Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Audio Clustering Using Vector Quantization and Second Order Statistics

This paper describes an effective unsupervised speaker indexing approach. We suggest a two stage algorithm to speed-up the state-of-the-art algorithm based on the Bayesian Information Criterion (BIC). In the first stage of the merging process a computationally cheap method based on the vector quantization (VQ) is used. Then in the second stage a more computational expensive technique based on the BIC is applied. In the speaker indexing task a turning parameter or a threshold is used. We suggest an on-line procedure to define the value of a turning parameter without using development data. The results are evaluated using 10 hours of audio data.
Can Your Friends Predict Where You Will Be?

With the development of mobile device and wireless networks, user location becomes increasingly valuable in enhancing user experience, system performance and resource allocation. Location-based services have been not only an important perspective of social media, but also a significant contributor to big data analysis. Location prediction, as an interesting topic, can help improve system performance and user experience in location-based services. Existing algorithms on such prediction focus mostly on exploring regularity in users' movement history without taking advantage of the research on social networks, which can provide information on other factors such as peer influence in human mobility. In this work, the aim is to propose an enhanced location prediction model based on both users' mobility patterns and social network information and the proposed algorithm shows a significant improvement over existing ones.
An efficient bi-criteria flow channel routing algorithm for flow-based microfluidic biochips

Rapid growth in capacity makes flow-based microfluidic biochips a promising candidate for biochemical analysis because they can integrate more complex functions. However, as the number of components grows, the total length of flow channels between components must increase exponentially. Recent empirical studies show that long flow channels are vulnerable due to blocking and leakage defects. Thus, it is desirable to minimize the total length of flow channels for robustness. Also, for timing-sensitive biochemical assays, increase in the longest length of flow channel will delay the assay completion time and lead to variation of fluid, thereby affecting the correctness of outcome. The increasing number of components, including the pre-placed components, on the chip makes the flow channel routing problem even more complicated. In this paper, we propose an efficient obstacle-avoiding rectilinear Steiner minimum tree algorithm to deal with flow channel routing problem in flow-based microfluidic biochips. Based on the concept of Kruskal algorithm and formulating the considerations as a bi-criteria function, our algorithm is capable of simultaneously minimizing the total length and the longest length of flow channel.
Characterization of the hot Neptune GJ 436 b with Spitzer and ground-based observations

We present Spitzer Space Telescope infrared photometry of a secondary eclipse of the hot Neptune GJ 436 b. The observations were obtained using the 8-µm band of the InfraRed Array Camera (IRAC). The data spanning the predicted time of secondary eclipse show a clear flux decrement with the expected shape and duration. The observed eclipse depth of 0.58 mmag allows us to estimate a blackbody brightness temperature of Tp = 717 ± 35 K at 8 µm. We compare this infrared flux measurement to a model of the planetary thermal emission, and show that this model reproduces properly the observed flux decrement. The timing of the secondary eclipse confirms the non-zero orbital eccentricity of the planet, while also increasing its precision (e = 0.14 ± 0.01). Additional new spectroscopic and photometric observations allow us to estimate the rotational period of the star and to assess the potential presence of another planet.
A Servo-assisted Gel-pack Cooling Method for Newborn Infants with Hypoxic-ischemic Encephalopathy

Outcome of cord-blood transplantation from related and unrelated donors. Graft-versus-host disease in children who have received a cord-blood or bone marrow transplant from an HLA-identical sibling. Cell dose and speed of engraftment in placental/umbilical cord blood transplantation: graft progenitor cell content is a better predictor than nucleated cell quantity. Analysis of maternal and neonatal factors that influence the nucleated and CD34þ cell yield for cord blood banking. Acknowledgement We are thankful for the effort of the attending residents and all the staff nurses on the labor and delivery ward at the Department of Obstetrics and Gynecology. Introduction Consensus recommendations on the use of therapeutic hypothermia for infants born at or near term with moderate to severe hypoxic-ischemic encephalopathy (HIE), state that treatment should be consistent with protocols used in randomized clinical trials [1]. The recently published Infant Cooling Evaluation (ICE) trial [2], provides a pragmatic protocol using manually applied refrigerated gel-packs to induce and
Identification and Verification of QTL Associated with Frost Tolerance Using Linkage Mapping and GWAS in Winter Faba Bean

Frost stress is one of the abiotic stresses that causes a significant reduction in winter faba bean yield in Europe. The main objective of this work is to genetically improve frost tolerance in winter faba bean by identifying and validating QTL associated with frost tolerance to be used in marker-assisted selection (MAS). Two different genetic backgrounds were used: a biparental population (BPP) consisting of 101 inbred lines, and 189 genotypes from single seed descent (SSD) from the Gottingen Winter bean Population (GWBP). All experiments were conducted in a frost growth chamber under controlled conditions. Both populations were genotyped using the same set of 189 SNP markers. Visual scoring for frost stress symptoms was used to define frost tolerance in both populations. In addition, leaf fatty acid composition (FAC) and proline content were analyzed in BPP as physiological traits. QTL mapping (for BPP) and genome wide association studies (for GWBP) were performed to detect QTL associated with frost tolerance. High genetic variation between genotypes, and repeatability estimates, were found for all traits. QTL mapping and GWAS identified new putative QTL associated with promising frost tolerance and related traits. A set of 54 SNP markers common in both genetic backgrounds showed a high genetic diversity with polymorphic information content (PIC) ranging from 0.31 to 0.37 and gene diversity ranging from 0.39 to 0.50. This indicates that these markers may be polymorphic for many faba bean populations. Five SNP markers showed a significant marker-trait association with frost tolerance and related traits in both populations. Moreover, synteny analysis between Medicago truncatula (a model legume) and faba bean genomes was performed to identify candidate genes for these markers. Collinearity was evaluated between the faba bean genetic map constructed in this study and the faba bean consensus map, resulting in identifying possible genomic regions in faba bean which may control frost tolerance genes. The two genetic backgrounds were useful in detecting new variation for improving frost tolerance in winter faba bean. Of the five validated SNP markers, one (VF_Mt3g086600) was found to be associated with frost tolerance and FAC in both populations. This marker was also associated with winter hardiness and high yield in earlier studies. This marker is located in a gene of unknown function.
An experimental study of a museum-based, science PD programme’s impact on teachers and their students

ABSTRACT We present results of an experimental study of an urban, museum-based science teacher PD programme. A total of 125 teachers and 1676 of their students in grades 4–8 were tested at the beginning and end of the school year in which the PD programme took place. Teachers and students were assessed on subject content knowledge and attitudes towards science, along with teacher classroom behaviour. Subject content questions were mostly taken from standardised state tests and literature, with an ‘Explain:’ prompt added to some items. Teachers in the treatment group showed a 7% gain in subject content knowledge over the control group. Students of teachers in the treatment group showed a 4% gain in subject content knowledge over the control group on multiple-choice items and an 11% gain on the constructed response items. There was no overall change in science attitudes of teachers or students over the control groups but we did find differences in teachers’ reported self-efficacy and teaching anxiety levels, plus PD teachers reported doing more student-centered science teaching activities than the control group. All teachers came into the PD with high initial excitement, perhaps reflecting its context within an informal learning environment.
An Introduction to Genetic Data Analysis Using SAS / Genetics

The Human Genome Project and other recent genome research efforts have intensified the already vital relationship between the fields of genetics and statistics. One important application of the analysis of genetic data is locating genes that affect complex traits, human diseases in particular. While statistical procedures already in SAS are capable of certain analyses, the unique nature of these data demands customized tools to fully accommodate the genetic dissection of a trait or disease. SAS/Genetics offers new statistical procedures specifically tailored for genetic data stored in a commonly used format. This paper discusses these methods and illustrates the features of the software with an example.
Compiling Existential Positive Queries to Bounded-Variable Fragments

A crucial property of bounded-variable fragments of first-order logic is that they can be evaluated in polynomial time. It is therefore a useful preprocessing step to rewrite, if possible, a first-order query to a logically equivalent one with a minimum number of variables. However, it may occur that reducing the number of variables causes an increase in formula size. We investigate this trade-off for the existential-positive fragment of first-order queries, where variable minimisation is decidable in general. In particular, we study the blow-up in the formula size when compiling existential-positive queries to the bounded variable fragment of positive first-order logic. While the increase of the formula size is always at most exponential, we identify situations (based on the signature and the number of variables) where only a polynomial blow-up is needed. In all other cases, we show that an exponential lower bound on the formula size of the compiled formula that matches the general upper bound. This exponential lower bound is unconditional, and is the first unconditional lower bound for formula size with respect to the studied compilation; it is proved via establishing a novel interface with circuit complexity which may be of future interest.
DETECTION OF DDOS ATTACKS USING DATA MINING

Distributed Denial of Service (DDoS) attacks are large-scale cooperative attacks launched from a large number of compromised hosts called Zombies is a major threat to Internet services. Popular web sites such as Yahoo, CNN, and Amazon, are among the well-known victims of DDoS attacks. Large number of companies transacting online are mainly facing the considerable loss as they are being targeted to DDoS attacks. Therefore, keeping this problem in view author presents various significant areas where data mining techniques seem to be a strong candidate for detecting and preventing DDoS attack. KEY WORD: Distributed Denial of Service attack, Data mining, Zombies.
LeMo: Exploring Virtual Space for Collaborative Creativity

Shared Virtual Environments (SVEs) have been extensively researched for education, entertainment, work, and training, yet there has been limited research on the creative aspects of collaboration in SVEs. This raises questions about how to design virtual working spaces to support collaborative creativity in SVEs. In this paper, we outline an SVE named LeMo, which allows two people to create music collaboratively. Then we present a study of LeMo, in which 42 users composed music together using three different virtual working space configurations. Results indicate that (i) two types of territory and working configurations emerged during collaborative composing (ii) when made available to them, personal working spaces were extensively used, and were considered to be essential to successful collaborative music making and (iii) a publicly visible personal working space was preferable to a publicly invisible one. Based on these findings, three corresponding design implications for Shared Virtual Environments focusing on supporting collaborative creativity are given.
On Broadcasting in Wireless Sensor Networks with Irregular and Dynamic Radio Coverage

Broadcast in radio-based wireless networks has been a difficult problem. When a node broadcasts, all nodes within its radio coverage will attempt to relay the message by rebroadcasting, causing excessive radio communication in the region that leads to the broadcast storm problem. Most previous solutions assumed a perfect radio condition with a static, circular coverage. However, in real situations, radio signals are very dynamic and irregular in their coverage. This is especially true for wireless sensor networks, which usually employ low quality radio modules to reduce the cost. Assuming no location information is available, we approach the problem in this paper from two aspects: space and time. To solve the irregularity in space in radio coverage, we propose a scheme that is able to find the neighbors on the rim of the irregular radio coverage region to narrow down the choices of the forwarders. To solve the dynamic in radio coverage, we consider an on-demand, stateless strategy to reduce the overhead of maintaining the state information. Through simulation, we compare the resultant algorithm, called RIM, with Double-Covered Broadcast (DCB) [10], one of the best algorithms proposed so far. The results show that under dense deployment and irregular and dynamic radio coverage, our algorithm performs better.
An Index-Based Sensor-Grouping Mechanism for Efficient Field-Coverage Wireless Sensor Networks

This paper discusses a point-distribution index, l, which measures the normalized minimum distance between sensors. Maximizing l of a set of points causes the Delaunay triangulation graph of these points to be a net of equilateral triangles. Such a structure indicates the lowest redundancy of coverage if each point represents the center of a disc. Thus l can serve as a promising measure for solving a critical problem in field coverage: How to group a set of sensor nodes into disjoint subsets so that each subset can cover the entire field? Based on the l index, we develop an effective algorithm, MAXINE (MAXimizing-l Node-redundancy Exploiting), for the sensor- grouping problem. We evaluate the performance of MAXINE through extensive simulations and compare it with existing algorithms. The results demonstrate the effectiveness of MAXINE and verify the superiority of employing i for the sensor-grouping problem.
Phase contrast imaging using sensitivity encoding and automatic coil sensitivity estimation: a feasibility study

In Fig. 2 the ERMS is shown for the three reduced data sets using different numbers of ACS-lines. When 25 ACS-lines are used the ERMS is less than 5% for all reconstructed data sets. In Fig. 3 the difference between the measured mean phase value within ROI 2,3 and 4 and the true mean phase value is shown. With the use of 25 ACS-lines the measured mean phase difference was less than ±2,5% in all three ROI:s and for all three reduced data sets.
Can Stimuli Improve Business Model Idea Generation?: Developing Software-Based Tools for Business Model Innovation

A business model describes the mechanisms whereby a firm creates, delivers, and captures value. Following the steadily growing interest in business model innovation, software tools have shown great potential in supporting business model development and innovation. Yet, understanding the cognitive processes involved in the generation of business model ideas is an aspect of software design-knowledge that has so far been neglected. To investigate whether providing stimuli - in this case, brainstorming questions - can enhance individual creativity in this context, we conduct an exploratory experiment with over 100 participants. Our study is the first to systematically investigate the process of idea generation using a software-based business model development tool with stimuli. Our preliminary findings have the potential to support the future development of business model development tools and to refine the research design used to evaluate such tools.
Bayesian regularisation methods in a hybrid MLP-HMM system

We have applied Bayesian regularisation methods to multi-layer perceptron (MLP) training in the context of a hybrid MLP– HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the training data. Experiments were carried out on the ARPA Resource Management database.
Weak and electromagnetic interactions

SummaryThe postulate of a «local connection» in a [3] charge space leads to the introduction of three spin one fields. One of these can be identified with the electro-magnetic field and the other two can be shown to mediate all known weak interactions, thus unifying these interactions with electro-magnetism. The theory takes account of the fact that weak interactions violate parity and strangeness conservation while electromagnetic interactions do not do so.RiassuntoIl postulato di una «connessione locale» in un [3] spazio della carica conduce all’introduzione di tre campi di spin uno. Uno di questi può identificarsi col campo elettromagnetico e si può dimostrare che gli altri due sono il veicolo di tutte le interazioni deboli note, unificando così tali interazioni coll’elettromagnetismo. La teoria tien conto del fatto che, a differenza delle interazioni elettromagnetiche, le interazioni deboli violano la conservazione della parità e della stranezza.
Reconfigurable and Traffic-Aware MAC Design for Virtualized Wireless Networks via Reinforcement Learning

In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound.
On Worst-Case Robin Hood Hashing

We consider open addressing hashing and implement it by using the Robin Hood strategy; that is, in case of collision, the element that has traveled the farthest can stay in the slot. We hash $\sim \alpha n$ elements into a table of size n where each probe is independent and uniformly distributed over the table, and $\alpha < 1$ is a constant. Let $M_n$ be the maximum search time for any of the elements in the table. We show that with probability tending to one, $M_n \in [ \log_2 \log n + \sigma, \log_2 \log n + \tau ]$ for some constants $\sigma, \tau$ depending upon $\alpha$ only. This is an exponential improvement over the maximum search time in case of the standard FCFS (firstcome first served) collision strategy and virtually matches the performance of multiple-choice hash methods.
Automated assembly skill acquisition through human demonstration

Acquiring robot assembly skills through human demonstration is a challenging problem. To achieve this goal, not only the actions and objects have to be shown to the robot, but also the effect of the action needs to be estimated. Recognizing the subtle assembly actions is a non-trivial task, and it is difficult to estimate the effect of the action on the assembly parts due to the small part sizes. In this paper, with a RGB-D camera, we build a Portable Assembly Demonstration (PAD) system which can recognize the part/tool used, the action applied and the assembly state characterizing the spatial relationship between the parts. The experiment results proved that this PAD system can generate an assembly script with good accuracy in object and action recognition as well as assembly state estimation.
A flexible datapath allocation method for architectural synthesis

We present a robust datapath allocation method that is flexible enough to handle constraints imposed by a variety of target architectures. Key features of this method are its ability to handle accurate modeling of datapath units and the simultaneous optimization of direct objective functions. The proposed method consists of a new binding model construction scheme and an optimization technique based on simulated annealing. To illustrate the flexibility of this method, two datapath allocation procedures have been developed for two problem enviroments: (1) a procedure that incorporates interconnection area and delay estimates, where floor-planning is tightly integrated into datapath allocation; and (2) a procedure that handles registers, register files, and multiport memories for data storage, as well as random and linear topologies for interconnection architectures. Results from these two applications show our method produces competitive designs for benchmark circuits, as well as being flexible enough to be used for a variety of different domains.
Spectral Sub-band Analysis of Speaker Verification Employing Narrowband and Wideband Speech

It is well known that the speaker discriminative information is not equally distributed over the spectral domain. However, it is still not clear whether that distribution is altered when the speech is transmitted through telecommunication channels, which introduce different kinds of degradations. In this paper we address the analysis of different frequency sub-bands when the speech is distorted with different bandwidth filters and channel codecs, considering narrowband and wideband communications. Our i-vector experiments on different subbands with 782 speakers show that standard landline codecs perform generally better than wireless codecs due to their intrinsic coding algorithm, their performance being close to, but slightly worse than that of uncoded speech. Wideband signals offer significant benefits over narrowband for speaker verification. A smaller experiment with 21 speakers leads us to believe that the emerging super-wideband transmissions may provide even better results because it shows important speaker-specific content in the band 8-14kHz.