Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing Good Habits Using Deep Learning Techniques

in this present work, we emphasize the main question about success: why do some people seem to be successful while the great majority of the rest of us seem not to be? To be more specific, the challenge of this work is to propose a novel system to assist people in developing good habits using deep learning techniques. To achieve this goal, we propose the use of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). The two best-known supervised learning method. The first step consists of using a deep RNN neural network to perform the captioning of the user’s activities. Then use another deep CNN to do the classification of the activity based on the result of the first network and then decide whether what the user is doing is a good habit or not. To our knowledge, there is no previous work dealing with this topic through a computer science lens, and this will be of greater value to most of the people who are interested in developing new good habits. This system will also suggest new directions for basic and applied research on success in general and good habits in particular.
Comprehensive Review of SDN Controller Placement Strategies

Software-Defined Networking (SDN) is a network paradigm introduced to overcome the inherent challenges of traditional networks. Its architecture is either deployed with a single controller or multiple controllers. While the first is not suitable for large-scale networks, the latter is confronted with a controller placement problem (CPP) in a large-scale network environment. CPP involves the challenge of deploying the optimal number of controllers within a network while meeting certain performance requirements considered conflicting in nature such as reliability, load balancing, latency, energy efficiency, and computation time. A single optimal or random placement may not be feasible in CPP and careful planning is of the essence to find an appropriate trade-off among the metrics. To achieve this, several CPP approaches have been proposed, developed, and deployed over the years, each having its unique objectives, strengths, and weaknesses. Therefore, this paper performed a comprehensive review of some of the existing approaches to identify the unique solutions offered, comprehend the different strategies and the challenges that exist as well as provide researchers with future directions aimed at improving the optimum location and allocation of controllers, in particular, for SDN application in wireless sensor network (WSN). The findings revealed several existing solutions and algorithms as well as several challenges such as the need for an efficient algorithm, attack-aware, cost-aware, and energy-aware CPP schemes while achieving a good quality of service.
On the Gaussian Cramér-Rao Bound for Blind Single-Input Multiple-Output System Identification: Fast and Asymptotic Computations

The Cramér-Rao Bound (CRB) is a powerful tool to assess the performance limits of a parameter estimation problem for a given statistical model. In particular, the Gaussian CRB (i.e., the CRB obtained assuming the data are Gaussian) corresponds to the worst case; giving the largest CRB among a large class of data distributions. This makes it very useful in practice since optimizing under the Gaussian data assumption can be interpreted as a min-max optimization (i.e., minimizing the largest CRB). The Gaussian CRB is also the corresponding bound of Second-Order Statistics (SOS)-based estimation methods, which are frequently used in practice. Despite its practicality, computing this bound might be cumbersome in some cases, particularly in the case where the input is assumed deterministic and has a large number of samples. In this paper, we address this computational issue by proposing a fast computation for the deterministic Gaussian CRB of Single-Input Multiple Output (SIMO) blind system identification. More precisely, we exploit circulant matrix properties to reduce the cost from cubic to quadratic with respect to the sample size. Moreover, we derive a closed-form formula for the asymptotic (large sample size) Gaussian CRB and show how it can be computed using the residue theorem.
The requirement for cyclin E in c-Myc overexpressing breast cancers

ABSTRACT Basal-like triple-negative breast cancers frequently express high levels of c-Myc. This oncoprotein signals to the core cell cycle machinery by impinging on cyclin E. High levels of E-type cyclins (E1 and E2) are often seen in human triple-negative breast tumors. In the current study, we examined the requirement for E-type cyclins in the c-Myc-driven mouse model of breast cancer (MMTV-c-Myc mice). To do so, we crossed cyclin E1- (E1-/-) and E2- (E2-/-) deficient mice with MMTV-c-Myc animals, and observed the resulting cyclin E1-/-/MMTV-c-Myc and cyclin E2-/-/MMTV-c-Myc females for breast cancer incidence. We found that mice lacking cyclins E1 or E2 developed breast cancers like their cyclin Ewild-type counterparts. In contrast, further reduction of the dosage of E-cyclins in cyclin E1-/-E2+/-/MMTV-c-Myc and cyclin E1+/-E2-/-/MMTV-c-Myc animals significantly decreased the incidence of mammary carcinomas, revealing arole for E-cyclins in tumor initiation. We also observed that depletion of E-cyclins in human triple-negative breast cancer cell lines halted cell cycle progression, indicating that E-cyclins are essential for tumor cell proliferation. In contrast, we found that the catalytic partner of E-cyclins, the cyclin-dependent kinase 2 (CDK2), is dispensable for the proliferation of these cells. These results indicate that E-cyclins, but not CDK2, play essential and rate-limiting roles in driving the proliferation of c-Myc overexpressing breast cancer cells.
Fire and stability in the postglacial forests of southwest Nova Scotia

Fire has been a major source of instability in the postglacial forests surrounding Everitt Lake in southwest Nova Scotia. Time series analyses of preserved pollen and charcoal reveal the processes involved. Correlograms and pollen sequence splits confirm that prior to 6000 years BP repeated widespread fires maintained the forest in constant disequilibrium. Each fire triggered rapid, major forest composition changes, especially the proliferation of invading tree species that had previously been excluded by competition. The absence of such widespread fires is shown to have left most tree populations in near-equilibrium for much of the last 6000 years. Power spectra for different pollen sections suggest the changing ecological 'strategies' and affinities of tree taxa through time and imply that much forest change occurred by piecewise replacement of individual stands in the forest mosaic.
Initial assessment of protein and amino acid digestive dynamics in protein-rich feedstuffs for broiler chickens

A study evaluating apparent digestibilities of protein and amino acids and their corresponding digestion rates in four small intestinal sites in broiler chickens was completed to further investigate dietary optimisation via synchronised nutrient digestion and absorption. A total of 288 male Ross 308 broiler chickens were offered semi-purified diets with eight protein-rich feedstuffs, including; blood meal (BM), plasma protein meal (PPM), cold pressed (CCM) and expeller-pressed (ECM) canola meal, high (SBM HCP) and low (SBM LCP) crude protein soybean meals, lupins and peas. Diets were iso-caloric, iso-nitrogenous and the test ingredient was the sole source of dietary nitrogen. Each diet was offered to 6 bioassay cages with 6 birds per cage from day 21 to 28 post hatch. On day 28, all birds were euthanized and digesta samples were collected from the proximal jejunum, distal jejunum, proximal ileum and distal ileum to determine apparent protein and amino acids digestibility coefficients, digestion rates and potential digestible protein and amino acids. Dietary protein source significantly influenced energy utilisation, nitrogen retention, apparent protein (N) digestibilities, digestion rates and potential digestible protein along the small intestine. Diets containing BM and SBM LCP exhibited the highest protein digestion rate and potential digestible protein, respectively. Digestibility coefficients and disappearance rates of the majority of amino acids in four sections of the small intestine were influenced by dietary protein source (P < 0.01) and blood meal had the fastest protein digestion rate. In general, jejunal amino acid and protein digestibilities were more variable in comparison to ileal digestibilities, and the differences in protein and amino acid disappearance rates were more pronounced between types of feedstuffs than sources of similar feedstuffs.
Culex (Culex) pipiens Linnacus (Diphtera: Culicidae): concepts, type designations, and description

The history of the concept of Culex pipien.s Linnaeus is reviewed. An illustration of a syntype published by Reaumur is designated as the lectotype of pipiens. A lectotype is also designated for Culex bifhcatus Linnaeus, which is stabilized as a synonym of pipiens. A neotype for pipiens is designated in place of the non-extant lectotype-specimen. The adult, pupal, and larval stages of the neotype are described and illustrated. Sexual differences are described and ilIus- trated for the alloneotype. A description of diagnostic and variable characters is provided for each life stage of the species. The present system of naming organisms originated with the work of Carolus Linnaeus, and the 10th edition of his Systema Naturae (1758) is the designated
Attitude Consensus of Heterogeneous Uncertain Flexible Spacecrafts with Disturbance Compensation

This paper studies the attitude consensus problem for heterogeneous flexible spacecrafts with parametric uncertainties and external disturbances under directed communication topologies. Associated with quaternion-based representations, we establish a distributed attitude error system, formulating the smooth controller design problem this paper concerned. Based on that error system, attitude consensus with uncertainties and external disturbances compensation are achieved by high-gain based controller design technology and internal model approach respectively.
Stacked Ge Nanosheet GAAFETs Fabrication and Strain Effects Measurement

Horizontally stacked Ge nanosheet GAA FETs are demonstrated, although Si, SiGe and GeSn stacked nanosheet FETs have been reported. The problem for formation of stacked pure-Ge nanostructures is that the Ge material is weaker than other IV group materials, and securing Ge material by selective etching away other IV group materials is difficult. In this work, we intentionally grow large mismatch Ge/Si multilayers rather than Ge/GeSi multilayers as the starting material, because the large difference of material properties between Ge/Si is beneficial to the following selective etching process. In order to avoid island growth, Ge/Si multilayers must be grown at a low temperature. For selective etching, we found that, at a proper temperature and with assistance of megasonic agitation, the Si layers can be easily etched away over Ge layers with good selectivity by TMAH solution. Additionally we found the dislocations in suspended Ge sheets are more easily to remove than the case that Ge layers are still tied with Si layers. Finally, the devices with gate length of 80nm were fabricated, and current of over $1650\mu A/\mu m$ (per width of channel footprint) was achieved.
IRBFL: An Information Retrieval Based Fault Localization Approach

Identifying the location of faults in real-world programs is one of the most costly processes during software debugging. In order to reduce debugging effort, many fault localization techniques have been proposed. One of the most widely studied technique is called Spectrum-based fault localization (SBFL), which uses the coverage information and execution results of test cases to do fault localization. Most SBFL techniques only consider the binary coverage information and ignore the execution frequency, so their fault localization accuracy is limited, especially when faults occur in the iteration entities or loop bodies. In this paper, we propose IRBFL, a novel fault localization technique based on information retrieval to extract information from execution frequencies of program entities. IRBFL uses mutation analysis to reduce the low suspicious classes, and then it adopts information retrieval techniques to calculate the suspiciousness value. We evaluate IRBFL on 205 real-world faults from 5 programs in Defects4J benchmark. The experimental results show that our proposed method outperforms the other five state-of-the-art SBFL techniques. More specifically, no matter in single-fault or multi-fault programs, IRBFL can identify 2 to 3 times more faulty methods than the other five SBFL techniques when checking the top 1 method. More empirical results in terms of other metrics, including acc@3, acc@5, EXAM, MRR, and MAP, also indicate that IRBFL technique is better than the other five SBFL techniques.
Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.
Proposal of an Efficient Blind Search Utilizing the Rendezvous of Random Walk Agents

A blind search in a network is used to discover a target node without detailed knowledge on the network. Because of its simplicity and the robust against network uncertainty, the blind search has been widely utilized by diverse applications in different types of networks (e.g., unstructured P2P (Peer-to-Peer) networks, ICNs (Information Centric Networks), mobile ad-hoc networks, and social networks). One of the major drawbacks of the blind search is its inefficiency; i.e., a large number of message exchanges is unavoidable for shortening the search time. In this paper, we propose an efficient blind search method utilizing the rendezvous of multiple random walkers, whose transition probabilities are adjusted based on our analysis results. Through simulation experiments, we show that the performance of the proposed search method is comparable with the flooding, which is the fastest but the least efficient method among blind search methods, and that it requires much smaller message exchanges than the flooding. We also show that the proposed search method works more effectively in scale-free networks than in non-scale-free networks.
DSLE: A Smart Platform for Designing Data Science Competitions

During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.
Warpage simulation and optimization of panel level fan-out package in post molding cure

Nowadays, the panel level fan-out packaging is gradually entering the advanced packaging market owning to the advantages of low cost, high cost performance and high throughput. Due to the mismatch of coefficient of thermal expansion(CTE) between the various packaging materials and the cure shrinkage of Epoxy Molding Compound (EMC), Warpage and cracking are observed and became main problems in further applications of fan-out packaging. In this article an equal-ratio reduction model in the form of "EMC-chip-temporary bonding layer-carrier" is established to understand the correlation between warpage and stress in panel level fan-out package. The finite element method(FEM) is used to study the influences of the packaging structure (including the thickness of each material and packaging ratio) , packaging process and material properties(Young's modulus and CTE) on warpage and stress. Based on the simulation results, it is found that the thickness of the chip, EMC and carrier has a strong influence on the packaging warpage distribution compared to the Young's modulus of EMC. During the post-curing stage from 175°C to 25°C, the chip thickness increased from 0.1mm to 0.3mm, and the warpage value decreased by about 15.6%. When the EMC thickness increased from 0.2mm to 0.4mm, the warping value increased by 10.3%. When the carrier thickness increased from 0.4mm to 0.8mm, the warping value decreased by about 43.3%. Increasing the packaging ratio can also effectively reduce the warpage value. The results are in effective range of (in according with) general experimental observation. The methods of this study can provide an approach to predict, control and design the warping behavior of fan-out packaging based on packaging material and structure.
Flexible and compact EBG structure design for Multi-band applications

A novel EBG structure is proposed. The proposed EBG (denote as DAS-EBG) structure is compact and flexible. The proposed EBG structure consists of a mushroom-type EBG with four symmetrical U-shaped slots etched on it, which is compact and flexible. Due to the change of surface resistance of multiple U-shaped slots, DAS-EBG got a width reduces of 33.75% and height reduces of 20%. By changing the size of the U-shaped slots, the number and size of DAS-EBG surface resonance points can be flexibly changed. The proposed EBG structure can be used in future broadband and multi-frequency applications.
Metal Organic Framework Derived MnO2-Carbon Nanotubes for Efficient Oxygen Reduction Reaction and Arsenic Removal from Contaminated Water

Even though manganese oxides are attractive materials for batteries, super-capacitors and electro-catalysts for oxygen reduction reactions, in most practical applications MnO2 needs to be hybridized with conductive carbon nano-structures to overcome its inherent poor electrical conductivity. In this manuscript we report microwave-assisted synthesis of MnO2 embedded carbon nanotubes (MnO2@CNT) from Mn-H3BTC (benzene-1,3,5-carboxylic acid) metal organic frameworks (MOF) precursors. Using graphene oxide as microwave susceptible surface, MnO2 nano-particles embedded in three dimensional reduced graphene oxide (rGO) -CNT frameworks (MnO2@CNT-rGO) were synthesized which when applied as electro-catalysts in oxygen reduction reaction (ORR) demonstrated comparable half-wave potential to commercial Pt/C, better stability, and excellent immunity to methanol crossover effect in alkaline media. When carbon fiber (CF) was used as substrate, three-dimensional MnO2@CNT-CF were obtained whose utility as effective adsorbents for arsenic removal from contaminated waters is demonstrated.
An enhanced backoff strategy for fair channel access in WBAN-based health monitoring systems

In this paper, we consider an IEEE 802.15. 4-based Wireless Body Area Network (WBAN), where different biomedical sensors are distributed on a human body and have to send the measured data to a coordinator node. Focusing on the Slotted Carrier Sense Multiple Access with Collision Avoidance algorithm (Slotted CSMA/CA) defined by the IEEE 802.15. 4 norm in the beacon-enabled mode, we propose an enhanced backoff strategy to provide nodes an equitable access to the communication medium. Then we analyze its performance over realistic requirements of the considered sensors using the latest version of Castalia Simulator. The obtained results reveal the efficiency of our proposal as compared to the traditional profile of the norm in terms of reliability, timeliness and throughput.
Regeneration of gross molecular body regions in planaria: from molecules to organs

For many organisms, the establishment and subsequent maintenance of anteroposterior polarity is governed by gene expression in several molecular regions. We have identified two novel regionally expressed molecules, TCEN49 and TNEX59, that may be involved in such processes. The analysis of tcen49 mRNA expression and TCEN49 protein localisation in a variety of physiological conditions, the specific arrest of anterior regeneration by exogenous retinoic acid administration, the functional analysis of tcen49 by RNA interference, and the structural similarities between TCEN49 and TNEX59, all suggest that planarian molecular regions reflect deeper morphological, physiological and functional compartmentalisation.
Models for adaptive feeding and population dynamics in plankton

Traditionally, differential-equation models for population dynamics have considered organisms as “fixed” entities in terms of their behaviour and characteristics. However, there have been many observations of adaptivity in organisms, both at the level of behaviour and as an evolutionary change of traits, in response to the environmental conditions. Taking such adaptiveness into account alters the qualitative dynamics of traditional models and is an important factor to be included, for example, when developing reliable model predictions under changing environmental conditions. In this thesis, we consider piecewise-smooth and smooth dynamical systems to represent adaptive change in a 1 predator-2 prey system. First, we derive a novel piecewise-smooth dynamical system for a predator switching between its preferred and alternative prey type in response to prey abundance. We consider a linear ecological trade-off and discover a novel bifurcation as we change the slope of the tradeoff. Second, we reformulate the piecewise-smooth system as two novel 1 predator-2 prey smooth dynamical systems. As opposed to the piecewise-smooth system that includes a discontinuity in the vector fields and assumes that a predator switches its feeding strategy instantaneously, we relax this assumption in these systems and consider continuous change in a predator trait. We use plankton as our reference organism because they serve as an important model system. We compare the model simulations with data from Lake Constance on the German-Swiss-Austrian border and suggest possible mechanistic explanations for cycles in plankton concentrations in spring.