Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrient intake and balancing among female Colobus angolensis palliatus inhabiting structurally distinct forest areas: Effects of group, season, and reproductive state

Understanding intraspecific behavioral and dietary variation is critical for assessing primate populations’ abilities to persist in habitats characterized by increasing anthropogenic disturbances. While it is evident that some species exhibit considerable dietary flexibility (in terms of species‐specific plant parts) in relation to habitat disturbance, it is unclear if primates are characterized by similar variation and flexibility regarding nutrient intake. This study examined the effects of group, season, and reproductive state on nutrient intake and balancing in adult female Colobus angolensis palliatus in the Diani Forest, Kenya. During July 2014 to December 2015, estimates of nutrient intake were recorded for eight females from three groups inhabiting structurally and ecologically distinct forest areas differing in tree species composition and density. There were differences in metabolizable energy (ME) and macronutrient intakes among groups, seasons, and reproductive states. Most notably, females inhabiting one of the more disturbed forest areas consumed less ME and macronutrients compared to females in the more intact forest area. Contrary to prediction, females in early lactation consumed significantly less ME and macronutrients compared to non‐lactating and late lactation females. Despite differences in macronutrient intake, the relative contribution of macronutrients to ME were generally more conservative among groups, seasons, and reproductive states. Average daily intake ratios of non‐protein energy to available protein ranged from approximately 3.5:1–4.3:1 among groups. These results indicate that female C. a. palliatus demonstrate a consistent nutrient balancing strategy despite significant intergroup differences in consumption of species‐specific plant parts. Data from additional colobine species inhabiting different forest types are required to assess the extent to which nutrient balancing is constrained by phylogeny or is more flexible to local ecological conditions.
Breaking discrete symmetries in the effective field theory of inflation

We study the phenomenon of discrete symmetry breaking during the inflationary epoch, using a model-independent approach based on the effective field theory of inflation. We work in a context where both time reparameterization symmetry and spatial diffeomorphism invariance can be broken during inflation. We determine the leading derivative operators in the quadratic action for fluctuations that break parity and time-reversal. Within suitable approximations, we study their consequences for the dynamics of linearized fluctuations. Both in the scalar and tensor sectors, we show that such operators can lead to new direction-dependent phases for the modes involved. They do not affect the power spectra, but can have consequences for higher correlation functions. Moreover, a small quadrupole contribution to the sound speed can be generated.
Comparative Effectiveness of Conservative Management Compared to Cryotherapy in Localized Prostate Cancer Patients

The high frequency of treatment-related side effects for men with localized prostate cancer creates uncertainty for treatment outcomes. This study assessed the comparative effectiveness of treatment-related side effects associated with conservative management and cryotherapy in patients with localized prostate cancer. A retrospective longitudinal cohort study was conducted, using the linked data of the Surveillance, Epidemiology, and End Results and Medicare, which included patients diagnosed from 2000 through year 2013, and their Medicare claims information from 2000 through 2014. To compare the differences in baseline characteristics and treatment-related side effects between the study cohorts, χ2 tests were conducted. Multivariate logistic regression was used to assess the association between treatment selection and side effects. There were 7,998 and 3,051 patients in the conservative management and cryotherapy cohort, respectively. The likelihood of erectile dysfunction, lower urinary tract obstruction, urinary fistula, urinary incontinence, and hydronephrosis was reported to be significantly lower (53%, 35%, 69%, 65%, and 36%, respectively) in the conservative management cohort. Conservative management had a lower likelihood of treatment-related side effects compared to cryotherapy. However, further research is needed to compare other significant long-term outcomes such as costs associated with these treatment choices and quality of life.
The Unintended Consequences of Maternity Leaves: How Agency Interventions Mitigate the Negative Effects of Longer Legislated Maternity Leaves

To support women in the workplace, longer legislated maternity leaves have been encouraged in Scandinavian countries and recently in Canada. Yet, past research shows that longer legislated maternity leaves (i.e., 1 year and longer) may unintentionally harm women’s career progress. To address this issue, we first sought to identify one potential mechanism underlying negative effects of longer legislated maternity leaves: others’ lower perceptions of women’s agency. Second, we utilize this knowledge to test interventions that boost others’ perceptions of women’s agency and thus mitigate negative effects of longer legislated maternity leaves. We test our hypotheses in three studies in the context of Canadian maternity leave policies. Specifically, in Study 1, we found that others’ lower perceptions of women’s agency mediated the negative effects of a longer legislated maternity leave, that is, 1 year (vs. shorter, i.e., 1 month maternity leave) on job commitment. In Study 2, we found that providing information about a woman’s agency mitigates the unintended negative effects of a longer legislated maternity leave on job commitment and hireability. In Study 3, we showed that use of a corporate program that enables women to stay in touch with the workplace while on maternity leave (compared to conditions in which no such program was offered; a program was offered but not used by the applicant; and the program was offered, but there was no information about its usage by the applicant) enhances agency perceptions and perceptions of job commitment and hireability. Implications for theory and practice are discussed.
Unsupervised Sentiment Classification: A Hybrid Sentiment-Topic Model Approach

With the large volume of text available online it is becoming impractical to use supervised machine learning methods that require a sizeable training set of labelled data. In this paper we introduced a new sentiment-topic model called the hybrid sentiment-topic model (HST). The HST model is a completely unsupervised sentiment classification method that allows for the topical context of words in documents to be accounted for when classifying sentiment. The only input needed for the model is a list of positive seed words, a list of negative seed words, and the number of topics. The HST model differs from similar models as it ensures that each objective topic discovered has both a positive sentiment-topic and negative sentiment-topic associated with it; other similar models do not guarantee symmetric sentiment-topics. The HST model performs three functions, firstly, it discovers objective topics in a corpus of text; secondly, it finds a positive and negative sentiment-topic for each objective topic; and finally, it performs sentiment classification. The HST model is tested using a dataset consisting of movie reviews and a dataset of social media posts. For each dataset a variety of seed word lists and different numbers of topics are tested; the HST model is then compared against similar sentiment-topic models. In all experiments conducted, the HST model was found to outperform similar sentiment-topic models in terms of classification accuracy by a noticeable margin. Additionally, the HST model was found to converge faster than similar models and the accuracy was found to be more stable during the Gibbs sampling process.
Q-learning for adaptive load based routing

The paper deals with the control problem of routing in packet switched networks. Using Q-learning an adaptive, distributed and autonomous routing strategy can be obtained. The objective of the Q-learner under study is to balance the load such that average packet delivery time is optimised. If pure Q-learning is applied to routing each source has to learn the expected cost for sending a message via each of its neighbours for all destinations. Since Q-learning is basically a trial and error method packets have to be sent along non-optimal paths, which artificially increases the load. To reduce this effect and to speed up the learning a variant of Q-learning has been developed. Exploration and exploitation are partially decoupled such that stabilising features can be included in the Q-learning algorithm to cope with instabilities and overhead that might be caused by the costly exploitation in search of alternative paths. In the paper the above statements are justified mathematically and supported by the results of experiments.
Cross-subject workload classification using pupil-related measures

Real-time evaluation of a person's cognitive load can be desirable in many situations. It can be employed to automatically assess or adjust the difficulty of a task, as a safety measure, or in psychological research. Eye-related measures, such as the pupil diameter or blink rate, provide a non-intrusive way to assess the cognitive load of a subject and have therefore been used in a variety of applications. Usually, workload classifiers trained on these measures are highly subject-dependent and transfer poorly to other subjects. We present a novel method to generalize from a set of trained classifiers to new and unknown subjects. We use normalized features and a similarity function to match a new subject with similar subjects, for which classifiers have been previously trained. These classifiers are then used in a weighted voting system to detect workload for an unknown subject. For real-time workload classification, our methods performs at 70.4% accuracy. Higher accuracy of 76.8% can be achieved in an offline classification setting.
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping

In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via some UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. We also verify that normalization affect significantly some UDA methods; we observe a meaningful complementary contribution between coffee crops data; and a visual behavior suggests an existent of a cluster of samples that are more likely to be drawn from a specific data.
Fast Timing Analysis of Non-Tree Clock Network with Shorted Wires

A non-tree clock network, such as crosslink and mesh, includes some shorted wires to reduce clock skew. A short-circuit current that flows through the shorted wires makes conventional static timing analysis (STA) inapplicable. Transistor-level simulation may be applied but takes long time. We address a fast timing analysis of non-tree clock network. A partial circuit made of drivers, shorted wires, and receivers is extracted and represented as voltagedependent current sources with π-model of RC load. Given voltage waveforms at driver inputs, we calculate the waveform at each shorted node by repeating nodal analysis for each time step; the waveform is represented as piecewise linear function. As the waveform propagates to receiver input via RC tree, the responses for all linear segments are obtained and merged into a full waveform. The waveform at receiver input then passes through receiver to produce a linear waveform at receiver output. Finally, timing parameters from the waveform at receiver output are transferred to STA, such that it utilizes the parameters to analyze the remaining circuit from receiver outputs to clock sinks. Experiments with a few test circuits demonstrate that analysis time is reduced by 10× with only 1% error on average (both in delay and transition time) compared to SPICE.
α2δ-4 Is Required for the Molecular and Structural Organization of Rod and Cone Photoreceptor Synapses

α2δ-4 is an auxiliary subunit of voltage-gated Cav1.4 L-type channels that regulate the development and mature exocytotic function of the photoreceptor ribbon synapse. In humans, mutations in the CACNA2D4 gene encoding α2δ-4 cause heterogeneous forms of vision impairment in humans, the underlying pathogenic mechanisms of which remain unclear. To investigate the retinal function of α2δ-4, we used genome editing to generate an α2δ-4 knock-out (α2δ-4 KO) mouse. In male and female α2δ-4 KO mice, rod spherules lack ribbons and other synaptic hallmarks early in development. Although the molecular organization of cone synapses is less affected than rod synapses, horizontal and cone bipolar processes extend abnormally in the outer nuclear layer in α2δ-4 KO retina. In reconstructions of α2δ-4 KO cone pedicles by serial block face scanning electron microscopy, ribbons appear normal, except that less than one-third show the expected triadic organization of processes at ribbon sites. The severity of the synaptic defects in α2δ-4 KO mice correlates with a progressive loss of Cav1.4 channels, first in terminals of rods and later cones. Despite the absence of b-waves in electroretinograms, visually guided behavior is evident in α2δ-4 KO mice and better under photopic than scotopic conditions. We conclude that α2δ-4 plays an essential role in maintaining the structural and functional integrity of rod and cone synapses, the disruption of which may contribute to visual impairment in humans with CACNA2D4 mutations. SIGNIFICANCE STATEMENT In the retina, visual information is first communicated by the synapse formed between photoreceptors and second-order neurons. The mechanisms that regulate the structural integrity of this synapse are poorly understood. Here we demonstrate a role for α2δ-4, a subunit of voltage-gated Ca2+ channels, in organizing the structure and function of photoreceptor synapses. We find that presynaptic Ca2+ channels are progressively lost and that rod and cone synapses are disrupted in mice that lack α2δ-4. Our results suggest that alterations in presynaptic Ca2+ signaling and photoreceptor synapse structure may contribute to vision impairment in humans with mutations in the CACNA2D4 gene encoding α2δ-4.
Relevance of deprivation studies in understanding rapid eye movement sleep

Rapid eye movement sleep (REMS) is a unique phenomenon essential for maintaining normal physiological processes and is expressed at least in species higher in the evolution. The basic scaffold of the neuronal network responsible for REMS regulation is present in the brainstem, which may be directly or indirectly influenced by most other physiological processes. It is regulated by the neurons in the brainstem. Various manipulations including chemical, elec-trophysiological, lesion, stimulation, behavioral, ontogenic and deprivation studies have been designed to understand REMS genesis, maintenance, physiology and functional significance. Although each of these methods has its significance and limitations, deprivation studies have contributed significantly to the overall understanding of REMS. In this review, we discuss the advantages and limitations of various methods used for REMS deprivation (REMSD) to understand neural regulation and physiological significance of REMS. Among the deprivation strategies, the flowerpot method is by far the method of choice because it is simple and convenient, exploits physiological parameter (muscle atonia) for REMSD and allows conducting adequate controls to overcome experimental limitations as well as to rule out nonspecific effects. Notwithstanding, a major criticism that the flowerpot method faces is that of perceived stress experienced by the experimental animals. Nevertheless, we conclude that like most methods, particularly for in vivo behavioral studies, in spite of a few limitations, given the advantages described above, the flowerpot method is the best method of choice for REMSD studies.
AEAS - Towards High Energy-efficiency Design for OpenSSL Encryption Acceleration through HW/SW Co-design

Entering the Big Data Era leads to the rapid development of web applications which provide high performance sensitive access on large data centers. OpenSSL has been widely deployed as a freely available implementation of SSL/TLS protocol that secures transactions over the Internet. In order to accelerate the speed of OpenSSL, many alternative encryption approaches are designed. However, energy consumption has been ignored in the rush for performance. Energy efficiency becomes a challenge with the increasing demands for performance and energy saving in data centers. In this paper, we present the Adaptive Encryption Acceleration System (AEAS), an OpenSSL encryption acceleration scheme. It provides high energy-efficiency encryption through HW/SW co-design. The essential idea is exerting the superiorities of energy efficiency for different encryption approaches and making full use of system resource through the Dynamic Management Mechanism including RequestAllocation algorithm and DynamicScheduler algorithm. Specifically, this scheme supports instruction set and hardware to process the computation compatibly by the Adaptive Control Crypto (ac_crypto) engine. Experimental results show that AEAS can improve energy efficiency by up to 933.5%, 68.8%, and 483.7% comparing with software, AES-NI and QAT, respectively.
Self-Paced Learning Based Multi-view Spectral Clustering

Multi-view data are prevalent in both machine learning and artificial intelligence. A panoply of multi-view clustering algorithms have been proposed to deal with multiview data. However, most of them just blindly concatenate all the views in spite of characteristic of different views. Self-paced learning is a kind of learning scheme which comes from human learning. It progresses from easy example to complex example during learning process. In analogy with these intuitions, we can learn the easiness of multiple views. Therefore, in this paper, we first present a new Self-Paced Learning Regularizer which is a kind of mixture weighting scheme to allocate different weight to the different view by considering views’ complexity. To recap the effectiveness of our self-paced learning regularizer, we propose a novel self-paced learning based multi-view spectral clustering algorithm(SPLMVC), which can define complexity across views and then automatically assign weight to each view. Extensive experiments on real-world multi-view datasets reveal its strength by comparison with other state-of-art methods.
Fixation detection for head-mounted eye tracking based on visual similarity of gaze targets

Fixations are widely analysed in human vision, gaze-based interaction, and experimental psychology research. However, robust fixation detection in mobile settings is profoundly challenging given the prevalence of user and gaze target motion. These movements feign a shift in gaze estimates in the frame of reference defined by the eye tracker's scene camera. To address this challenge, we present a novel fixation detection method for head-mounted eye trackers. Our method exploits that, independent of user or gaze target motion, target appearance remains about the same during a fixation. It extracts image information from small regions around the current gaze position and analyses the appearance similarity of these gaze patches across video frames to detect fixations. We evaluate our method using fine-grained fixation annotations on a five-participant indoor dataset (MPIIEgoFixation) with more than 2,300 fixations in total. Our method outperforms commonly used velocity- and dispersion-based algorithms, which highlights its significant potential to analyse scene image information for eye movement detection.
Characterizing Memory-Latency Sensitivity of Sparse Matrix Kernels

Intel announced to launch a Xeon with high-latency main memory based on 3D Xpoint in 2018. This paper presents the performance evaluation of sparse matrix kernels on the future supercomputers with high-latency main memory such as 3D Xpoint. The authors propose a high throughput evaluation methodology for exhaustive experiments, which use the University of Florida sparse matrix collection and/or LIS (a Library of Iterative Solvers for linear systems) etc. Proposed methodology is very simple to use, highly flexible for environment and high-throughput. Latency sensitivity of SpMV is measured based on the proposed methodology with 208 sparse matrices and ten storage formats only in two days, which would take for about ten years by conventional simulators. We got several interesting knowledge about latency-sensitive kernels, sparse matrices, storage formats, and preconditioners, etc. We observed notable latency sensitivity in some applications, which are Graph500, HPCG and a part of preconditioners of iterative solvers. We found latency sensitivities of SpMV are high for larger matrices than the capacity of last level cache. This suggests main memory using 3D Xpoint must be combined with large DRAM cache.
New Faces of Marketing In The Era of The Web: From Marketing 1.0 To Marketing 3.0

In th e new economic and social environment, the development of new technologies coupled with the evolution of the Internet, had a deep impact on the lifestyles of the consumers and consequently on the concepts and the practices of the Marketing. Understanding the changes undergone by the marketing with the development of digital social networks and Web 3.0 technology is essential and the way in which the marketeurs can exploit this new technology. This contribution highlights mutations of marketing techniques to align the evolution of the Internet and the changes introduced by the passage of Web1.0 Web 3.0
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.