Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach for Hybrid BF-pfPSO Technique for Face and Fingerprint Multimodal Biometric System

In the recent developments in technologies, security is the main aspect to provide the better confidentiality and integrity to the system. Generally, the biometric system is the important authentication mechanism which provides to protect from any misleading action. It has to validate and verify a person based on unique features of an individual. Basically, biometrics system has two broad areas namely unimodal and multimodal biometric system. Multimodal biometrics is a more secure way of authentication for the identity of a person than unimodal biometrics. Because unimodal biometrics has its own limitations like non-universality, vulnerability to spoofing attacks, noisy data, less robustness. In this paper, we proposed a multimodal biometrics system for face and fingerprint recognition using weighted sum score level fusion. In addition to that, the Hybrid Bacterial Foraging with parameter free Particle Swarm Optimization BF-pfPSO algorithm is used to optimize the weights assigned to face and fingerprint modalities at the score level fusion which attains good optimality and showing faster convergence than other hybrid algorithms. Feature extraction of the face is done by using PCA (Principal Component Analysis) whereas the feature extraction of the fingerprint is done by using minutiae matching. The matching scores of each modality are fused after normalization using weighted sum score level fusion technique and finally, the result is compared with the Equal error rate and RoC curve for the performance measure.
Uniform inference for bounds on the distribution and quantile functions of treatment effects in randomized experiments

This paper develops a novel approach to uniform inference for functions that bound the distribution and quantile functions of heterogeneous treatment effects in randomized experiments when only marginal treatment and control distributions are observed and the joint distribution of outcomes is unobserved. These bounds are nonlinear maps of the marginal distribution functions of control and treatment outcomes, and statistical inference methods for nonlinear maps usually rely on smoothness through a type of differentiability. We show that the maps from marginal distributions to bound functions are not differentiable, but uniform test statistics applied to the bound functions - such as Kolmogorov-Smirnov or Cramer-von Mises - are directionally differentiable. We establish the consistency and weak convergence of nonparametric plug-in estimates of the test statistics and show how they can be used to conduct inference for bounds uniformly over the distribution of treatment effects. We also establish the directional differentiability of minimax operators applied to general - that is, not only convex-concave - functions, which may be of independent interest. In addition, we develop detailed resampling techniques to conduct practical inference for the bounds or for the true distribution or quantile function of the treatment effect distribution. Finally, we apply our methods to the evaluation of a job training program.
Stage-based Hyper-parameter Optimization for Deep Learning

As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters. Although hyper-parameter optimization is crucial in training deep learning models for high model performance, effectively executing such a computation-heavy workload still remains a challenge. We observe that numerous trials issued from existing hyper-parameter optimization algorithms share common hyper-parameter sequence prefixes, which implies that there are redundant computations from training the same hyper-parameter sequence multiple times. We propose a stage-based execution strategy for efficient execution of hyper-parameter optimization algorithms. Our strategy removes redundancy in the training process by splitting the hyper-parameter sequences of trials into homogeneous stages, and generating a tree of stages by merging the common prefixes. Our preliminary experiment results show that applying stage-based execution to hyper-parameter optimization algorithms outperforms the original trial-based method, saving required GPU-hours and end-to-end training time by up to 6.60 times and 4.13 times, respectively.
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.
Weighted infinitesimal bialgebras

As a uniform of two versions of infinitesimal bialgebras introduced respectively by Joni-Rota and Loday-Ronco, weighted infinitesimal bialgebras play an important role in mathematics and mathematical physics. In this paper, we introduce the concept of weighted infinitesimal Hopf modules and show that any module carries a natural structure of weighted infinitesimal unitary Hopf module over a weighted quasitriangular infinitesimal unitary bialgebra. We decorate planar rooted forests $H_{\mathrm{RT}}(X, \Omega)$ in a new way, and prove that the $H_{\mathrm{RT}}(X, \Omega)$, together with a coproduct $\Delta_{\epsilon}$ and grafting operations $\{ B^+_\omega \mid \omega\in \Omega\}$, is the free $\Omega$-cocycle infinitesimal unitary bialgebra (resp. Hopf algebra) of weight zero on a set $X$. A combinatorial description of $\Delta_{\epsilon}$ is given. As applications, we obtain the initial object in the category of cocycle infinitesimal unitary bialgebras (resp. Hopf algebras) on undecorated planar rooted forests, which is the object studied in the (noncommutative) Connes-Kreimer Hopf algebra. Finally, we derive two pre-Lie algebras from an arbitrary weighted infinitesimal bialgebra and weighted commutative infinitesimal bialgebra, respectively. The second construction generalizes the Gelfand-Dorfman Theorem on Novikov algebras.
The Tilted Beta Binomial Linear Regression Model: a Bayesian Approach

This paper proposes new linear regression models to deal with overdispersed binomial datasets. These new models, called tilted beta binomial regression models, are defined from the tilted beta binomial distribution, proposed assuming that the parameter of the binomial distribution follows a tilted beta distribution. As a particular case of this regression models, we propose the beta rectangular binomial regression models, defined from the binomial distribution assuming that their parameters follow a beta rectangular distribution. These new linear regression models, defined assuming that the parameters of these new distributions follow regression structures, are fitted applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to an overdispersed binomial dataset of the number of seeds that germinate depending on the type of chosen seed androot.
NANYANG TECHNOLOGICAL UNIVERSITY SCHOOL OF HUMANITIES AND SOCIAL SCIENCES #SG50ShadesOfGrey – The social life of a hashtag

“Hashtags that straddle its multiple uses (tagging and paralanguage, narrative and the informality of speech) are most likely to be successful. ” (MacDonald, 2015). To better understand the workings of the hashtag, how it evolved and even becoming a form of online interactive action, this study investigates a hashtag that went viral in Singapore last year. The #SG50ShadesOfGrey hashtag was created using the combination of two domains – the popular western erotic story ‘50 Shades of Grey’ and Singapore’s 50 year of independence ‘SG50’. Notably, the studied is a prime example of a global-turned-local product where Singaporeans perpetuated the hashtag with their dash of local experiences that were relatable and engaging By analysing the #SG50ShadesOfGrey content produced from February to August 2015, particularly the tweets, this discourse study showed that the #SG50ShadesOfGrey phenomenon was largely successful due to the production of sexually-charged jokes. The study also demonstrated how the sexual innuendos are formed within these jokes and how the Singapore reference at the end made the punchline. Additionally, this phenomenon exemplified the creative side of Singaporeans as there were #SG50ShadesOfGrey-related content produced on both online and offline platforms (i.e. official #SG50ShadesOfGrey clothing); ultimately depicting the vibrant social life of the hashtag. #SG50SHADESOFGREY – THE SOCIAL LIFE OF A HASHTAG 3
Radius Adaptive Convolutional Neural Network

Convolutional neural network (CNN) is widely used in computer vision applications. In the networks that deal with images, CNNs are the most time-consuming layer of the networks. Usually, the solution to address the computation cost is to decrease the number of trainable parameters. This solution usually comes with the cost of dropping the accuracy. Another problem with this technique is that usually the cost of memory access is not taken into account which results in insignificant speedup gain. The number of operations and memory access in a standard convolution layer is independent of the input content, which makes it limited for certain accelerations. We propose a simple modification to a standard convolution to bridge this gap. We propose an adaptive convolution that adopts different kernel sizes (or radii) based on the content. The network can learn and select the proper radius based on the input content in a soft decision manner. Our proposed radius-adaptive convolutional neural network (RACNN) has a similar number of weights to a standard one, yet, results show it can reach higher speeds. The code has been made available at: this https URL.
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning

Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization to make a model robust to systematic task or domain shifts. However, in practice, the performance of these methods can suffer, when there are no coherent semantic relationships between the tasks (or domains). We present Invenio, a structured meta-learning algorithm to infer semantic similarities between a given set of tasks and to provide insights into the complexity of transferring knowledge between different tasks. In contrast to existing techniques such as Task2Vec and Taskonomy, which measure similarities between pre-trained models, our approach employs a novel self-supervised learning strategy to discover these relationships in the training loop and at the same time utilizes them to update task-specific models in the meta-update step. Using challenging task and domain databases, under few-shot learning settings, we show that Invenio can discover intricate dependencies between tasks or domains, and can provide significant gains over existing approaches in terms of generalization performance. The learned semantic structure between tasks/domains from Invenio is interpretable and can be used to construct meaningful priors for tasks or domains.
On the $\Lambda$-doubling in the B$^3\Pi_1$ state of TlF

Thallium monofluoride (TlF) is a prime candidate molecule for precision measurements aimed at discovering new physics. Optical cycling on the B $\leftarrow$ X transition around 271 nm enhances this potential. Hyperfine resolved ultraviolet spectra have been reported to determine the degree of rotational level mixing in the B-state and the efficiency of laser cooling on the B $\leftarrow$ X transition [E. B. Norrgard ${\it et~al.}$, Physical Review A ${\bf 95}$, 062506 (2017)]. Using these high-resolution spectra, the hyperfine structure in the B-state of TlF is re-analysed and the magnitude of the $\Lambda$-doubling in the B-state is discussed.
Meta Adaptation using Importance Weighted Demonstrations

Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dynamically shifts, model fitting on continuously aggregated data alone would be futile. In some cases, the distribution shifts, so much, that it is difficult for an agent to infer the new task. We propose a novel algorithm to generalize on any related task by leveraging prior knowledge on a set of specific tasks, which involves assigning importance weights to each past demonstration. We show experiments where the robot is trained from a diversity of environmental tasks and is also able to adapt to an unseen environment, using few-shot learning. We also developed a prototype robot system to test our approach on the task of visual navigation, and experimental results obtained were able to confirm these suppositions.
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where each part is first encoded into a feature representation using a part autoencoder. The core component of PQ-NET is a sequence-to-sequence or Seq2Seq autoencoder which encodes a sequence of part features into a latent vector of fixed size, and the decoder reconstructs the 3D shape, one part at a time, resulting in a sequential assembly. The latent space formed by the Seq2Seq encoder encodes both part structure and fine part geometry. The decoder can be adapted to perform several generative tasks including shape autoencoding, interpolation, novel shape generation, and single-view 3D reconstruction, where the generated shapes are all composed of meaningful parts.
Should the Music Industry Sue Its Own Customers? Impacts of Music Piracy and Policy Suggestions

Two beliefs about music piracy prevail in the music industry. First, music piracy hurts music record sales. Second, the only copyright regime that can help the music industry is one that will eradicate music piracy. To test the two beliefs, I construct a unique survey data set, estimate the demand for music and iPods and show three things. First, music piracy does hurt record sales. Second, music piracy contributes 20% to iPod sales. Finally, counterfactuals experiments show that while a regime without music piracy benefits music producers at the expense of students and Apple, another regime with legal online music and iPod royalty benefits most students and music producers at the expense of Apple. ∗I am indebted to my advisor Pat Bajari for his continuous encouragement and support. I benefit from the suggestions of Tom Holmes, Kyoo il Kim, Om Narasimhan, Minjung Park, Amil Petrin, Hakki Yazici, and seminar participants at Bates White, Colby College, Columbia University, Federal Reserve Bank at Kansas City and University of Minnesota. I also thank Dulguun Batbold, Andrew Cassey, John Dalton, Tom Holmes, Nick Guo, Christos Ioannou, Ka Fai Li, Mallory Leung, Tina Marsh, Connan Snider, and Junichi Suzuki for their help in conducting the survey. Lastly I am grateful to the Economics department of the University of Minnesota for financial support. The usual disclaimer applies. Correspondence: tleung@cuhk.edu. hk
A numerical study of rippling instability driven by the electron-phonon coupling in finite size graphene membranes

Suspended graphene exhibits ripples of size ranging from 50 to 100 A and height $\sim$10A, however, their origin remains undetermined. Previous theoretical works have proposed that rippling in graphene might be generated by the coupling between the bending modes and the density of electrons. These studies theoretically proposed that in the thermodynamic limit a membrane of single layer graphene displays a lattice instability for large enough electron-phonon coupling, undergoing a phase transition from a flat phase to a rippled one. In this work we solve the elasticity equations for a finite membrane of graphene coupled to the charge distribution, modeled in the tight-binding approximation, we find that the electron-phonon coupling controls a transition from a stable flat configuration to a stable rippled phase.
M L ] 2 1 N ov 2 01 9 PAC-Bayesian Transportation Bound

Empirically, the PAC-Bayesian analysis is known to produce tight risk bounds for practical machine learning algorithms. However, in its näıve form, it can only deal with stochastic predictors while such predictors are rarely used and deterministic predictors often performs well in practice. To fill this gap, we develop a new generalization error bound, the PAC-Bayesian transportation bound, unifying the PAC-Bayesian analysis and the chaining method in view of the optimal transportation. It is the first PACBayesian bound that relates the risks of any two predictors according to their distance, and capable of evaluating the cost of derandomization of stochastic predictors faced with continuous loss functions. As an example, we give an upper bound on the derandomization cost of spectrally normalized neural networks (NNs) to evaluate how much randomness contributes to the generalization of NNs.
Perceptions of Infection Control Practices and the use of Vignettes to Alter Infection Control Behavior: A Feasibility Study

Aim To explore the perceptions of infection control practices among healthcare personnel and evaluate the use of authentic vignettes as a means to alter infection control behavior. Method Four authentic vignettes were used as a part of reflective dialogues with healthcare personnel. An evaluation of the dialogues was performed with six healthcare personnel using the focus group technique. Qualitative content analysis was used to analyze the data. Results The mind-set to help one another and do one’s best in every situation was described as a core aspect in preventing the transmission of microorganisms. Having support, taking personal responsibility, being knowledgeable about infection control practices, and having a reasonable workload were seen to play decisive roles in controlling the spread of infection. Discussing authentic comprehensible vignettes with colleagues during the allotted time was considered a valuable method for improving infection control practices. Conclusion Meaningful insights on how best to use vignettes as a means to improve infection control practice were gained. These findings should be considered when designing theory-driven interventions in different contexts, which are aimed at improving infection control practices in health care.
Climate change challenges of small and medium sized Nordic non-life insurers

Climate change is one of the most powerful megatrends altering the operating conditions of the insurance sector because of vulnerability of societies, individuals and firms interconnected with their businesses. It has been claimed that climaterelated business risk resembles an iceberg, were around 30 percent of the overall risk is visible, while the bigger and much greater part is under the surface (Lenckus, 2008). This is the climate risk exposure that might transfer into insurance risks consciously or subconsciously depending on risk strategies insurers choose. The risk strategies in question are to accept/retain, reduce, share/transfer, exploit, or ignore risks.
Parasitic infection and anaemia during pregnancy in Sierra Leone.

The role of intestinal nematode infections in the aetiology of iron deficiency and anaemia in pregnant women was investigated in peri-urban and rural areas of Western Sierra Leone. A randomised placebo-controlled field trial was carried out to evaluate the efficacy of a single course of albendazole (400 mg) and daily iron-folate supplements (36 g iron and 5 mg folate), administered after the first trimester, as control interventions for intestinal nematode infections and anaemia during pregnancy. At baseline, in the first trimester of pregnancy, the prevalence (and geometric mean intensity) of intestinal nematodes was as follows: Ascaris lumbricoides 21.1% (267 eggs per gram); Necator americanus 66.5% (191 epg); and Trichuris trichiura 71.9% (93 epg). Chronic undernutrition (height g/l) in 21.2% of women. Iron deficiency is likely to have a dietary basis in these women. The dietary iron intake was predominantly non-haem, and was estimated to supply less than 1 g of absorbable iron daily. High fertility and closely spaced pregnancies placed additional stress on iron stores. The findings of this study indicate that anthelminthic treatment should be included in strategies to control maternal anaemia in Western Sierra Leone. It is recommended that pregnant women routinely receive a single course of anthelminthics after the first trimester, alongside daily iron-folate supplements, to minimise the decline in maternal Hb concentration during pregnancy. These interventions could be implemented through the existing primary health care system, although the use of traditional birth attendants should be investigated as a means of improving compliance and coverage. The implications of these findings for public health policy in other antenatal populations will depend on the local epidemiology of intestinal nematode infections and on the extent of underlying maternal iron deficiency and anaemia.
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. Previous work usually took a pair of utterances to decide whether one utterance is the parent of the other. We believe the entire ancestral history is a very important information source to make accurate prediction. Therefore, we design a novel masking mechanism to guide the ancestor flow, and leverage the transformer model to aggregate all ancestors to predict parent utterances. Our experiments are performed on the Reddit dataset (Zhang, Culbertson, and Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In addition, we also report experiments on a new larger corpus from the Reddit platform and release this dataset. We show that the proposed model, that takes into account the ancestral history of the conversation, significantly outperforms several strong baselines including the BERT model on all datasets.