Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual bandit algorithm for risk-aware recommender systems

Context-Aware Recommender Systems can be naturally modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces

In this work, we ﬁrst show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classiﬁcation (CTC) system produces state-of-the-art results. We then show that using wordpieces as modeling units combined with CTC training, we can greatly simplify the engineering pipeline compared to conventional frame-based cross-entropy training by excluding all the GMM bootstrapping, decision tree building and force alignment steps, while still achieving very competitive word-error-rate. Additionally, using wordpieces as modeling units can signiﬁcantly improve runtime efﬁciency since we can use larger stride without losing accuracy. We further conﬁrm these ﬁndings on two internal VideoASR datasets: German, which is similar to English as a fusional language, and Turkish, which is an agglutinative language.
Surface deformation associated with dip‐slip faulting

A fault surface may be represented by a rectangular surface of horizontal length 2L, width W, and dip δ embedded in an elastic half-space with the top of the fault a depth h below the free surface. The vertical displacement of the free surface for a dip-slip motion Δu on such a fault surface can be calculated from the theory of Maruyama. This calculation has been made for fault models of three different earthquakes, and the results compared with the observed surface deformation in each case. For each calculation the dip of the fault plane was taken from the P wave fault plane solution. The preferred fault models are as follows: Alaska earthquake of March 28, 1964 (magnitude 8.4), 2L = 600 km, W = 200 km, δ = 9°, h = 20 km, and Δu = 10 m. Fairview Peak, Nevada, earthquake of December 16, 1954 (magnitude 7.1), 2L = 24 to 48 km, W = 6 km, δ = 62°, h = 0 km, and Δu = 2 m. Hebgen Lake, Montana, earthquake of August 18, 1959 (magnitude 7.1), 2L = 30 km, W = 15 km, δ = 54°, h = 0.4 km, and Δu = 10 m.
Participation-Contributed Temporal Dynamic Model for Group Activity Recognition

Group activity recognition, a challenging task that a number of individuals occur in the scene of activity while only a small subset of them participate in, has received increasing attentions. However, most of the previous methods model all the individuals' actions equivalently while ignoring a fact that not all of them are contributed to the discrimination of group activity. That is to say, only a small number of key actors (participants) play important roles in the whole group activity. Inspired by this, we explore a new "One to Key" idea to progressively aggregate temporal dynamics of key actors with different participation degrees over time from each person. Here, we focus on two types of key actors in the whole activity, who steadily move in the whole process (long moving time) or intensely move (but closely related to the group activity) at a significant moment. Based on this, we propose a novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a "One to Key" network. Specifically, "One" network aims at modeling the individual dynamic of each person. "One to Key" network feeds the outputs from the "One" network into a Bidirectional LSTM (Bi-LSTM) according to the order of individual's moving time. Subsequently, each output state of Bi-LSTM weighted by a trainable time-varying attention factor is aggregated by going through LSTM one-by-one. Experimental results on two benchmarks demonstrate that the proposed method improves group activity recognition performance compared to the state-of-the-arts.
The MITLL NIST LRE 2011 language recognition system

 This paper presents a description of the MIT Lincoln Laboratory (MITLL) language recognition system developed for the NIST 2011 Language Recognition Evaluation (LRE). The submitted system consisted of a fusion of four core classifiers, three based on spectral similarity and one based on tokenization. Additional system improvements were achieved following the submission deadline. In a major departure from previous evaluations, the 2011 LRE task focused on closed-set pairwise performance so as to emphasize a system’s ability to distinguish confusable language pairs. Results are presented for the 24-language confusable pair task at test utterance durations of 30, 10, and 3 seconds. Results are also shown using the standard detection metrics (DET, minDCF) and it is demonstrated the previous metrics adequately cover difficult pair performance. On the 30 s 24-language confusable pair task, the submitted and post-evaluation systems achieved average costs of 0.079 and 0.070 and standard detection costs of 0.038 and 0.033.
Efficient Buyer Groups With Prediction-of-Use Electricity Tariffs

Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use (POU) tariff that better reflects the predictability cost of a customer. Our tariff asks customers to pre-commit to a baseline consumption, and charges them based on both their actual consumption and the deviation from the anticipated baseline. First, we study, from a cooperative game theory perspective, the cost game induced by a single such tariff, and show customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. Second, we study the efficient (i.e., cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing POU tariffs are available. We propose a polynomial time algorithm to compute the efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic consumers in the U.K.
Seasonal changes in the net radiation/solar radiation ratio above a Cryptomeria japonica plantation forest

Predicting forest evapotranspiration has been a major goal in forest hydrology and ecology for many years (e.g., LANDSBERG and GOWER, 1997; KOMATSU and HOTTA, 2005). Evapotranspiration is a major component of the forest water cycle (e.g., WILSON et al., 2001; KUMAGAI et al., 2004a,b). Furthermore, evapotranspiration relates strongly to canopy photosynthesis (e.g., BERBIGIER et al., 2001), which is a major component of the forest carbon cycle (e.g., Van WIJK et al., 2001). Thus, accurate prediction of evapotranspiration rates leads to accurate prediction of forest water and carbon cycles (e.g., MCMURTRIE et al., 1990; HINGSTON et al., 1998). The Priestley-Taylor (PT) equation (PRIESTLEY and TAYLOR, 1972) is a useful tool to predict evapotranspiration rates from land surface. The PT equation formulates the evapotranspiration rate as
Research Article: Three Tools for Evaluating Participation: Focus Groups, Q Method, and Surveys

To enable successful public participation in environmental decision making, practitioners need to know what works, but evaluation of proposed and existing processes is often lacking. We tested three tools for evaluation—focus groups, Q method, and surveys—at two contaminated sites with extensive public participation. Each tool is evaluated based on its requirements for implementation, the information it produces, and its acceptability to stakeholders. Which tool is most appropriate depends heavily on the available resources, what is happening at the site, and the evaluator's goals.
How tourists see other tourists: analysis of online travelogues.

The study, based on the content analysis of 120 samples of online travelogues written by Japanese and Americans, investigates how tourist-tourist encounters are reported. Eight hundred and thirty eight episodes were extracted from the sample travelogues and coded according to the types of encounter (absence, direct presence or indirect presence of others), attitude of the writer (positive, negative or neutral) and also the types of tourist(s) encountered (same or different nationality). Overall results show that Japanese and Americans see other tourists differently. For example, when Japanese describe other tourists, they tend to make clear distinctions between Japanese and non-Japanese and to have more direct contacts with Japanese tourists than with tourists from other countries. Americans, on the other hand, seem quite the opposite: they are willing to make personal contact with non-American tourists. Some of the factors that lead to these differences are discussed and include differences in cultural characteristics and home environment.
Can Facebook be an effective mechanism for generating growth and value in small businesses?

Purpose – Online social networks (OSNs) offer organisations direct access to a plethora of information about their networks of connections and provide the means by which to create two‐way, business‐to‐consumer (B2C), information channels. Instead of traditional impersonal and one‐direction advertising, organisations can establish a personal and two‐way communication medium, by accepting members and having friends on these platforms. This paper aims to discuss the phenomenon of OSNs, and in particular Facebook, and examine whether they can be employed by small businesses as a resource for growth and adding value.Design/methodology/approach – A case study is presented that examines how a small business in New Zealand, specialising in the distribution of products that help babies and toddlers sleep through the night, has adopted Facebook as a tool for engaging with its largely stay‐at‐home customer base. This examination of The Sleep Store is an impartial study based on findings collected over a period of se.
Performance of a Speculative Transmission Scheme for Scheduling-Latency Reduction

Low latency is a critical requirement in some switching applications, specifically in parallel computer interconnection networks. The minimum latency in switches with centralized scheduling comprises two components, namely, the control-path latency and the data-path latency, which in a practical high-capacity, distributed switch implementation can be far greater than the cell duration. We introduce a speculative transmission scheme to significantly reduce the average control-path latency by allowing cells to proceed without waiting for a grant, under certain conditions. It operates in conjunction with any centralized matching algorithm to achieve a high maximum utilization and incorporates a reliable delivery mechanism to deal with failed speculations. An analytical model is presented to investigate the efficiency of the speculative transmission scheme employed in a non-blocking N times NR input-queued crossbar switch with R receivers per output. Using this model, performance measures such as the mean delay and the rate of successful speculative transmissions are derived. The results demonstrate that the control-path latency can be almost entirely eliminated for loads up to 50%. Our simulations confirm the analytical results.
Suburban Forest Change and Vegetation Water Dynamics in Atlanta, USA

In the suburban area of northwestern Atlanta, this project tests the hypothesis that tree species composition affects the hydrological response of forest patches and therefore should be considered when assessing the effect of land cover change in the water cycle of suburban areas. In a typical mixed forest fragment a diverse canopy with a rich tree species composition of several deciduous and evergreen tree species was found. The results show a relationship of more than 2:1 water flow under different soil moisture levels, for the broadleaf deciduous (Liquidambar styraciflua L.) compared to the evergreen pine tree species (Pinus taeda L.). The land cover change analysis for the lower Etowah watershed and five counties of northwestern Atlanta from 1992 to 2007 indicates forest cover loss, disturbance within the forest areas, changes from evergreen forest into mixed forest, and localized process of transition from forest towards urbanization. Overall, this research suggests that the water cycle in northern Atlanta may be affected by forest cover decrease, and by changes in the species composition of the forest fragment towards mixed forest.
Blueprints - Illustrating Architecture and Technical Parts using Hardware-Accelerated Non-Photorealistic Rendering

Outlining and enhancing visible and occluded features in drafts of architecture and technical parts are essential techniques to visualize complex aggregated objects and to illustrate position, layout, and relations of their components. In this paper, we present blueprints, a novel non-photorealistic hardware-accelerated rendering technique that outlines visible and non-visible perceptually important edges of 3D objects. Our technique is based on the edge map algorithm and the depth peeling technique to extract these edges from arbitrary 3D scene geometry in depth-sorted order. After edge maps have been generated, they are composed in image space using depth sprites, which allow us to combine blueprints with further 3D scene contents. We introduce depth masking to dynamically adapt the number of rendering passes for highlighting and illustrating features of particular importance and their relation to the entire assembly. Finally, we give an example of blueprints that visualize and illustrate ancient architecture in the scope of cultural heritage.
Sparsity-based Convolutional Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets coupled with recent advances in supervised deep learning methods are enabling the derivation of representative image features that can potentially impact different image analysis problems. However, such supervised approaches are not feasible in the medical domain where it is challenging to obtain a large volume of labelled data due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. Algorithms designed to work on small annotated datasets are useful but have limited applications. In an effort to address the lack of annotated data in the medical image analysis domain, we propose an algorithm for hierarchical unsupervised feature learning. Our algorithm introduces three new contributions: (i) we use kernel learning to identify and represent invariant characteristics across image sub-patches in an unsupervised manner; (ii) we leverage the sparsity inherent to medical image data and propose a new sparse convolutional kernel network (S-CKN) that can be pre-trained in a layer-wise fashion, thereby providing initial discriminative features for medical data; and (iii) we propose a spatial pyramid pooling framework to capture subtle geometric differences in medical image data. Our experiments evaluate our algorithm in two common application areas of medical image retrieval and classification using two public datasets. Our results demonstrate that the medical image feature representations extracted with our algorithm enable a higher accuracy in both application areas compared to features extracted from other conventional unsupervised methods. Furthermore, our approach achieves an accuracy that is competitive with state-of-the-art supervised CNNs.
Aversive Pavlovian Control of Instrumental Behavior in Humans

Adaptive behavior involves interactions between systems regulating Pavlovian and instrumental control of actions. Here, we present the first investigation of the neural mechanisms underlying aversive Pavlovian–instrumental transfer using fMRI in humans. Recent evidence indicates that these Pavlovian influences on instrumental actions are action-specific: Instrumental approach is invigorated by appetitive Pavlovian cues but inhibited by aversive Pavlovian cues. Conversely, instrumental withdrawal is inhibited by appetitive Pavlovian cues but invigorated by aversive Pavlovian cues. We show that BOLD responses in the amygdala and the nucleus accumbens were associated with behavioral inhibition by aversive Pavlovian cues, irrespective of action context. Furthermore, BOLD responses in the ventromedial prefrontal cortex differed between approach and withdrawal actions. Aversive Pavlovian conditioned stimuli modulated connectivity between the ventromedial prefrontal cortex and the caudate nucleus. These results show that action-specific aversive control of instrumental behavior involves the modulation of fronto-striatal interactions by Pavlovian conditioned stimuli.
A Methodological Framework of Travel Time Distribution Estimation for Urban Signalized Arterial Roads

Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic flows, signals, and other conditions on the infrastructure. However, the most common way both in literature and practice is to estimate or predict only expected travel times, not travel time distributions. By doing so, it fails to provide full insight into the travel time dynamics and variability on urban roads. Another limitation of this common approach is that the effect of traffic measures on travel time reliability cannot be evaluated. In this paper, an analytical travel time distribution model is presented especially for urban roads with fixed-time controlled intersections by investigating the underlying mechanisms of urban travel times. Different from mean travel time models or deterministic travel time models, the proposed model takes stochastic properties of traffic flow, stochastic arrivals and departures at intersections, and traffic signal coordination between adjacent intersections into account, a.
CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data

MOTIVATION Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem. RESULTS We develop a new method for prioritizing genes associated with a phenotype by Combining Gene expression and protein Interaction data (CGI). The method is applied to yeast gene expression data sets in combination with protein interaction data sets of varying reliability. We found that our method outperforms the intuitive prioritizing method of using either gene expression data or protein interaction data only and a recent gene ranking algorithm GeneRank. We then apply our method to prioritize genes for Alzheimer's disease. AVAILABILITY The code in this paper is available upon request.
TILE64 - Processor: A 64-Core SoC with Mesh Interconnect

The TILE64TM processor is a multicore SoC targeting the high-performance demands of a wide range of embedded applications across networking and digital multimedia applications. A figure shows a block diagram with 64 tile processors arranged in an 8x8 array. These tiles connect through a scalable 2D mesh network with high-speed I/Os on the periphery. Each general-purpose processor is identical and capable of running SMP Linux.
Enhancing the success rates by performing pooling decisions adjacent to the output layer

Learning classification tasks of (2 𝑛 × 2 𝑛 ) inputs typically consist of ≤ 𝑛 (2 × 2 ) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using the CIFAR-10 database, that pooling decisions adjacent to the last convolutional layer significantly enhance accuracy success rates (SRs). In particular, average SRs of the advanced-VGG with 𝒎 layers (A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m=6, 8, 14, 13, and 16, respectively. The results indicate A-VGG8’s SR is superior to VGG16’s, and that the SRs of A-VGG13 and A-VGG16 are equal, and comparable to that of Wide-ResNet16. In addition, replacing the three fully connected (FC) layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation FC layers, yielded similar SRs. These significantly enhanced SRs stem from training the most influential input-output routes, in comparison to the inferior routes selected following multiple MP decisions along the deep architecture. In addition, SRs are sensitive to the order of the non-commutative MP and average pooling operators adjacent to the output layer, varying the number and location of training routes. The results call for the reexamination of previously proposed deep architectures and their SRs by utilizing the proposed pooling strategy adjacent to the output layer.
Distributed beamforming in wireless relay networks with quantized feedback

This paper is on quantized beamforming in wireless amplify-and-forward (AF) relay networks. We use the generalized Lloyd algorithm (GLA) to design the quantizer of the feedback information and specifically to optimize the bit error rate (BER) performance of the system. Achievable bounds for different performance measures are derived. First, we analytically show that a simple feedback scheme based on relay selection can achieve full diversity. Unlike the previous diversity analysis on the relay selection scheme, our analysis is not aided by any approximations or modified forwarding schemes. Then, for highrate feedback, we find an upper bound on the average signalto- noise ratio (SNR) loss. Using this result, we demonstrate that both the average SNR loss and the capacity loss decay at least exponentially with the number of feedback bits. In addition, we provide approximate upper and lower bounds on the BER, which can be calculated numerically.We observe that our designs can achieve both full diversity as well as high array gain with only a moderate number of feedback bits. Simulations also show that our approximate BER is a reliable estimation on the actual BER. We also generalize our analytical results to asynchronous networks, where perfect carrier level synchronization is not available among the relays.
Evolutionary Sound Matching: A Test Methodology and Comparative Study

With the ever-increasing complexity of sound synthesisers, there is a growing demand for automated parameter estimation and sound space navigation techniques. Recent research in this domain has focused on the application of general-purpose evolutionary algorithms to match specific types of target sounds. However, it is difficult to establish whether success or failure of a particular match is due to the inefficiency of the optimisation engine, or the limitations of the matching synthesiser. In this paper the distinction between optimiser inefficiency and synthesiser limitations is elucidated with a contrived target test methodology that enables the performance of different optimisation techniques to be measured and compared. The methodology is applied to a Frequency Modulation synthesiser, in order to compare the performance of different Evolution Strategy-based algorithms. The algorithm producing the best results with contrived targets is then used to match a non-contrived acoustic instrument tone.