Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental evidence for helper effects in a cooperatively breeding cichlid

Neolamprologus pulcher is a cooperatively breeding cichlid fish, in which helpers stay in their natal territory and help with brood care, territory defense, and maintenance. In this study we investigated helper effects by an experimental group size reduction in the field. After this manipulation, focal helpers in reduced groups tended to feed less, and small helpers visited the breeding shelter significantly more often than same-sized helpers in control groups. No evidence was found that remaining helpers compensated for the removed helpers by increasing territory defense and maintenance behavior. Breeders, however, did show a lower defense rate, possibly caused by an increase in brood care effort. Survival of fry was significantly lower in removal than control groups, which provides the first experimental proof in a natural population of fish that brood care helpers do effectively help. The data suggest that in small, generally younger, helpers, kin selection may be an important evolutionary cause of cooperation. Large helpers, however, who are generally older and less related to the breeders than small helpers are suggested to pay to be allowed to stay in the territory by helping. All group members benefit from group augmentation. Copyright 2005.
A Tree Building Technique for Overlay Multicasting in DiffServ Domains

Overlay multicasting technology is now considered as a promising alternative to the native IP multicasting. While many studies are currently focused on how to build an overlay tree/mesh on top of conventional networks in a self-organized fashion, only limited attention has been directed toward QoS provisioning. QoS can be provisioned by leveraging the implementation of differentiated services in the Internet. In this paper, we have proposed an incremental insertion tree-building algorithm to avoid extensive signaling overhead in differentiated service domains. The simulation results depict that on an average our approach produces a tree with a cost of only 10% to 20% more than the fully costoptimized tree. The relative delay penalties generated by the incremental insertion algorithm is comparable to that of the previously proposed Narada scheme. Although our scheme is designed for differentiated services, the algorithm can also be adapted to integrated services as well.
Biocompatible Phospholipid-Based Mixed Micelles for Tamoxifen Delivery: Promising Evidences from In - Vitro Anticancer Activity and Dermatokinetic Studies

Tamoxifen (TAM) is frequently prescribed for the management breast cancer, but is associated with the challenges like compromised aqueous solubility and poor bioavailability to the target site. It was envisioned to develop phospholipid-based mixed micelles to explore the promises offered by the biocompatible carriers. Various compositions were prepared, employing soya lecithin, polysorbate 80, sodium chloride/dextrose, and water, by self-assembled technique. The formulations were characterized for micromeritics and evaluated for in vitro drug release, hemolysis study, dermatokinetic studies on rodents, and cytotoxicity on MCF-7 cell lines. Cellular uptake of the system was also studied using confocal laser scanning microscopy. The selected composition was of sub-micron range (28.81 ± 2.1 nm), with spherical morphology. During in-vitro studies, the mixed micelles offered controlled drug release than that of conventional gel. Cytotoxicity was significantly enhanced and IC50 value was reduced that of the naïve drug. The bioavailability in epidermis and dermis skin layers was enhanced approx. fivefold and threefold, respectively. The developed nanosystem not only enhanced the efficacy of the drug but also maintained the integrity of skin, as revealed by histological studies. The developed TAM-nanocarrier possesses potential promises for safe and better delivery of TAM.
Cardiac Micro-CT Using ECG Gating Signal for Small Animal Imaging

Micro-CT system is widely used for small laboratory animals imaging as a pre-clinical technique. The image quality for in vivo micro-CT system is substantially influenced by the physiological motion, e.g. cardiac motion and respiratory motion. In this paper, a cardiac micro-CT system with a CMOS flat panel detector and a pulse generated X-ray tube is presented in details to address this issue. ECG detection circuit is designed to provide a cardiac gating signal as a synchronized trigger for the pulsed Xray generation. In this method, projection images are acquired at each step angle with exact R peak of ECG signal. The CT image is reconstructed using the cone beam FDK algorithm. The proposed method will be used in our micro-CT system to restrain heart motion artifacts. Keywords-Micro-CT, cardiac gating, small animal
Group matrix factorization for scalable topic modeling

Topic modeling can reveal the latent structure of text data and is useful for knowledge discovery, search relevance ranking, document classification, and so on. One of the major challenges in topic modeling is to deal with large datasets and large numbers of topics in real-world applications. In this paper, we investigate techniques for scaling up the non-probabilistic topic modeling approaches such as RLSI and NMF. We propose a general topic modeling method, referred to as Group Matrix Factorization (GMF), to enhance the scalability and efficiency of the non-probabilistic approaches. GMF assumes that the text documents have already been categorized into multiple semantic classes, and there exist class-specific topics for each of the classes as well as shared topics across all classes. Topic modeling is then formalized as a problem of minimizing a general objective function with regularizations and/or constraints on the class-specific topics and shared topics. In this way, the learning of class-specific topics can be conducted in parallel, and thus the scalability and efficiency can be greatly improved. We apply GMF to RLSI and NMF, obtaining Group RLSI (GRLSI) and Group NMF (GNMF) respectively. Experiments on a Wikipedia dataset and a real-world web dataset, each containing about 3 million documents, show that GRLSI and GNMF can greatly improve RLSI and NMF in terms of scalability and efficiency. The topics discovered by GRLSI and GNMF are coherent and have good readability. Further experiments on a search relevance dataset, containing 30,000 labeled queries, show that the use of topics learned by GRLSI and GNMF can significantly improve search relevance.
A 3-D CPU-FPGA-DRAM Hybrid Architecture for Low-Power Computation

The power budget is expected to limit the portion of the chip that we can power ON at the upcoming technology nodes. This problem, known as the utilization wall or dark silicon, is becoming increasingly serious. With the introduction of 3-D integrated circuits (ICs), it is likely to become more severe. Thus, how to take advantage of the extra transistors, made available by Moore's law and the onset of 3-D ICs, within the power budget poses a significant challenge to system designers. To address this challenge, we propose a 3-D hybrid architecture consisting of a CPU layer with multiple cores, a field-programmable gate array (FPGA) layer, and a DRAM layer. The architecture is designed for low power without sacrificing performance. The FPGA layer is capable of supporting a large number of accelerators. It is placed adjacent to the CPU layer, with a communication mechanism that allows it to access CPU data caches directly. This enables fast switches between these two layers. This architecture reduces the power and energy significantly, at better or similar performance. This then alleviates the dark silicon problem by letting us power ON more components to achieve higher performance. We evaluate the proposed architecture through a new framework we have developed. Relative to the out-of-order CPU, the accelerators on the FPGA layer can reduce function-level power by 6.9× and energy-delay product (EDP) by 7.2×, and application-level power by 1.9× and EDP by 2.2×, while delivering similar performance. For the entire system, this translates to a 47.5% power reduction relative to a baseline system that consists of a CPU layer and a DRAM layer. This also translates to a 72.9% power reduction relative to an alternative system that consists of a CPU layer, an L3 cache layer, and a DRAM layer.
Modeling Musical Influence with Topic Models

The role of musical influence has long been debated by scholars and critics in the humanities, but never in a data-driven way. In this work we approach the question of influence by applying topic-modeling tools (Blei & Lafferty, 2006; Gerrish & Blei, 2010) to a dataset of 24941 songs by 9222 artists, from the years 1922 to 2010. We find the models to be significantly correlated with a human-curated influence measure, and to clearly outperform a baseline method. Further using the learned model to study properties of influence, we find that musical influence and musical innovation are not monotonically correlated. However, we do find that the most influential songs were more innovative during two time periods: the early 1970's and the mid 1990's.
Numerics and subgrid‐scale modeling in large eddy simulations of stratocumulus clouds

Stratocumulus clouds are the most common type of boundary layer cloud; their radiative effects strongly modulate climate. Large eddy simulations (LES) of stratocumulus clouds often struggle to maintain fidelity to observations because of the sharp gradients occurring at the entrainment interfacial layer at the cloud top. The challenge posed to LES by stratocumulus clouds is evident in the wide range of solutions found in the LES intercomparison based on the DYCOMS‐II field campaign, where simulated liquid water paths for identical initial and boundary conditions varied by a factor of nearly 12. Here we revisit the DYCOMS‐II RF01 case and show that the wide range of previous LES results can be realized in a single LES code by varying only the numerical treatment of the equations of motion and the nature of subgrid‐scale (SGS) closures. The simulations that maintain the greatest fidelity to DYCOMS‐II observations are identified. The results show that using weighted essentially non‐oscillatory (WENO) numerics for all resolved advective terms and no explicit SGS closure consistently produces the highest‐fidelity simulations. This suggests that the numerical dissipation inherent in WENO schemes functions as a high‐quality, implicit SGS closure for this stratocumulus case. Conversely, using oscillatory centered difference numerical schemes for momentum advection, WENO numerics for scalars, and explicitly modeled SGS fluxes consistently produces the lowest‐fidelity simulations. We attribute this to the production of anomalously large SGS fluxes near the cloud tops through the interaction of numerical error in the momentum field with the scalar SGS model.
Neuropsychiatry: An Introductory Approach

Foreword Preface Acknowledgements Part I. Fundamental Concepts in Neuropsychiatry: 1. Neurosychiatry - an introduction and brief history 2. Essential behavioral neuroanatomy 3. Basic cognition 4. Complex cognition 5. Emotion 6. Personality 7. Psychological adaptation Part II. A Neuropsychiatric Approach to Evaluating the Patient: 8. Neuropsychiatric evaluation 9. The mental status examination 10. Clinical electrophysiology 11. Neuroimaging Part III. Applying the Approach to Neuropsychiatric Disorders: 12. Delirium 13. Dementia 14. Obsessive-compulsive disorder 15. Diminished motivation and apathy 16. Parkinson's disease 17. Alcoholism and alcohol-related disorders 18. Traumatic brain injury References.
A Cdk1 phosphomimic mutant of MCAK impairs microtubule end recognition

The microtubule depolymerising kinesin-13, MCAK, is phosphorylated at residue T537 by Cdk1. This is the only known phosphorylation site within MCAK’s motor domain. To understand the impact of phosphorylation by Cdk1 microtubule depolymerisation activity, we have investigated the molecular mechanism of the phosphomimic mutant T537E. This mutant significantly impairs microtubule depolymerisation activity and when transfected into cells causes metaphase arrest and misaligned chromosomes. We show that the molecular mechanism underlying the reduced depolymerisation activity of this phosphomimic mutant is an inability to recognise the microtubule end. The microtubule-end residence time is reduced relative to wild-type MCAK, whereas the lattice residence time is unchanged by the phosphomimic mutation. Further, the microtubule-end specific stimulation of ADP dissociation, characteristic of MCAK, is abolished by this mutation. Our data shows that T537E is unable to distinguish between the microtubule end and the microtubule lattice.
THE RELATIONSHIP BETWEEN PERCEIVED COACH LEADERSHIP BEHAVIOURS AND TEAM COHESION AMONG ROMANIAN ATHLETES

Cohesion is an important aspect of performance groups. Therefore it is important to investigate the antecedents of team cohesion, such as leadership. Although the relationship between coach leadership behaviors and team cohesion has been extensively studied, it is not so clear. In the present study, we examined the relationship between the perceived coach leadership behaviors and group cohesion. The 81 participants have completed the Leadership Scale for Sport (LSS; athlete perceptions version) and Group Environment Questionnaire (GEQ). The results show that perceived cohesion of the basket team members is significantly lower than the perceived cohesion of the volleyball, rugby and handball team members. Most of the coach leadership behaviors were negatively associated with the overall team cohesion and its dimensions. It was found that the coach leadership behaviors did not predict the overall team cohesion, when controlling the effects of gender and the type of sport. Theoretical and practical implications are
Model-based fault diagnosis of sequential circuits and its acceleration

Describes an algorithm for the location of faulty components in digital circuits using the model-based approach to circuit fault diagnosis. The model-based approach is first extended to apply to synchronous sequential circuits. Acceleration strategies that exploit domain knowledge particular to digital circuits are then proposed. The effect of these acceleration strategies is a drastic reduction in the execution time of the diagnosis procedure. The effectiveness and performance of the diagnosis algorithm are illustrated through an example for which encouraging results are obtained. <<ETX>>
Evidence for an inositol hexakisphosphate-dependent role for Ku in mammalian nonhomologous end joining that is independent of its role in the DNA-dependent protein kinase

Nonhomologous end-joining (NHEJ) is an important pathway for the repair of DNA double-strand breaks (DSBs) and plays a critical role in maintaining genomic stability in mammalian cells. While Ku70/80 (Ku) functions in NHEJ as part of the DNA-dependent protein kinase (DNA-PK), genetic evidence indicates that the role of Ku in NHEJ goes beyond its participation in DNA-PK. Inositol hexakisphosphate (IP6) was previously found to stimulate NHEJ in vitro and Ku was identified as an IP6-binding factor. Through mutational analysis, we identified a bipartite IP6-binding site in Ku and generated IP6-binding mutants that ranged from 1.22% to 58.48% of wild-type binding. Significantly, these Ku IP6-binding mutants were impaired for participation in NHEJ in vitro and we observed a positive correlation between IP6 binding and NHEJ. Ku IP6-binding mutants were separation-of-function mutants that bound DNA and activated DNA-PK as well as wild-type Ku. Our observations identify a hitherto undefined IP6-binding site in Ku and show that this interaction is important for DSB repair by NHEJ in vitro. Moreover, these data indicate that in addition to binding of exposed DNA termini and activation of DNA-PK, the Ku heterodimer plays a role in mammalian NHEJ that is regulated by binding of IP6.
Peeking into Your App without Actually Seeing It: UI State Inference and Novel Android Attacks

The security of smartphone GUI frameworks remains an important yet under-scrutinized topic. In this paper, we report that on the Android system (and likely other OSes), a weaker form of GUI confidentiality can be breached in the form of UI state (not the pixels) by a background app without requiring any permissions. Our finding leads to a class of attacks which we name UI state inference attack. The underlying problem is that popular GUI frameworks by design can potentially reveal every UI state change through a newly-discovered public side channel -- shared memory. In our evaluation, we show that for 6 out of 7 popular Android apps, the UI state inference accuracies are 80-90% for the first candidate UI states, and over 93% for the top 3 candidates. Even though the UI state does not reveal the exact pixels, we show that it can serve as a powerful building block to enable more serious attacks. To demonstrate this, we design and fully implement several new attacks based on the UI state inference attack, including hijacking the UI state to steal sensitive user input (e.g., login credentials) and obtain sensitive camera images shot by the user (e.g., personal check photos for banking apps). We also discuss non-trivial challenges in eliminating the identified side channel, and suggest more secure alternative system designs.
Supercomputers Ready for Use as Discovery Machines for Neuroscience

NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to achieve excellent scaling. K is capable of simulating networks corresponding to a brain area with 108 neurons and 1012 synapses in the worst case scenario of random connectivity; for larger networks of the brain its hierarchical organization can be exploited to constrain the number of communicating computer nodes. We discuss the limits of the software technology, comparing maximum filling scaling plots for K and the JUGENE BG/P system. The usability of these machines for network simulations has become comparable to running simulations on a single PC. Turn-around times in the range of minutes even for the largest systems enable a quasi interactive working style and render simulations on this scale a practical tool for computational neuroscience.
A spiking network for object and ego-motion detection in roving robots

This paper proposes a neural-based model for ego-motion detection and compensation in applications related to bionic antennae. Touch or near range sensors are used on moving platforms to detect the presence of surrounding objects: additional feature could also be extracted, like distance, material characteristics and the shape of objects. The processing and control architecture is based on spiking neurons and in particular a series of resonate and fire neurons has been used to extract important features from the sensory data. The use of spiking resonant neuron arrays is treated as a general methodology for bio-inspired feature clustering. The use of the resonance allows to emphasize particular signal contents which depend on ego-motion: the detection and compensation of such components hidden in the acquired signal is a real added value towards the construction of robust and bio-inspired methodologies for simple and efficient forward models. The methodology is applied to a bionic antenna which was mounted on a roving robot. This could be very useful in different scenarios enriching the multimodal sensory system usually available on a navigation platform. Simulation results and experiments performed on a roving platform are reported.
Sensorless control of PMSM fed through the sinusoidal filter

The paper presents results of a research on the sensorless drive with PMSM supplied through the sinusoidal filter. The back electromotive force based observer is used to the shaft position estimation. Used in the study, the observer's correction function, contains multi integrators in the path, which improves the position estimation. Otherwise than in a typical implementation of the position observer, the presence of the filter was not taken into account in observer structure, however, it was taken into account in the observer parameters. This type of control allows the use of conventional control system and there is no need to take account the filter in the control circuit. Presented concept has been proven in laboratory.
Simplex meshes: a general representation for 3D shape reconstruction

Simplex meshes are simply connected meshes that are topologically dual of triangulations. In a previous work we have introduced the Simplex mesh representation for performing recognition of partially occluded smooth objects (ICCV'93, p.103-12). In this paper, we present a physically-based approach for recovering three-dimensional objects, based on the geometry of Simplex meshes. Elastic behavior is modelled by local stabilizing functionals, controlling the mean curvature through the Simplex angle extracted at each vertex. Those functionals are viewpoint-invariant, intrinsic and scale-sensitive. Unlike deformable surfaces defined on regular grids, Simplex meshes are highly adaptive structures, and we have developed a refinement process for increasing the mesh resolution at highly curved or inaccurate parts. Furthermore, operations for connecting Simplex meshes are performed to recover complex models from parts with simpler shapes. <<ETX>>
Distributed regression over sensor networks: An support vector machine approach

This paper presents a distributed support vector regression (SV R) algorithm for sensor networks. The idea behind this algorithm is to make use of the structure similarity between sensor networks and SV Rs with 2D input data in order to implement SV R in a distributed way. During training stage, each sensor node provides its 2D coordinates as an input pattern and a sensory data as an output to the algorithm. By using local wireless communication with neighbors and kernel function with finite support, each sensor node independently learns its own Lagrange multipliers. During evaluation stage of learned regression function, each sensor node obtains a local result by communicating with local neighbors and estimates a global result by using a consensus algorithm. Simulations are provided to verify the proposed algorithm.