Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Sesame!: A Look at Personal Assistants

Software agents, or softbots, are supposedly intelligent programs that assist the user in performing repetitive, boring, and time-consuming tasks. An overview of software agents, with special attention focused on the so-called personal assistants, is presented as an introduction. Following that, an in-depth look at one of the first commercially available intelligent agents Open Sesame! , a personal assistant for managing the Macintosh desktop environment, is presented. The existing intelligent agent paradigms, we argue, break down when it comes to actively helping the end user because of flaws inherent in the current approaches to intelligent agent design. To that end, we discuss the reasons why this is, particularly with respect to Open Sesame! , and promote the notion of situatedness to be taken into account more seriously for developing software agents that actively support the user for the duration of the task at hand. As an example for a more situated approach to personal assistant design, a quick overview of a project to develop a personal assistant for news filtering is discussed.
Video vehicle detection through multiple background-based features and statistical learning

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
Metropolitan-Scale Location Estimation Using FM Radio with Analysis of Measurements

This study presents a metropolitan-scale location system using FM radio. FM is a wider-covered and longer-lived infrastructure and originally designed to listen for broadcasting information. Although FM towers have existed for a long time and recent mobile devices have already contained a low-cost FM receiving function, using FM signal strength for positioning has not been well explored. Our system is deployed by collecting realistic FM radio in the campus of National Taiwan University. The experimental results show that the proposed FM location system not only satisfies the Federal Communications Commission (FCC) requirement but also provides a comparable performance to GSM based solution. Moreover, an additional contribution of this work is to examine the correlation between different wireless technologies and positioning accuracy. We completely analyze the radio measurements of FM and GSM from three perspectives, including temporal variation, spatial separation and spectrum utilization.
Tree Decomposition of Multiclass Problems

Several popular machine learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary subproblems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.
Evaluating Scalability of Multi-threaded Applications on a Many-core Platform

Multicore processors have been effective in scaling application performance by dividing computation among multiple threads running in parallel. However, application performance does not necessarily improve as more cores are added. Application performance can be limited due to multiple bottlenecks including contention for shared resources such as caches and memory. In this paper, we perform a scalability analysis of parallel applications on a 64-threaded Intel Nehalem-EX based system. We find that applications which scale well on small number of cores, exhibit poor scalability on large number of cores. Using hardware performance counters, we show that many performance limited applications are limited by memory bandwidth on manycore platforms and exhibit improved scalability when provisioned with higher memory bandwidth. By regulating the number of threads used and applying dynamic voltage and frequency scaling for memory bandwidth limited benchmarks, significant energy savings can be achieved.
Precise Chandra Localization of the Soft Gamma-Ray Repeater SGR 1806–20

We present observations of the soft γ-ray repeater SGR 1806-20 taken with the Chandra X-Ray Observatory. We identify the X-ray counterpart of SGR 1806-20 based on detection of 7.5 s pulsations. Using three unrelated X-ray sources (and USNO stars) as position references, we are able to determine that the SGR is at α2000 = 18h08m39. ˢ32 and δ2000 = -20°24'39. ″5, with rms uncertainty of 0. ″3 in each coordinate. We find that SGR 1806-20 is located within the 1 σ error region determined by Interplanetary Network data and is 14'' ± 0. ″5 distant from the nonthermal core of SNR G10.0-0.3, excluding SGR 1806-20 as the origin of the core. We see evidence of a significant deviation of the spin-down of SGR 1806-20 from its long-term trend, demonstrating erratic spin-down behavior in this source similar to that seen in other SGRs. Finally, we show that there is a broad X-ray halo surrounding SGR 1806-20 out to radii ~1' due to scattering in the interstellar medium.
Gait Exploration of Sub-2 g Robots Using Magnetic Actuation

This work describes a method to study terrestrial robot locomotion at small scales (robot mass <; 2 g) and is inspired by incredible feats of locomotion by natural organisms like insects at these same scales. 3-D printing was used to fabricate lightweight robots and magnets were embedded in the robot legs. An external magnetic field was then applied to actuate the legs and move the robot. To illustrate the studies that this method enables, a number of different quadrapedal gaits (e.g., pronking, trotting, bounding, and waddling) are demonstrated by changing the orientation of the magnets in the robots' legs, and the robot is tested at a variety of speeds (up to 3.9 body lengths per second) and over multiple terrains, with random height variations up to 8.3 mm (1.47 times hip height). These results demonstrate the versatility of magnetic actuation to study terrestrial locomotion at small scales; a single control signal can produce a variety of gait patterns.
Building a bayesian factor tree from examples

A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example.
Gesture based human - Multi-robot swarm interaction and its application to an interactive display

A taxonomy for gesture-based interaction between a human and a group (swarm) of robots is described. Methods are classified into two categories. First, free-form interaction, where the robots are unconstrained in position and motion and the user can use deictic gestures to select subsets of robots and assign target goals and trajectories. Second, shape-constrained interaction, where the robots are in a configuration shape that can be modified by the user. In the later, the user controls a subset of meaningful degrees of freedom defining the overall shape instead of each robot directly. A multi-robot interactive display is described where a depth sensor is used to recognize human gesture, determining the commands sent to a group comprising tens of robots. Experimental results with a preliminary user study show the usability of the system.
On perturbations of unitary minimal models by boundary condition changing operators

In this note we consider boundary perturbations in the A-Series unitary minimal models by r,r+2 fields on superpositions of boundaries. In particular, we consider perturbations by boundary condition changing operators. Within conformal perturbation theory we explicitly map out the space of perturbative renormalisation group flows for the example 13 and find that this sheds light on more general r,r+2 perturbations. Finally, inspired by a lattice realisation we find a simple diagrammatic representation for the space of flows from a single Cardy boundary condition.
Global analysis of p53-regulated transcription identifies its direct targets and unexpected regulatory mechanisms

The p53 transcription factor is a potent suppressor of tumor growth. We report here an analysis of its direct transcriptional program using Global Run-On sequencing (GRO-seq). Shortly after MDM2 inhibition by Nutlin-3, low levels of p53 rapidly activate ∼200 genes, most of them not previously established as direct targets. This immediate response involves all canonical p53 effector pathways, including apoptosis. Comparative global analysis of RNA synthesis vs steady state levels revealed that microarray profiling fails to identify low abundance transcripts directly activated by p53. Interestingly, p53 represses a subset of its activation targets before MDM2 inhibition. GRO-seq uncovered a plethora of gene-specific regulatory features affecting key survival and apoptotic genes within the p53 network. p53 regulates hundreds of enhancer-derived RNAs. Strikingly, direct p53 targets harbor pre-activated enhancers highly transcribed in p53 null cells. Altogether, these results enable the study of many uncharacterized p53 target genes and unexpected regulatory mechanisms. DOI: http://dx.doi. org/10.7554/eLife.02200. 001
Sparse support vector machines by kernel discriminant analysis

We discuss sparse support vector machines (SVMs) by select- ing the linearly independent data in the empirical feature space. First we select training data that maximally separate two classes in the empirical feature space. As a selection criterion we use linear discriminant analysis in the empirical feature space and select training data by forward selec- tion. Then the SVM is trained in the empirical feature space spanned by the selected training data. We evaluate our method by computer experi- ments and show that our method can realize sparse SVMs with comparable generalization performance with that of regular SVMs.
TSG-6 Inhibits Neutrophil Migration via Direct Interaction with the Chemokine CXCL8

TNF-stimulated gene/protein-6 (TSG-6) is expressed by many different cell types in response to proinflammatory cytokines and plays an important role in the protection of tissues from the damaging consequences of acute inflammation. Recently, TSG-6 was identified as being largely responsible for the beneficial effects of multipotent mesenchymal stem cells, for example in the treatment of animal models of myocardial infarction and corneal injury/allogenic transplant. The protective effect of TSG-6 is due in part to its inhibition of neutrophil migration, but the mechanisms underlying this activity remain unknown. In this study, we have shown that TSG-6 inhibits chemokine-stimulated transendothelial migration of neutrophils via a direct interaction (KD, ∼25 nM) between TSG-6 and the glycosaminoglycan binding site of CXCL8, which antagonizes the association of CXCL8 with heparin. Furthermore, we found that TSG-6 impairs the binding of CXCL8 to cell surface glycosaminoglycans and the transport of CXCL8 across an endothelial cell monolayer. In vivo this could limit the formation of haptotactic gradients on endothelial heparan sulfate proteoglycans and, hence, integrin-mediated tight adhesion and migration. We further observed that TSG-6 suppresses CXCL8-mediated chemotaxis of neutrophils; this lower potency effect might be important at sites where there is high local expression of TSG-6. Thus, we have identified TSG-6 as a CXCL8-binding protein, making it, to our knowledge, the first soluble mammalian chemokine-binding protein to be described to date. We have also revealed a potential mechanism whereby TSG-6 mediates its anti-inflammatory and protective effects. This could inform the development of new treatments for inflammation in the context of disease or following transplantation.
SURVEY ON FACE IMAGE RETRIEVAL

Face image retrieval, a technique which searches a set of faces from the large scale database based on user's query. It is an enabling technology used in many recent application such as face verification, crime investigation etc., however it is still a challenging problem because of similarities between images, irrelevant attributes and low level features like pose, texture, and color. This paper deals with study on different methodology used in retrieval system to resolve above issues.
Transformation during Leukemic NUP 98-HOXD 13 Collaborate with Retroviral Insertional Mutagenesis Identifies Genes

The t(2;11)(q31;p15) chromosomal translocation results in a fusion between the NUP98 and HOXD13 genes and has been observed in patients with myelodysplastic syndrome (MDS) or acute myelogenous leukemia. We previously showed that expression of the NUP98-HOXD13 (NHD13) fusion gene in transgenic mice results in an invariably fatal MDS; approximately one third of mice die due to complications of severe pancytopenia, and about two thirds progress to a fatal acute leukemia. In the present study, we used retroviral insertional mutagenesis to identify genes that might collaborate with NHD13 as the MDS transformed to an acute leukemia. Newborn NHD13 transgenic mice and littermate controls were infected with the MOL4070LTR retrovirus. The onset of leukemia was accelerated, suggesting a synergistic effect between the NHD13 transgene and the genes neighboring retroviral insertion events. We identified numerous common insertion sites located near protein-coding genes and confirmed dysregulation of a subset of these by expression analyses. Among these genes were Meis1, a known collaborator of HOX and NUP98-HOX fusion genes, and Mn1 , a transcriptional coactivator involved in human leukemia through fusion with the TEL gene. Other putative collaborators included Gata2, Erg, and Epor . Of note, we identified a common insertion site that was >100 kb from the nearest coding gene, but within 20 kb of the miR29a/miR29b1 microRNA locus. Both of these miRNA were up-regulated, demonstrating that retroviral insertional mutagenesis can target miRNA loci as well as protein-coding loci. Our data provide new insights into NHD13-mediated leukemogenesis as well as retroviral insertional mutagenesis mechanisms. [Cancer Res 2007;67(11):5148–55]
Autonomous Vacuum Cleaners Must Be Bayesian

Due to limited power supplies, finite dust storage, poor location sensing, and random failures caused by sucking up numerous small objects, autonomous vacuum cleaners require accurate and timely information about environmental parameters and the state of internal components. The best methods for providing such estimates are Bayesian. The recent advent of compact, efficient probabilistic representations has made the implementation of these methods not only more tractable, but has also extended the range and scope of architectures in which they can be implemented. We explore the anomalies of the vacuuming domain and illustrate how Bayesian techniques can be applied to reduce their effects on agent performance.
Age Encounters: Exploring Age and Intergenerational Perceptions

ABSTRACT Age Encounters was a video documentary project wherein 10 younger people and 10 older people were asked questions about age; ageism; and their perception of the other generation, including challenges the other group might face and whether they could identify possibilities for solidarity between the two generations. It was found that most intergenerational relationships are respectful and occur within the family. Any lack of generational understanding appeared due to a lack of relationships outside of the family, leading to a reliance on assumptions and recognized stereotypes. When opportunities for intergenerational relationships and shared spaces were available, the stereotypes were not applied.
Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks

Problem statement: The study presented an efficient Iris recognition system. Approach: The design used the discrete cosine transform for feature extraction and artificial neural networks for classification. The iris images used in this system were obtained from the CASIA database. Results: A robust system for iris recognition was developed. Conclusion: An iris recognition system that produces very low error rates was successfully designed.
Tensor Fold-in Algorithms for Social Tagging Prediction

Social tagging predictions involve the co occurrence of users, items and tags. The tremendous growth of users require the recommender system to produce tag recommendations for millions of users and items at any minute. The triplets of users, items and tags are most naturally described by a 3D tensor, and tensor decomposition-based algorithms can produce high quality recommendations. However, each day, thousands of new users are added to the system and the decompositions must be updated daily in a online fashion. In this paper, we provide analysis of the new user problem, and present fold-in algorithms for Tucker, Para Fac, and Low-order tensor decompositions. We show that these algorithm can very efficiently compute the needed decompositions. We evaluate the fold-in algorithms experimentally on several datasets and the results demonstrate the effectiveness of these algorithms.
Offline Cursive Character Recognition: A state of the art comparison

Recent research has demonstrated the superiority of SVM-based approaches for offline cursive character recognition. In particular, Camastra's 2007 study showed SVM to be better than alternative LVQ and MLP approaches on the large C-Cube data set. Subsequent work has applied hierarchical vector quantization (HVQ) with temporal pooling to the same data set, improving on LVQ and MLP but still not reaching SVM recognition rates. In the current paper, we revisit Camastra's SVM study in order to explore the effects of using an alternative modified direction feature (MDF) vector representation, and to compare the performance of a RBF-based approach against both SVM and HVQ. Our results show that SVMs still have the better performance, but that much depends on the feature sets employed. Surprisingly, the use of more sophisticated MDF feature vectors produced the poorest results on this data set despite their success on signature verification problems.
On Polymorphic Logical Gates in Subexcitable Chemical Medium

In a subexcitable light-sensitive Belousov–Zhabotinsky (BZ) chemical medium an asymmetric disturbance causes the formation of localized traveling wave-fragments. Under the right conditions these wave-fragments can conserve their shape and velocity vectors for extended time periods. The size and life span of a fragment depend on the illumination level of the medium. When two or more wave-fragments collide they annihilate or merge into a new wave-fragment. In computer simulations based on the Oregonator model, we demonstrate that the outcomes of inter-fragment collisions can be controlled by varying the illumination level applied to the medium. We interpret these wave-fragments as values of Boolean variables and design collision-based polymorphic logical gates. The gate implements operation XNOR for low illumination, and it acts as NOR gate for high illumination. As a NOR gate is a universal gate, then we are able to demonstrate that a simulated light sensitive BZ medium exhibits computational universality.
Localized cerebral blood flow response at submillimeter columnar resolution

Functional magnetic resonance imaging (fMRI) has been widely used for imaging brain functions. However, the extent of the fMRI hemodynamic response around the active sites, at submillimeter resolution, remains poorly understood and controversial. With the use of perfusion-based fMRI, we evaluated the hemodynamic response in the cat visual cortex after orientation-specific stimuli. Activation maps obtained by using cerebral blood flow fMRI measurements were predominantly devoid of large draining vein contamination and reproducible at columnar resolution. Stimulus-specific cerebral blood flow responses were spatially localized to individual cortical columns, and columnar layouts were resolved. The periodic spacing of orientation columnar structures was estimated to be 1.1 ± 0.2 mm (n = 14 orientations, five animals), consistent with previous findings. The estimated cerebral blood flow response at full width at half-maximum was 470 μm under single-stimulus conditions without differential subtraction. These results suggest that hemodynamic-based fMRI can indeed be used to map individual functional columns if large-vessel contributions can be minimized or eliminated.
Expectation along the beat: a Use Case for Music Expectation Models

We present a system to produce expectations based on the observation of a rhythmic music signals at a constant tempo. The algorithms we use are causal, in order be fit closer to cognitive constraints and allow a future realtime implementation. In a first step, an acoustic front-end based on the aubio library extracts onsets and beats from the incoming signal. The extracted onsets are then encoded in a symbolic way using an unsupervised scheme: each hit is assigned a timbre cluster based on its timbre features, while its inter-onset interval regarding the previous hit is computed as a proportion of the extracted tempo period and assigned an inter-onset interval cluster. In a later step, the representation of each hit is sent to an expectation module, which learns the statistics of the symbolic sequence. Hence, at each musical hit, the system produces both what and when expectations regarding the next musical hit. For evaluating our system, we consider a weighted average F-measure, that takes into account the uncertainty associated with the unsupervised encoding of the musical sequence. We then present a preliminary experiment involving generated musical material and propose a roadmap in the context of this novel application field.
RazorII: In Situ Error Detection and Correction for PVT and SER Tolerance

Traditional adaptive methods that compensate for PVT variations need safety margins and cannot respond to rapid environmental changes. In this paper, we present a design (RazorII) which implements a flip-flop with in situ detection and architectural correction of variation-induced delay errors. Error detection is based on flagging spurious transitions in the state-holding latch node. The RazorII flip-flop naturally detects logic and register SER. We implement a 64-bit processor in 0.13 mum technology which uses RazorII for SER tolerance and dynamic supply adaptation. RazorII based DVS allows elimination of safety margins and operation at the point of first failure of the processor. We tested and measured 32 different dies and obtained 33% energy savings over traditional DVS using RazorII for supply voltage control. We demonstrate SER tolerance on the RazorII processor through radiation experiments.