Miracle-dz/UniFAR-FTUs huggingface dataset:
Play Duration based User-Entity Affinity Modeling in Spoken Dialog System

Multimedia streaming services over spoken dialog systems have become ubiquitous. User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences. However, fully voice-based interaction demands quantification of novel behavioral cues to determine user affinities. In this work, we propose using play duration cues to learn a matrix factorization based collaborative filtering model. We first binarize play durations to obtain implicit positive and negative affinity labels. The Bayesian Personalized Ranking objective and learning algorithm are employed in our low-rank matrix factorization approach. To cope with uncertainties in the implicit affinity labels, we propose to apply a weighting function that emphasizes the importance of high confidence samples. Based on a large-scale database of Alexa music service records, we evaluate the affinity models by computing Spearman correlation between play durations and predicted affinities. Comparing different data utilizations and weighting functions, we find that employing both positive and negative affinity samples with a convex weighting function yields the best performance. Further analysis demonstrates the model's effectiveness on individual entity level and provides insights on the temporal dynamics of observed affinities.
Historical biogeography, mitochondrial DNA, and allozymes of Psammodromus algirus (Lacertidae): a preliminary hypothesis

Abstract. Pairwise sequence polymorphism in mitochondrial DNA and levels of differentiation among presumptive gene loci(expressed as Nei’s D ˆ) tend to be greater between populations separated by the Strait of Gibraltar than between populationsinhabiting either Morocco or Spain. Ancestral Psammodromus algirus inhabiting Iberia and North Africa while the Strait ofGibraltar was being formed and stabilized (Miocene-Pliocene) evolved in association with physiogeographic change broughtabout by this barrier to gene exchange. Considered in units of genetic change per kilometer, mtDNA differentiation is greaterin Morocco than in Spain, and allozyme differentiation is slightly greater than, or equal to, that in Spain, suggesting that P . algirus has a longer and more complex history in Morocco than in Spain. Introduction Found virtually throughout Iberia, along theMediterranean coast of France, and acrossNorth Africa from southern Morocco to thenorthern half of Tunisia, Psammodromus al-girus is often one of the most common rep-tiles encountered (Guillaume, 1997; Schleichet al., 1996:444-447). Geographic change in-volving the formation of the Strait of Gibral-tar is fairly well understood (see Busack et al.,2005: ﬁg. 5), but we are just beginning to un-derstand diverse effects this cataclysmic seriesof events had on amphibian and reptile popu-lations inhabiting the region. While no geneticchange is apparent in populations of the stripe-necked turtle,
MORAL PSYCHOLOGY : THE IMPLICATIONS OF VENTROMEDIAL PREFRONTAL CORTEX DAMAGE by NICHOLAS ROBERSON Under the Direction of

In his book, A New Stoicism, Lawrence A. Becker sides with Posidonius, and against Chrysippus and others, on the issue of whether the sage will experience passions. The Posidonian position is that the sage will experience conflicting passions but will, nevertheless, overcome those passions through his or her perfected agency. In opposition, Chrysippus and others assert that the sage will have extirpated all his or her passions as a necessary precondition for achieving sagehood. I will argue that a body of neuroscientific evidence chiefly concerned with the effects of lesions to the ventromedial prefrontal cortex (vmPFC) supports Becker’s assertion that Chrysippus’ call for the extirpation of the passions is neither plausible nor desirable. Recent empirical discoveries support Posidonius’ view of the role of the passions in the virtuous life. INDEX WORDS: Stoicism, Neuroscience STOIC MORAL PSYCHOLOGY: THE IMPLICATIONS OF VENTROMEDIAL PREFRONTAL CORTEX DAMAGE
Interrogating Segregation, Integration and the Community Cohesion Agenda

The notion of segregation in its current application in British social policy confuses rather than illuminates social processes. While its historical roots lie in a discriminatory practice that was legally instilled in the US, current usage implies the self-segregation of minority ethnic groups. This paper examines the historical legacy of segregation in the US and UK to argue that a shift has occurred in the discourse surrounding the integration of ethnic minority groups, particularly British Muslims. Any attempt to advocate desegregation as a way to promote material equality has been replaced by its use to promote the removal of cultural difference. Contemporary British social policy has taken this further by advocating the necessity of social capital as a means to achieve community cohesion and shared values, further shifting emphasis away from material difference.
Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System

The structural organization of cortical areas is not random, with topographic maps commonplace in sensory processing centers. This topographical organization allows optimal wiring between neurons, multimodal sensory integration, and performs input dimensionality reduction. In this work, a model of topographic map formation is implemented on the SpiNNaker neuromorphic platform, running in realtime using point neurons, and making use of both synaptic rewiring and spike-timing dependent plasticity (STDP). In agreement with Bamford et al. (2010), we demonstrate that synaptic rewiring refines an initially rough topographic map over and beyond the ability of STDP, and that input selectivity learnt through STDP is embedded into the network connectivity through rewiring. Moreover, we show the presented model can be used to generate topographic maps between layers of neurons with minimal initial connectivity, and stabilize mappings which would otherwise be unstable through the inclusion of lateral inhibition.
Symbolic VQA on Visual Advertisements with SymViSe Networks

Understanding the symbolic rhetoric of advertisements is difficult. An agent must recognize creatively portrayed objects, their relationships and cultural connotations, and perform common-sense reasoning. To that end, we will be tackling a new task defined by the CVPR Automatic Understanding of Visual Advertisements workshop. For each visual advertisement image, we must predict one of the three correct labels amongst a set of 15 action-reason statements. An advertisement of a woman squeezing a dog as a perfume bottle and causing the dog pain would match the following action-reason statement: ”I should not buy products that are tested on animals because it is very cruel. ” To attempt this task, we adapt a popular VQA architecture to implement a 2-stream ViSe (Visual Semantic) Network, which takes an image and candidate action-reason statement and predicts a probability that the two match; this helps demonstrate the difficulty of the task and serves as a baseline of existing techniques. Then, we add a third symbolic stream to ViSe to construct a novel model, which we coin as the SymViSe (Symbolic Visual Semantic) Network. We further demonstrate how applying attention mechanisms can boost the performance of these two networks. Our best SymViSe model with attention achieves a 37.62% validation accuracy, significantly higher than the naive approach of guessing randomly (20%) and anything found in the literature. We also further isolate the magnitude of influence of the symbolic stream and attention mechanisms on task performance. We conduct a further error analysis to understand common failure modes.
Regulation of ATM and ATR by Smarcal1 and BRG1

The G2/M checkpoint is activated on DNA damage by the ATM and ATR kinases that are regulated by post-translational modifications. In this paper, the transcriptional co-regulation of ATM and ATR by SMARCAL1 and BRG1, both members of the ATP-dependent chromatin remodeling protein family, is described. SMARCAL1 and BRG1 co-localize on the promoters of ATM and ATR; downregulation of SMARCAL1/BRG1 results in transcriptional repression of ATM/ATR and therefore, overriding of the G2/M checkpoint leading to mitotic abnormalities. On doxorubicin-induced DNA damage, SMARCAL1 and BRG1 are upregulated and in turn, upregulate the expression of ATM/ATR. Phosphorylation of ATM/ATR is needed for the transcriptional upregulation of SMARCAL1 and BRG1, and therefore, of ATM and ATR on DNA damage. The regulation of ATM/ATR is rendered non-functional if SMARCAL1 and/or BRG1 are absent or if the two proteins are mutated such that they are unable to hydrolyze ATP, as in for example in Schimke Immuno-Osseous Dysplasia and Coffin-Siris Syndrome. Thus, an intricate transcriptional regulation of DNA damage response genes mediated by SMARCAL1 and BRG1 is present in mammalian cells.
Temporal changes in North Atlantic circulation constrained by planktonic foraminiferal shell weights

Records of planktonic foraminiferal shell weights for Globigerina bulloides, covering Termination I from four proximal sites at waters depths from 1150 to 4045 m in the northeast Atlantic, demonstrate the influence of dissolution superimposed upon initial shell weight variability. Records of shell weight, unaffected by dissolution, may be used as a reference for interpreting deeper records in terms of preservation history. Combining records of planktonic shell weight (and benthic δ13C) from shallow and deep sites suggests that maximum oceanic stratification and incursion of southern sourced deep waters in the North Atlantic occurred at about 18–20 ka, defining the glacial mode of Atlantic circulation. Reduced stratification and enhanced preservation in deeper waters reflect conditions during Heinrich event 1. A state similar to the modern mode of Atlantic circulation was attained by about 10 ka.
A novel distributed secondary frequency regulation scheme for power networks with high order turbine governor dynamics

We consider the problem of distributed secondary frequency regulation in power networks such that stability and an optimal power allocation are attained. This is a problem that has been widely studied in the literature, and two main control schemes have been proposed, usually referred to as 'primal-dual' and 'distributed averaging proportional-integral (DAPI)' respectively. However, each has its limitations, with the former requiring knowledge of uncontrollable demand, which can be difficult to obtain in real time, and with the existing literature on the latter being based on static models for generation and demand. We propose a novel control scheme that overcomes these issues by making use of generation measurements in the control policy. In particular, our analysis allows distributed stability and optimality guarantees to be deduced with practical measurement requirements and permits a broad range of linear generation dynamics, that can be of higher order, to be incorporated in the power network. We show how the controller parameters can be selected in a computationally efficient way by solving appropriate linear matrix inequalities (LMIs). Furthermore, we demonstrate how the proposed analysis applies to several examples of turbine governor models. The practicality of our analysis is demonstrated with simulations on the Northeast Power Coordinating Council (NPCC) 140-bus system that verify that our proposed controller achieves convergence to the nominal frequency and an economically optimal power allocation.
Strength advantages of chemically polished boron fibers before and after reaction with aluminum

In order to determine their strength potential, the fracture properties of different types of commercial boron fibers were measured before and after application of secondary strengthening treatments. The principal treatments employed were a slight chemical polish, which removed low strength surface flaws, and a heat treatment in oxygen, which contracted the fibers and thereby compressed intrinsic bulk flaws. Those fiber types most significantly strengthened were 200 to 400 micrometers (8 to 16 mil) diameter boron on tungsten fibers produced in a single chemical vapor deposition reactor. The slight polish increased average tensile strenghts from 3.4 to 4.4 CN/m2 (500 to 640 ksi) and reduced coefficients of variation from about 15 to 3 percent. The oxygen heat treatment plus slight polish further improved average strengths to 5.5 GN/m2 (800 ksi) with coefficients near 3 percent. To ascertain whether these excellent properties could be retained after fabrication of B/Al composites, as produced and polished 203 micrometers (8 mil) fibers were thinly coated with aluminum, heat treated at B/Al fabrication temperatures, and then tested in tension and flexure at room temperature. The pre-polished fibers were observed to retain their superior strengths to higher temperatures than the as-produced fibers even though both were subjected to the same detrimental reaction with aluminum.
Neuromorphic Event-Based Generalized Time-Based Stereovision

3D reconstruction from multiple viewpoints is an important problem in machine vision that allows recovering tridimensional structures from multiple two-dimensional views of a given scene. Reconstructions from multiple views are conventionally achieved through a process of pixel luminance-based matching between different views. Unlike conventional machine vision methods that solve matching ambiguities by operating only on spatial constraints and luminance, this paper introduces a fully time-based solution to stereovision using the high temporal resolution of neuromorphic asynchronous event-based cameras. These cameras output dynamic visual information in the form of what is known as “change events” that encode the time, the location and the sign of the luminance changes. A more advanced event-based camera, the Asynchronous Time-based Image Sensor (ATIS), in addition of change events, encodes absolute luminance as time differences. The stereovision problem can then be formulated solely in the time domain as a problem of events coincidences detection problem. This work is improving existing event-based stereovision techniques by adding luminance information that increases the matching reliability. It also introduces a formulation that does not require to build local frames (though it is still possible) from the luminances which can be costly to implement. Finally, this work also introduces a methodology for time based stereovision in the context of binocular and trinocular configurations using time based event matching criterion combining for the first time all together: space, time, luminance, and motion.
Domestic quality certification and growth of Vietnamese MSMEs

Using two waves of the Mirco, Small and Medium sized Enterprises (MSMEs) survey of Vietnamese manufacturing firms, this paper first explores what drives firms' decision to have a domestically recognized certificate, taking into account a rich number of factors related to the cost and expected benefits of certification as well as institutional factors. It further explores the presence of a positive and significant effect of domestic certificates on firm growth, these serving as signaling devices for desirable attributes under information asymmetry and thus leading to an increase in legitimacy and reputation. Evidence is indeed found for a signaling effect of certification, this being stronger for more recently adopted certificates, for advertising firms and for women entrepreneurs.
Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles

We introduce Ignition: an end-to-end neural network architecture for training unconstrained self-driving vehicles in simulated environments. The model is a ResNet-18 variant, which is fed in images from the front of a simulated F1 car, and outputs optimal labels for steering, throttle, braking. Importantly, we never explicitly train the model to detect road features like the outline of a track or distance to other cars; instead, we illustrate that these latent features can be automatically encapsulated by the network.
Function Norms and Regularization in Deep Networks

Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of functions defined by a network and the difficulty in measuring function complexity. There exists no method in the literature for additive regularization based on a norm of the function, as is classically considered in statistical learning theory. In this work, we propose sampling-based approximations to weighted function norms as regularizers for deep neural networks. We provide, to the best of our knowledge, the first proof in the literature of the NP-hardness of computing function norms of DNNs, motivating the necessity of an approximate approach. We then derive a generalization bound for functions trained with weighted norms and prove that a natural stochastic optimization strategy minimizes the bound. Finally, we empirically validate the improved performance of the proposed regularization strategies for both convex function sets as well as DNNs on real-world classification and image segmentation tasks demonstrating improved performance over weight decay, dropout, and batch normalization. Source code will be released at the time of publication.
Gradient-Free Supervised and Unsupervised Learning with Rewards

We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment. We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent’s lifetime as it learns to drive in a realistic simulated environment. A full version of this paper [2] and videos can be found in https: //saynaebrahimi.github. io/corl.html
Ontogenetic and sexual variation in the coloration of the lacertid lizards Iberolacerta monticola and Podarcis bocagei. Do the females prefer the greener males

Changes in the coloration of the lacertid lizards Iberolacerta monticola and Podarcis bocagei with age in populations from NW Spain are described. The onset of sexual maturity in P. bocagei males involves a change in the ventral (yellow) and dorsal (green) colorations, which is different from immature males (dorsally brownish in color). In I. monticola males, the ventral coloration also changes to a deep green when they reach maturity, while the dorsal coloration remains brownish as in the immature specimens. In this species, the green dorsal coloration is acquired gradually after maturity. Only the oldest individuals have a predominantly green dorsal coloration. The differences between the two species in the time males take to acquire the green dorsal coloration could be related to their different longevity. The coloring is acquired gradually in the most long-lived species ( I. monticola ). A field study was carried out on the behaviour of adult males of I. monticola during the reproductive period. The males with green dorsal coloration were seen to pair with females significantly more frequently than those with the brownish dorsal color. The increase in the green dorsal coloration (conspicuous) with the size and age of the males of this species would appear to have a clear function as an intersexual or intrasexual signal.
Image Caption Validation

Our paper introduces the novel task of image caption validation and a ﬁrst attempt towards achieving high performance on this task using deep learning models trained on MS COCO and ReferIt. Image caption validation is the task of classifying whether an image caption correctly describes the contents of the image. Image captions capture information about objects, their attributes and their relationships with other objects in the image. Our system parses this textual information using a GloVe word embedding and an LSTM network and compares it against the visual features extracted from the image using a convolutional neural network to realize whether the textual features match the contents of the image or of a portion of the image. Our best model achieves 88.62% accuracy on MS COCO. However, our qualitative analysis shows that the development of a dataset tailored for image caption validation is needed for the training of a model that is able to handle the more nuanced caption validation cases. Our codebase developed in Python using Tensorﬂow can be found by clicking here.
The Mechanism for siRNA Transmembrane Assisted by PMAL

The capacity of silencing genes makes small interfering RNA (siRNA) appealing for curing fatal diseases. However, the naked siRNA is vulnerable to and degraded by endogenous enzymes and is too large and too negatively charged to cross cellular membranes. An effective siRNA carrier, PMAL (poly(maleic anhydride-alt-1-decene) substituted with 3-(dimethylamino) propylamine), has been demonstrated to be able to assist siRNA transmembrane by both experiments and molecular simulation. In the present work, the mechanism of siRNA transmembrane assisted by PMAL was studied using steered molecular dynamics simulations based on the martini coarse-grained model. Here two pulling rates, i.e., 10−6 and 10−5 nm·ps−1, were chosen to imitate the passive and active transport of siRNA, respectively. Potential of mean force (PMF) and interactions among siRNA, PMAL, and lipid bilayer membrane were calculated to describe the energy change during siRNA transmembrane processes at various conditions. It is shown that PMAL-assisted siRNA delivery is in the mode of passive transport. The PMAL can help siRNA insert into lipid bilayer membrane by lowering the energy barrier caused by siRNA and lipid bilayer membrane. PMAL prefers to remain in the lipid bilayer membrane and release siRNA. The above simulations establish a molecular insight of the interaction between siRNA and PMAL and are helpful for the design and applications of new carriers for siRNA delivery.
Comparative thermal biology and depth distribution of largemouth bass (Micropterus salmoides) and northern pike (Esox lucius) in an urban harbour of the Laurentian Great Lakes

Understanding how individuals are distributed in space and time, as well as how they interact with dynamic environmental conditions, represent fundamental knowledge gaps for many fish species. Using acoustic telemetry tags, we monitored the temperatures and depths used by northern pike (Esox lucius L., 1758) and largemouth bass (Micropterus salmoides (Lacepede, 1802)) in Toronto Harbour (Lake Ontario). Northern pike and largemouth bass had similar thermal experiences throughout the year, except during summer, when northern pike were observed in cooler waters than largemouth bass. Both species used different depths throughout the year, with northern pike occupying deeper depths. Statistical modelling indicated that depth usage was influenced by all variables (season, species, and body size) and interactions between them, whereas thermal preferences were influenced by the main effects and interactions between species:season and species : body size. Both species were observed at temperatures warmer than thos.