Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era

Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an understanding will yield practical techniques that support learning and retention. Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that oﬀers quantitative, individualized guidance. Our approach synthesizes theory-driven and data-driven methodologies. Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas machine-learning techniques use observations from a population to make inferences about individuals. We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for speciﬁc material. We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In an ambitious year-long intervention in a middle-school foreign language course, we demonstrate the value of systematic review on long-term educational outcomes, but more speciﬁcally, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual’s study history and past performance.
Image based Cryptography from a distance

An information is a message which is received and understood. Information can be sent one person to another over a long range but the process of sending information must be done in a secure way especially in case of a private message. Mathematicians and Engineers have historically relied on different algorithmic techniques to secure messages and signals. Cryptography, to most people, is concerned with keeping communications private. Indeed, the protection of sensitive communications has been the emphasis of cryptography throughout much of its history. Sometimes it is safer to send a message using an image and thus cryptography can also be done using images during an emergency. The need to extract information from images and interpret their contents has been one of the driving factors in the development of image processing and cryptography during the past decades. In this paper, a simple cryptographic method was used to decode a message which was in an image and it was done using a popular computational software.
Bacterial Communities in Malagasy Soils with Differing Levels of Disturbance Affecting Botanical Diversity

Madagascar is well-known for the exceptional biodiversity of its macro-flora and fauna, but the biodiversity of Malagasy microbial communities remains relatively unexplored. Understanding patterns of bacterial diversity in soil and their correlations with above-ground botanical diversity could influence conservation planning as well as sampling strategies to maximize access to bacterially derived natural products. We present the first detailed description of Malagasy soil bacterial communities from a targeted 16S rRNA gene survey of greater than 290,000 sequences generated using 454 pyrosequencing. Two sampling plots in each of three forest conservation areas were established to represent different levels of disturbance resulting from human impact through agriculture and selective exploitation of trees, as well as from natural impacts of cyclones. In parallel, we performed an in-depth characterization of the total vascular plant morphospecies richness within each plot. The plots representing different levels of disturbance within each forest did not differ significantly in bacterial diversity or richness. Changes in bacterial community composition were largest between forests rather than between different levels of impact within a forest. The largest difference in bacterial community composition with disturbance was observed at the Vohibe forest conservation area, and this difference was correlated with changes in both vascular plant richness and soil pH. These results provide the first survey of Malagasy soil bacterial diversity and establish a baseline of botanical diversity within important conservation areas.
THE ECOLOGICAL EFFECTS OF CATTLE GRAZING ON REPTILES AND SMALL MAMMALS IN A SAN JOAQUIN VALLEY GRASSLAND

Livestock grazing is a common and extensive land use practice in the United States occurring in a wide range of habitat types. As such, livestock grazing has the potential to alter ecosystem structure, function and community composition. The primary component (Chapter 1) of this thesis examined the effects of cattle grazing in a San Joaquin Valley grassland on two target taxa: reptiles and small mammals. The study took place on the Chimineas Unit of the Carrizo Ecological Reserve, San Luis Obispo County, California during Fall 2009 and Spring 2010. These taxa were sampled on matched pairs of two grazed and two ungrazed sites. Live trapping methods appropriate to sampling reptiles (coverboards and pit fall traps) and small mammals (Sherman live traps) were used to determine species richness and abundance of these taxa. Uta stansburiana (side-blotched lizard) abundances were greatest on Ungrazed1 (Fall: 1.75±0.49 captures/night, Spring: 3.58±0.35 captures/night). In addition, Chaetodipus californicus (Spiny pocket mouse) abundance was also greatest on Ungrazed1. Population estimates generated by Program CAPTURE suggest C. californicus could be 6.27 times more abundant on Ungrazed1 than Grazed1. However, sample size issues and site level effects confounded and made it difficult to determine significant differences between the grazed and ungrazed treatments for both reptile and small mammal taxa. Habitat structure at these sites was also evaluated including small mammal burrow abundance and vegetation structure. Again, differences between the grazed and ungrazed pasture could not be discerned because of site level effects occurring among matched pairs. As such, this thesis illustrates the difficulty in studying grazing and its potential effects on biotic systems because an array of variables can make unclear the differences between grazed and ungrazed areas. As a follow up study (Chapter 2) I used logistic regression to model U. stansburiana presence to examine possible sources of variation observed at trapping array locations utilized in the main study (Chapter 1). Shrubs exhibited a quasi-complete separation of data points and the three best models included: 1) Bare soil cover (AICc = 28.12), 2) Holes (AICc = 29.76), and 3) Bare soil cover + Holes (AICc = 29.90). Shrubs, bare soil cover and small mammal burrow density were all positively associated with U. stansburiana presence at array locations. Although species have general habitat requirements based upon their ecology and evolution, variations in habitat utilization exists depending upon the resources present at a specific location. Quantifying basic ecological information on a …
Pedestrian Recognition in Aerial Video Using Saliency and Multi-Features

 Abstract—Pedestrian recognition in aerial video is a challenge problem for the problem of low resolution, camera movement and target's blurred detail in aerial video. This paper proposes weighted region matching algorithm with Kalman filter, Multi-features fusion model and saliency segmentation (KMFS-WRM) to detect and recognize pedestrian. The KMFS-WRM algorithm first uses Kalman filter algorithm to mark candidate's region, which can avoid the problem of selecting candidates under supervision. Then we proposed the fusion algorithm of multi-feature, including HOG, LBP and SIFT features, namely HLS model to detect the pedestrian in aerial video. Our proposed detection method is robust for whether the camera is moving. And instructing human percept and concept, we segment the pedestrians in marked region using Context-Aware saliency detection algorithm that proposed by Goferman et al. and revised the segmentation results by HST model (Head Shoulder and Torso) and AAM model (Active Appearance Model) to obtain the candidates set. Last the matching of voter and candidates set using weighted region matching algorithm. Experimental results in complex aerial video demonstrated that our KMFS-WRM algorithm not only cuts down calculated complexity, but also improves adaptive and real-time ability. Moreover proposed method outperforms recent state-of-the-art methods.
Human Action Recognition Based on DMMs, HOGs and Contourlet Transform

This paper proposes a framework for recognizing human actions from depth video sequences by designing a novel feature descriptor based on Depth Motion Maps (DMMs), Contour let Transform (CT) and Histogram of Oriented Gradients (HOGs). First, CT is implemented on the generated DMMs of a depth video sequence and then HOGs are computed for each contour let sub-band. Finally, the concatenation of these HOG features is used as a feature descriptor for the depth video sequence. With this new feature descriptor, the l2-regularized collaborative representation classifier is utilized to recognize human actions. The experimental results on Microsoft Research Action3D dataset demonstrate that our proposed method can achieve the state-of-the-art performance for human activity recognition due to the precise feature extraction of contour let transform on the DMMs.
Clinical Outcome of IM-Guided Total Knee Arthroplasty with Inappropriate Femoral Resection in Coronal Plane

Purpose The purpose of this study was to evaluate clinical results and accuracy of femoral cutting in the coronal plane in total knee arthroplasty (TKA) using a fixed length intramedullary guide. Materials and Methods From 2005 to 2008, 101 patients (154 knees) underwent TKA (NexGen LPS implant). The minimal follow-up period was 3 years (mean, 4.4 years). The patients were divided into two groups (group 1, 94≤α angle<98; group 2, 94>α, 98≤α). Mechanical axis deviation (MAD), patellar tilting angle (PTA), Knee Society Knee Score (KSKS), and Knee Society Function Score (KSFS) were measured in both groups. Results There were 120 knees in group 1 and 34 knees in group 2. There was no significant intergroup difference in the postoperative MAD (group 1, 1.59°; group 2, 1.91°). The number of outliers with ≥2° MAD was 65 in group 1 and 24 in group 2. The mean PTA, KSKS, and KSFS were 10.17°, 96.0, and 96.6, respectively, in group 1 and 11.58°, 84.5, and 85.5, respectively, in group 2. Conclusions The percentage of coronal alignment outliers was relatively high (34 in 154 cases, 22%) after TKA using a fixed length intramedullary guide. However, there was no statistically significant intergroup difference in clinical results (KSKS, p=0.67; KSFS, p=0.56).
A domain ontology building process based on principles of social web

In this paper, we present a domain ontology building process based on principles of social web. The proposed process takes into account the deficiencies that have marked the development of ontologies, mainly those related to the negligence of user, the sustainability of knowledge and the problem of consensus among different users. Our focus concentrates on introducing users in the development process in order to address these problems. We model this initiative by the principles of social web and this by exploiting, in particular, wikis and content sharing to ensure sustainability, availability and sharing of knowledge between different collaborators, and the use of tags and folksonomies to converge to a consensus vocabulary.
Situation-driven control of a robotic wheelchair to follow a caregiver

Recently, several robotic/intelligent wheelchairs that have autonomous functions to get to some goals or user-friendly interfaces, have been proposed. Although it is desirable for wheelchair users to go out alone, caregivers often accompany them. Therefore it is important to consider reducing caregivers' load and support their activities. Hence, we have proposed a robotic wheelchair that can move with a caregiver side by side by observing his/her behaviors such as body position and orientation. This way of motion enables easy communication between the wheelchair user and the caregiver. However, caregivers cannot always move with the wheelchair side by side. For instance, they may step forward of the wheelchair to open a door, and they may step aside when pedestrians are approaching in narrow corridors. To cope with these situations we propose a new robotic wheelchair that can move with a caregiver collaboratively depending on the situation. A laser range sensor is used for observing the environment around the wheelchair. When any obstacles are found in its travelling direction, the wheelchair gives up moving with the caregiver side by side and follows him/her behind. When the caregiver stops at a door, the wheelchair waits for him/her to open the door and follows him/her while avoiding obstacles. The wheelchair returns to move side by side with the caregiver when it enters wide space. We propose a motion control method based on the potential field method, which enables the wheelchair to take an appropriate position with respect to the caregiver depending on the situation.
New Tools for Targeted Disruption of Cholinergic Synaptic Transmission in Drosophila melanogaster

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels. The α7 subtype of nAChRs is involved in neurological pathologies such as Parkinson’s disease, Alzheimer’s disease, addiction, epilepsy and autism spectrum disorders. The Drosophila melanogaster α7 (Dα7) has the closest sequence homology to the vertebrate α7 subunit and it can form homopentameric receptors just as the vertebrate counterpart. The Dα7 subunits are essential for the function of the Giant Fiber circuit, which mediates the escape response of the fly. To further characterize the receptor function, we generated different missense mutations in the Dα7 nAChR’s ligand binding domain. We characterized the effects of targeted expression of two UAS-constructs carrying a single mutation, D197A and Y195T, as well as a UAS-construct carrying a triple D77T, L117Q, I196P mutation in a Dα7 null mutant and in a wild type background. Expression of the triple mutation was able to restore the function of the circuit in Dα7 null mutants and had no disruptive effects when expressed in wild type. In contrast, both single mutations severely disrupted the synaptic transmission of Dα7-dependent but not glutamatergic or gap junction dependent synapses in wild type background, and did not or only partially rescued the synaptic defects of the null mutant. These observations are consistent with the formation of hybrid receptors, consisting of D197A or Y195T subunits and wild type Dα7 subunits, in which the binding of acetylcholine or acetylcholine-induced conformational changes of the Dα7 receptor are altered and causes inhibition of cholinergic responses. Thus targeted expression of D197A or Y195T can be used to selectively disrupt synaptic transmission of Dα7-dependent synapses in neuronal circuits. Hence, these constructs can be used as tools to study learning and memory or addiction associated behaviors by allowing the manipulation of neuronal processing in the circuits without affecting other cellular signaling.
Quantitative and Functional Characterization of the Hyper-Conserved Protein of Prochlorococcus and Marine Synechococcus

A large fraction of any bacterial genome consists of hypothetical protein-coding open reading frames (ORFs). While most of these ORFs are present only in one or a few sequenced genomes, a few are conserved, often across large phylogenetic distances. Such conservation provides clues to likely uncharacterized cellular functions that need to be elucidated. Marine cyanobacteria from the Prochlorococcus/marine Synechococcus clade are dominant bacteria in oceanic waters and are significant contributors to global primary production. A Hyper Conserved Protein (PSHCP) of unknown function is 100% conserved at the amino acid level in genomes of Prochlorococcus/marine Synechococcus, but lacks homologs outside of this clade. In this study we investigated Prochlorococcus marinus strains MED4 and MIT 9313 and Synechococcus sp. strain WH 8102 for the transcription of the PSHCP gene using RT-Q-PCR, for the presence of the protein product through quantitative immunoblotting, and for the protein's binding partners in a pull down assay. Significant transcription of the gene was detected in all strains. The PSHCP protein content varied between 8±1 fmol and 26±9 fmol per ug total protein, depending on the strain. The 50 S ribosomal protein L2, the Photosystem I protein PsaD and the Ycf48-like protein were found associated with the PSHCP protein in all strains and not appreciably or at all in control experiments. We hypothesize that PSHCP is a protein associated with the ribosome, and is possibly involved in photosystem assembly.
Kinematic and dynamic analogies between planar biped robots and the reaction mass pendulum (RMP) model

In order to simplify dynamic analysis, humanoid robots are often abstracted with various versions of the inverted pendulum model. However, most of these models do not explicitly characterize the robotpsilas rotational inertia, a critical component of its dynamics, and especially of its balance. To remedy this, we have earlier introduced the reaction mass pendulum (RMP), an extension of the inverted pendulum, which models the rotational inertia and angular momentum of a robot through its centroidal composite rigid body (CCRB) inertia. However, we presented only the kinematic mapping between a robot and its corresponding RMP. Focussing in-depth on planar mechanisms, here we derive the dynamic equations of the RMP and explicitly compute the parameters that it must possess in order to establish equivalence with planar compass gait robot. In particular, we show that, a) an angular momentum equality between the robot and RMP does not necessarily guarantee kinetic energy equality, and b) a cyclic robot gait may not result in a cyclic RMP movement. The work raises the broader question of how quantitatively similar the simpler models of humanoid robot must be in order for them to be of practical use.
An efficient control variates method for yield estimation of analog circuits based on a local model

Statistical analysis of analog circuits usually relies on the standard Monte Carlo method to estimate the yield of a circuit. However, this method is limited by a slow convergence rate which leads to a prohibitive number of simulations to reach a given accuracy. In this paper, we propose to combine the kernel-based distribution estimator with the control variates method in order to obtain an accurate yield estimation with only a few hundred simulations. With respect to the auxiliary variable needed for the control variates method, we propose a quick modeling technique based on local sensitivities.
A non-progressive model of innovation diffusion in social networks

In this paper we deal with the diffusion of innovation in social network, presenting a non progressive instance of the well known Linear Threshold model. Each individual in the social network is influenced by the behaviour of its neighbours, and at each steps it decides either to adopt, abandon or maintain the innovation by following a threshold mechanism. We assume that the innovation is incepted in the network by a seed set of individuals which are assumed to maintain the innovation independently of the state of their neighbours for a finite time. Here we describe in details the evolution of the system depending on different initial conditions, and in particular we focus on how the system dynamics are influenced by the cohesive sub-groups of the network.
The FLT3 Internal Tandem Duplication Mutation Is a Secondary Target of the Aurora B Kinase Inhibitor AZD1152-HQPA in Acute Myelogenous Leukemia Cells

Aurora kinases play an essential role in orchestrating chromosome alignment, segregation, and cytokinesis during mitotic progression and both aurora-A and B are frequently overexpressed in a variety of human malignancies. In this study, we report the effects of AZD1152-HQPA, a highly selective inhibitor of aurora-B kinase, in acute myeloid leukemia (AML) cell lines and primary samples. We show that AZD1152-HQPA inhibits the phosphorylation of Histone H3 (pHH3) on serine 10 resulting in polyploid cells, apoptosis, and loss of viability in a panel of AML cell lines. We also show that AZD1152-HQPA sensitivity in our cell lines is irrespective of p53 status and the FLT3-ITD–expressing MOLM-13 and MV4-11 cell lines are particularly sensitive to AZD1152-HQPA. Internal tandem duplications (ITD) within the FLT3 tyrosine kinase receptor are found in ∼25% of AML patients and are associated with a poor prognosis. Here, we report that AZD1152-HQPA directly targets phosphorylated FLT3 along with inhibiting its downstream target phospho–signal transducer and activator of transcription 5 (STAT5) in the FLT3-ITD cell lines. We show pHH3 expression in primary AML blasts and its inhibition by AZD1152-HQPA at low doses in all of our primary samples tested. AZD1152-HQPA inhibits the clonogenic potential of primary AML samples, with FLT3-ITD samples being the most sensitive (P = 0.029). FLT3-ITD primary samples are also more sensitive to pHH3 inhibition (P = 0.022) and are particularly sensitive to pSTAT5 downregulation after treatment with AZD1152-HQPA compared with FLT3 wild-type samples (P = 0.007). We conclude that mutant FLT3 is a secondary target of AZD1152-HQPA and that FLT3-ITD primary samples are particularly sensitive to the drug. Mol Cancer Ther; 9(3); 661–72
Lightweight Internet Traffic Classification: A Subject-Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application- level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word- embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.