applications, however, only the region close to the extremalparticles is relevant (the “tip”). We present a simple algorithmwhich allows to simulate a branching random walk in one dimension, keepingonly the particles that arrive within some distance of the rightmost particle ata predefined time T. The complexity of the algorithm growslinearly with T. We can furthermore choose to require that therealizations have their rightmost particle arbitrarily far on the right from itstypical position. We illustrate our algorithm by evaluating an observable forwhich no other practical method is known.
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning

The emergence of new wireless technologies together with the requirement of massive connectivity results in several technical issues such as excessive interference, high computational demand for signal processing, and lengthy processing delays. In this work, we propose several beamforming techniques for an uplink cell-free network with centralized, semi-distributed, and fully distributed processing, all based on deep reinforcement learning (DRL). First, we propose a fully centralized beamforming method that uses the deep deterministic policy gradient algorithm (DDPG) with continuous space. We then enhance this method by enabling distributed experience at access points (AP). Indeed, we develop a beamforming scheme that uses the distributed distributional deterministic policy gradients algorithm (D4PG) with the APs representing the distributed agents. Finally, to decrease the computational complexity, we propose a fully distributed beamforming scheme that divides the beamforming computations among APs. The results show that the D4PG scheme with distributed experience achieves the best performance irrespective of the network size. Furthermore, the proposed distributed beamforming technique performs better than the DDPG algorithm with centralized learning only for small-scale networks. The performance superiority of the DDPG model becomes more evident as the number of APs and/or users increases. Moreover, during the operation stage, all DRL models demonstrate a significantly shorter processing time than that of the conventional gradient descent (GD) solution.
Seismogenic Mobile Network

In the presented paper we will focus on the optimal option of network arrangemnet of the VLF / LF EM radiation before the earthquakes. The paper discusses the new possibility of arrangement of VLF / LF network based on certain physical considerations, which is relatively simplified and completely different from the existing networks. It will increase to the limit the number of earthquakes fixed with the relevant EM emissions which is the aim of the new network arrangement.