Miracle-dz/UniFAR-FTUs huggingface dataset:
A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

Abstract The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.
A database of genus-2 curves over the rational numbers

<jats:p>We describe the construction of a database of genus-<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="gif" xlink:type="simple" xlink:href="S146115701600019X_inline1" /><jats:tex-math>$2$</jats:tex-math></jats:alternatives></jats:inline-formula>curves of small discriminant that includes geometric and arithmetic invariants of each curve, its Jacobian, and the associated<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="gif" xlink:type="simple" xlink:href="S146115701600019X_inline2" /><jats:tex-math>$L$</jats:tex-math></jats:alternatives></jats:inline-formula>-function. This data has been incorporated into the<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="gif" xlink:type="simple" xlink:href="S146115701600019X_inline3" /><jats:tex-math>$L$</jats:tex-math></jats:alternatives></jats:inline-formula>-Functions and Modular Forms Database (LMFDB). </jats:p>
A Unified Implicit Dialog Framework for Conversational Search

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing the underlying data representation to build the components required for dialog interaction, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. A centralized knowledge representation is used to semantically ground multiple dialog modules. An associated set of tools are integrated with the framework to gather end users' input for continuous improvement of the system. The goal is to facilitate development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.
AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributed to the availability of large annotated datasets, such as ImageNet and Places. However, in biomedical imaging, it is very challenging to create such large annotated datasets, as annotating biomedical images is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, called AFT*, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhance the (fine-tuned) CNN via continuous fine-tuning. We have evaluated our method in three distinct biomedical imaging applications, demonstrating that it can cut the annotation cost by at least half, in comparison with the state-of-the-art method. This performance is attributed to the several advantages derived from the advanced active, continuous learning capability of our method. Although AFT* was initially conceived in the context of computer-aided diagnosis in biomedical imaging, it is generic and applicable to many tasks in computer vision and image analysis; we illustrate the key ideas behind AFT* with the Places database for scene interpretation in natural images.
The role of web hosting providers in detecting compromised websites

Compromised websites are often used by attackers to deliver malicious content or to host phishing pages designed to steal private information from their victims. Unfortunately, most of the targeted websites are managed by users with little security background - often unable to detect this kind of threats or to afford an external professional security service. In this paper we test the ability of web hosting providers to detect compromised websites and react to user complaints. We also test six specialized services that provide security monitoring of web pages for a small fee. During a period of 30 days, we hosted our own vulnerable websites on 22 shared hosting providers, including 12 of the most popular ones. We repeatedly ran five different attacks against each of them. Our tests included a bot-like infection, a drive-by download, the upload of malicious files, an SQL injection stealing credit card numbers, and a phishing kit for a famous American bank. In addition, we also generated traffic from seemingly valid victims of phishing and drive-by download sites. We show that most of these attacks could have been detected by free network or file analysis tools. After 25 days, if no malicious activity was detected, we started to file abuse complaints to the providers. This allowed us to study the reaction of the web hosting providers to both real and bogus complaints. The general picture we drew from our study is quite alarming. The vast majority of the providers, or "add-on" security monitoring services, are unable to detect the most simple signs of malicious activity on hosted websites.
A comprehensive review of 3D point cloud descriptors

The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention on the effective extraction of novel 3D point cloud descriptors for accurate and efficient of 3D computer vision tasks. However, how to de- velop discriminative and robust feature descriptors from various point clouds remains a challenging task. This paper comprehensively investigates the exist- ing approaches for extracting 3D point cloud descriptors which are categorized into three major classes: local-based descriptor, global-based descriptor and hybrid-based descriptor. Furthermore, experiments are carried out to present a thorough evaluation of performance of several state-of-the-art 3D point cloud descriptors used widely in practice in terms of descriptiveness, robustness and efficiency.
User experience enhancing filter for a Webcam based human computer interaction

In this paper, we present a physics filter which can enhance the user experience of human computer interaction applications. We demonstrate the filter through an interaction system with head gestures and movements. Users interact with the computer through the wink detection function, implemented by eye brightness intensity analysis, and head tracking function, implemented by a facial landmark detector. However, both intensity and facial landmark data are unstable in temporal domain, which deteriorates user experience. Our proposed physics filter is capable of enhancing stability with minimizing latency. This filter can be used in various interaction applications where data stabilization is required.
Artificial Financial Markets with Adaptive Trading Agents

Motivation: In the last decade there has been a surge of interest within the finance community in describing equity markets through computational agent models [5]. At the same time, financial markets are an important application area within artificial intelligence for the fields of agent-based modeling and machine learning, since agent objectives and interactions tend to be more clearly defined, both practically and mathematically, in these markets than in other areas. Computational modeling of markets allows for the opportunity to push beyond the restrictions of traditional theoretical models of markets through the use of computational power. At the same time, the artificial markets approach allows a fine-grained level of experimental control that is not available in real markets. Thus, data obtained from artificial market experiments can be compared to the predictions of theoretical models and to data from real-world markets, and the level of control allows one to examine precisely which settings and conditions lead to the deviations from theoretical predictions usually seen in the behavior of real markets. This project is also an application area for the general problems of distributed intelligence such as collective learning, coordination and competition. We are interested in studying how software agents endowed with learning abilities might interact, co-evolve, and cooperate in societies of learning agents.
Selling Out: The Inauthenticity Discount in the Craft Beer Industry

This paper investigates why audiences devalue organizations that behave inauthentically. One explanation is that inauthenticity leads to lower perceptions of product quality. This stems from the audience's doubt of an inauthentic actor's capability and commitment to produce high quality goods. Another explanation is that audiences discount the symbolic value -- or what the good represents -- of goods from inauthentic actors. I empirically test each of these mechanisms in the craft beer industry. First, I exploit exogenous variation in consumers' knowledge of craft brewers' inauthentic identity (whether they are owned by a corporate brewer) to empirically demonstrate an inauthenticity discount. Next, I decompose audience evaluations to show that knowledge of a producer's inauthenticity does not have a statistically significant impact on evaluators' sensory experience of the product, but that it does affect audience evaluations of the product's symbolic value.
Alterations of in vivo CA1 network activity in Dp(16)1Yey Down syndrome model mice

Down syndrome, the leading genetic cause of intellectual disability, results from an extra-copy of chromosome 21. Mice engineered to model this aneuploidy exhibit Down syndrome-like memory deficits in spatial and contextual tasks. While abnormal neuronal function has been identified in these models, most studies have relied on in vitro measures. Here, using in vivo recording in the Dp(16)1Yey model, we find alterations in the organization of spiking of hippocampal CA1 pyramidal neurons, including deficits in the generation of complex spikes. These changes lead to poorer spatial coding during exploration and less coordinated activity during sharp-wave ripples, events involved in memory consolidation. Further, the density of CA1 inhibitory neurons expressing neuropeptide Y, a population key for the generation of pyramidal cell bursts, were significantly increased in Dp(16)1Yey mice. Our data refine the ‘over-suppression’ theory of Down syndrome pathophysiology and suggest specific neuronal subtypes involved in hippocampal dysfunction in these model mice.
Bioavailability of phosphorus from various phosphates based on body weight and toe ash measurements.

Two experiments involving 1,152 turkeys were conducted to determine the relative bioavailability of phosphorus of eight phosphates from commercial and experimental sources. The basal diet, composed of 52% dehulled soybean meal and 41% ground yellow corn, contained 1.48% calcium and . 44% total phosphorus. Phosphorus from the eight sources was added to the basal diet at levels of . 09, . 18, or . 27% to form 24 diets with a constant calcium content. Each diet was fed to one pen of eight males and eight females from 1 to 4 wk of age in the first experiment and to two pens (one of each sex) of 16 poults/pen from 0 to 4 wk of age in the second experiment. Measurements of percentage ash of the middle toe pooled from all poults within a pen and of average body weight at 4 wk of age provided similar relative phosphorus availability values. With the phosphorus in monocalcium phosphate as the standard set at 100%, phosphorus from two sources of dicalcium phosphate and one source of defluorinated phosphate were found not significantly different in bioavailability (95, 105, and 103%, respectively). Phosphorus in curacao phosphate was only 55% available and in the three experimental defluorinated phosphates, phosphorus was 81, 70, and 87% available. Significant differences in bioavailability between two samples greater than 13 and 18% were found when using toe ash and body weight measurement, respectively.
Cooperation and Competition with Hyperscanning Methods: Review and Future Application to Emotion Domain

Cooperation and competition, as two common and opposite examples of interpersonal dynamics, are thought to be reflected by different cognitive, neural, and behavioral patterns. According to the conventional approach, they have been explored by measuring subjects' reactions during individual performance or turn-based interactions in artificial settings, that don't allow on-line, ecological enactment of real-life social exchange. Considering the importance of these factors, and accounting for the complexity of such phenomena, the hyperscanning approach emerged as a multi-subject paradigm since it allows the simultaneous recording of the brain activity from multiple participants interacting. In this view, the present paper aimed at reviewing the most significant work about cooperation and competition by EEG hyperscanning technique, which proved to be a promising tool in capturing the sudden course of social interactions. In detail, the review will consider and group different experimental tasks that have been developed so far: (1) paradigms that used rhythm, music and motor synchronization; (2) card tasks taken from the Game Theory; (3) computerized tasks; and (4) possible real-life applications. Finally, although highlighting the potential contribution of such approach, some important limitations about these paradigms will be elucidated, with a specific focus on the emotional domain.
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.
Mix design procedure for foamed asphalt mixtures

Mechanical foaming is the most popular method for producing warm mix asphalt in the United States; yet, a standard mix design procedure for foamed asphalt mixtures has not been established. Currently, the design of foamed asphalt mixtures consists of determining the optimum binder content by following a standard hot mix asphalt (HMA) design procedure and then estimating the foaming water content based on the foamer manufacturer's recommendation, engineering judgement, or previous experience. This practice fails to account for the effect of foaming on binder and mixture properties, and therefore may not ensure desirable foamed mixture performance. To overcome this shortcoming, additions to the standard mix design procedure were proposed in this study in order to accommodate foamed asphalt mixtures, which included optimisation of foaming water content and evaluation of foamed mixture performance. In addition, the proposed procedure was verified using materials procured from two field projects. The optimum foaming water content (Wopt%) was first determined by comparing the workability results of foamed mixtures at various foaming water contents. Afterwards, the foamed mixture at Wopt% was tested for coatability and performance evaluation, and all laboratory test parameters complied with established American Association of State Highway and Transportation Officials or agency specifications or were equivalent to or better than the control HMA test results. Therefore, the proposed foamed mix design procedure seemed a viable alternative for designing foamed mixtures with adequate pavement performance.
Age Effect on Automatic Inhibitory Function of the Somatosensory and Motor Cortex: An MEG Study

Age-related deficiency in the top-down modulation of cognitive inhibition has been extensively documented, whereas the effects of age on a bottom-up or automatic operation of inhibitory function were less investigated. It is unknown that whether the older adults (OA)’ reduced behavioral performance and neural responses are due to the insufficient bottom-up processes. Compared to behavioral assessments which have been widely used to examine the top-down control of response inhibition, electrophysiological recordings are more suitable to probe the early-stage processes of automatic inhibitory function. Sensory gating (SG), a phenomenon of attenuated neural response to the second identical stimulus in a paired-pulse paradigm, is an indicator to assess automatic inhibitory function of the sensory cortex. On the other hand, electricity-induced beta rebound oscillation in a single-pulse paradigm reflects cortical inhibition of the motor cortex. From the neurophysiological perspective, SG and beta rebound oscillation are replicable indicators to examine the automatic inhibitory function of human sensorimotor cortices. Thus, the present study aimed to use a whole-head magnetoencephalography (MEG) to investigate the age-related alterations of SG function in the primary somatosensory cortex (SI) and of beta rebound oscillation in the primary motor cortex (MI) in 17 healthy younger and 15 older adults. The Stimulus 2/Stimulus 1 (S2/S1) amplitude ratio in response to the paired-pulse electrical stimulation to the left median nerve was used to evaluate the automatic inhibitory function of SI, and the beta rebound response in the single-pulse paradigm was used to evaluate the automatic inhibitory function of MI. Although there were no significant age-related differences found in the SI SG ratios, the MI beta rebound power was reduced and peak latency was prolonged in the OA. Furthermore, significant association between the SI SG ratio and the MI beta rebound power, which was seen in the younger adults (YA), was absent in the OA. In conclusion, our data suggested an age-related defect of association between sensorimotor cortices regarding automatic inhibitory function.
Graph Based Proactive Secure Decomposition Algorithm for Context Dependent Attribute Based Inference Control Problem

Relational DBMSs continue to dominate the database market, and inference problem on external schema of relational DBMS's is still an important issue in terms of data privacy.Especially for the last 10 years, external schema construction for application-specific database usage has increased its independency from the conceptual schema, as the definitions and implementations of views and procedures have been optimized. This paper offers an optimized decomposition strategy for the external schema, which concentrates on the privacy policy and required associations of attributes for the intended user roles. The method proposed in this article performs a proactive decomposition of the external schema, in order to satisfy both the forbidden and required associations of attributes.Functional dependency constraints of a database schema can be represented as a graph, in which vertices are attribute sets and edges are functional dependencies. In this representation, inference problem can be defined as a process of searching a subtree in the dependency graph containing the attributes that need to be related. The optimized decomposition process aims to generate an external schema, which guarantees the prevention of the inference of the forbidden attribute sets while guaranteeing the association of the required attribute sets with a minimal loss of possible association among other attributes, if the inhibited and required attribute sets are consistent with each other. Our technique is purely proactive, and can be viewed as a normalization process. Due to the usage independency of external schema construction tools, it can be easily applied to any existing systems without rewriting data access layer of applications. Our extensive experimental analysis shows the effectiveness of this optimized proactive strategy for a wide variety of logical schema volumes.
Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non‐linear terms

The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed.