Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinically Significant Drug-Drug Interaction in a Large Antiretroviral Treatment Centre in Lagos, Nigeria.

Background: An important cause of treatment failure to antiretroviral therapy (ART) is the potential interaction between the antiretroviral (ARV) drugs and co-prescribed drugs used concomitantly for the treatment of opportunistic infections and co-morbid ailments in HIV-infected patients. Objectives: The study evaluated potential clinically significant drug interactions (CSDIs) occurring between recommended ART regimens and their co-prescribed non-antiretroviral drugs (CPD) Method: This study was carried out in a large HIV treatment centre (APIN clinic) in a Nigerian teaching hospital, in Lagos Nigeria, caring for over 20,000 registered patients. Electronic Medical Records (EMR) of 500 patients who received treatment between 2005 and 2015, were selected using systematic random sampling, reviewed retrospectively, and evaluated for potential CSDIs using Liverpool HIV Pharmacology Database and other similar databases. Results: Majority of patients, 421 (84%) were at risk of CSDIs, of which 410, (82%) were moderate and frequently involved co-trimoxazole + zidovudine (or stavudine) /lamivudine (386, 77.2%) and NNRTIs or PIs + artemisinin-based combination therapies (ACTs) [296, 59.2%]. Age (p=0.131), sex (p=0.316) and baseline CD4+ cell counts (p>0.05) were not significantly associated with CSDIs. The interactions, however, were significantly associated with the development of antiretroviral treatment failure (p <0.001) which occurred in nearly a third (139; 27.8%) of the patients. Conclusion: There is a high prevalence of CSDIs between ART and CPDs most of which were categorized as moderate. Further studies are required to evaluate the pharmacokinetic and clinical relevance of these interactions.
Neoadjuvant Chemotherapy in Cervical Cancer:
A Review Article

neoadjuvant chemotherapy in patients with FIGO . [5] Carcinoma of the cervix uteri. FIGO 26th annual report on the . [6] Randomized comparison of fluorouracil plus cisplatin . [7] Pelvic radiation with . [8] Concurrent cisplatin-based radiotherapy and . [9] Cisplatin, radiation, and adjuvant . [10] Concurrent chemotherapy and pelvic radiation therapy compared . [11] Improved treatment for cervical . [12] Survival and recurrence after concomitant chemotherapy . [13] Concomitant and neoadjuvant chemotherapy . [14] Hysterectomy with radiotherapy or chemotherapy . [15] Cost-effectiveness of radical hysterectomy . [16] Young cervical cancer patients may be more responsive than older . [17] Phase III randomized controlled trial of neoadjuvant . [18] Neo-adjuvant chemotherapy . [19] Prognostic value of responsiveness of neoadjuvant chemotherapy before . [20] Clinical efficacy of modified preoperative neoadjuvant . [21] Treatment patterns of FIGO Stage IB2 cervical cancer . [22] Neoadjuvant chemotherapy for locally advanced . [23] A review of topotecan in combination chemotherapy . [24] Clinical efficacy and safety of paclitaxel plus carboplatin . [25] Major clinical research advances in gynecologic . [26] Neoadjuvant chemotherapy for locally . [27] When does neoadjuvant chemotherapy . [28] Radiation-sparing managements for . [29] Evaluation of mediastinal lymph nodes . [30] New response evaluation criteria in . [31] Comparative study of neoadjuvant . [32] Is there a role for postoperative . [33] Optimal pathological response indicated . [34] Concurrent chemoradiation versus . [35] Improved survival with bevacizumab . [36] Randomized trial of cisplatin and . [37] Prognostic value of responsiveness . [38] Cervical . [39] Adjuvant chemotherapy . [40] Practice patterns of adjuvant therapy for intermediate/high . [41] Outcome of neoadjuvant intra-arterial chemotherapy and radical . [42] What is the value of hemoglobin as a prognostic . [43] Efficacy of neoadjuvant cisplatin . [44] Cervical cancer in pregnant . ©2018 ASP Ins. , Afarand Scholarly Publishing Institute, Iran ISSN: 2476-5848; Journal of Obstetrics, Gynecology and Cancer Research. 2018;3(2):87-91.
Additivity of relative magnetic helicity in finite volumes

Context. Relative magnetic helicity is conserved by magneto-hydrodynamic evolution even in the presence of moderate resistivity. For that reason, it is often invoked as the most relevant constraint on the dynamical evolution of plasmas in complex systems, such as solar and stellar dynamos, photospheric flux emergence, solar eruptions, and relaxation processes in laboratory plasmas. However, such studies often indirectly imply that relative magnetic helicity in a given spatial domain can be algebraically split into the helicity contributions of the composing subvolumes, in other words that it is an additive quantity. A limited number of very specific applications have shown that this is not the case. Aims. Progress in understanding the nonadditivity of relative magnetic helicity requires removal of restrictive assumptions in favor of a general formalism that can be used in both theoretical investigations and numerical applications. Methods. We derive the analytical gauge-invariant expression for the partition of relative magnetic helicity between contiguous finite volumes, without any assumptions on either the shape of the volumes and interface, or the employed gauge. Results. We prove the nonadditivity of relative magnetic helicity in finite volumes in the most general, gauge-invariant formalism, and verify this numerically. We adopt more restrictive assumptions to derive known specific approximations, which yields a unified view of the additivity issue. As an example, the case of a flux rope embedded in a potential field shows that the nonadditivity term in the partition equation is, in general, non-negligible. Conclusions. The nonadditivity of relative magnetic helicity can potentially be a serious impediment to the application of relative helicity conservation as a constraint on the complex dynamics of magnetized plasmas. The relative helicity partition formula can be applied to numerical simulations to precisely quantify the effect of nonadditivity on global helicity budgets of complex physical processes.
SeqDialN: Sequential Visual Dialog Network in Joint Visual-Linguistic Representation Space

The key challenge of the visual dialog task is how to fuse features from multimodal sources and extract relevant information from dialog history to answer the current query. In this work, we formulate a visual dialog as an information flow in which each piece of information is encoded with the joint visual-linguistic representation of a single dialog round. Based on this formulation, we consider the visual dialog task as a sequence problem consisting of ordered visual-linguistic vectors.For featurization, we use a Dense SymmetricCo-Attention network (Nguyen and Okatani,2018) as a lightweight vison-language joint representation generator to fuse multimodal features (i.e., image and text), yielding better computation and data efficiencies. For inference, we propose two Sequential Dialog Networks (SeqDialN): the first uses LSTM(Hochreiter and Schmidhuber,1997) for information propagation (IP) and the second uses a modified Transformer (Vaswani et al.,2017) for multi-step reasoning (MR). Our architecture separates the complexity of multimodal feature fusion from that of inference, which allows simpler design of the inference engine. On VisDial v1.0 test-std dataset, our best single generative SeqDialN achieves 62.54% NDCG and 48.63% MRR; our ensemble generative SeqDialN achieves 63.78% NDCG and 49.98% MRR, which set a new state-of-the-art generative visual dialog model. We fine-tune discriminative SeqDialN with dense annotations and boost the performance up to 72.41% NDCG and 55.11% MRR. In this work, we discuss the extensive experiments we have conducted to demonstrate the effectiveness of our model components. We also provide visualization for the reasoning process from the relevant conversation rounds and discuss our fine-tuning methods. The code is available at https://github.com/xiaoxiaoheimei/SeqDialN.
A Learned Performance Model for the Tensor Processing Unit

Accurate hardware performance models are critical to efficient code generation. They can be used by compilers to make heuristic decisions, by superoptimizers as an minimization objective, or by autotuners to find an optimal configuration of a specific program. However, they are difficult to develop because contemporary processors are complex, and the recent proliferation of deep learning accelerators has increased the development burden. We demonstrate a method of learning performance models from a corpus of tensor computation graph programs for the Tensor Processing Unit (TPU). We train a neural network over kernel-level sub-graphs from the corpus and find that the learned model is competitive to a heavily-optimized analytical cost model used in the production XLA compiler.
Survey of Spectrum Regulation for Intelligent Transportation Systems

As 5G communication technology develops, vehicular communications that require high reliability, low latency, and massive connectivity are drawing increasing interest from those in academia and industry. Due to these developing technologies, vehicular communication is not limited to vehicle components in the forms of Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) networks, but has also been extended to connect with others, such as pedestrians and cellular users. Dedicated Short-Range Communications (DSRC) is the conventional vehicular communication standard for Intelligent Transportation Systems (ITS). More recently, the 3rd Generation Partnership Project introduced Cellular-Vehicle-to-Everything (C-V2X), a competitor to DSRC. Meanwhile, the Federal Communications Commission (FCC) issued a Notice of Proposed Rulemaking (NPRM) to consider deploying Unlicensed National Information Infrastructure (U-NII) devices in the ITS band with two interference mitigation approaches: Detect-and-Vacate (DAV) and Re-channelization (Re-CH). With multiple standard options and interference mitigation approaches, numerous regulatory taxonomies can be identified and notification of relevant technical challenges issued. However, these challenges are much broader than the current and future regulatory taxonomies pursued by the different countries involved. Because their plans differ, the technical and regulatory challenges vary. This article presents a literature survey about the technical challenges, the current and future ITS band usage plans, and the major research testbeds for the U.S., Europe, China, Korea, and Japan. This survey shows that the most likely deployment taxonomies are (1) DSRC, C-V2X, and Wi-Fi with Re-CH; (2) DSRC and C-V2X with interoperation, and (3) C-V2X only. The most difficult technical challenge is the interoperability between the Wi-Fi-like DSRC and 4G LTE-like C-V2X.
Time Perception: A Review on Psychological, Computational, and Robotic Models

Animals exploit time to survive in the world. Temporal information is required for higher level cognitive abilities, such as planning, decision making, communication, and effective cooperation. Since time is an inseparable part of cognition, there is a growing interest in the artificial intelligence approach to subjective time, which has a possibility of advancing the field. The current survey study aims to provide researchers with an interdisciplinary perspective on time perception. First, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and related abilities. Second, we summarize the emergent computational and robotic models of time perception. A general overview to the literature reveals that a substantial amount of timing models are based on a dedicated time processing like the emergence of a clock-like mechanism from the neural network dynamics and reveals a relationship between the embodiment and time perception. We also notice that most models of timing are developed for either sensory timing (i.e., ability to assess an interval) or motor timing (i.e., ability to reproduce an interval). The number of timing models capable of retrospective timing, which is the ability to track time without paying attention, is insufficient. In this light, we discuss the possible research directions to promote the interdisciplinary collaboration in the field of time perception.
Relational Algorithms for k-means Clustering

The majority of learning tasks faced by data scientists involve relational data, yet most standard algorithms for standard learning problems are not designed to accept relational data as input. The standard practice to address this issue is to join the relational data to create the type of geometric input that standard learning algorithms expect. Unfortunately, this standard practice has exponential worst-case time and space complexity. This leads us to consider what we call the Relational Learning Question: ``Which standard learning algorithms can be efficiently implemented on relational data, and for those that can not, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm? '' In this paper, we address the relational learning question for two well-known algorithms for the standard $k$-means clustering problem. We first show that the $k$-means++ algorithm can be efficiently implemented on relational data. In contrast, we show that the adaptive $k$-means algorithm likely can not be efficiently implemented on relational data, as this would imply $P = \#P$. However, we show that a slight variation of this adaptive $k$-means algorithm can be efficiently implemented on relational data, and that this alternative algorithm has the same performance guarantee as the original algorithm, that is that it outputs an $O(1)$-approximate sketch.
Structural Causal Models Are (Solvable by) Credal Networks

A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model into a credal network. Causal inferences, such as interventions and counterfactuals, can consequently be obtained by standard algorithms for the updating of credal nets. These natively return sharp values in the identifiable case, while intervals corresponding to the exact bounds are produced for unidentifiable queries. A characterization of the causal models that allow the map above to be compactly derived is given, along with a discussion about the scalability for general models. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks and hence to systematically compute causal inferences. A number of demonstrative examples is presented to clarify our methodology. Extensive experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
Cold Posteriors and Aleatoric Uncertainty

Recent work has observed that one can outperform exact inference in Bayesian neural networks by tuning the "temperature" of the posterior on a validation set (the "cold posterior" effect). To help interpret this phenomenon, we argue that commonly used priors in Bayesian neural networks can significantly overestimate the aleatoric uncertainty in the labels on many classification datasets. This problem is particularly pronounced in academic benchmarks like MNIST or CIFAR, for which the quality of the labels is high. For the special case of Gaussian process regression, any positive temperature corresponds to a valid posterior under a modified prior, and tuning this temperature is directly analogous to empirical Bayes. On classification tasks, there is no direct equivalence between modifying the prior and tuning the temperature, however reducing the temperature can lead to models which better reflect our belief that one gains little information by relabeling existing examples in the training set. Therefore although cold posteriors do not always correspond to an exact inference procedure, we believe they may often better reflect our true prior beliefs.
Dissecting contact tracing apps in the Android platform

Contact tracing has historically been used to retard the spread of infectious diseases, but if it is exercised by hand in large-scale, it is known to be a resource-intensive and quite deficient process. Nowadays, digital contact tracing has promptly emerged as an indispensable asset in the global fight against the coronavirus pandemic. The work at hand offers a meticulous study of all the official Android contact tracing apps deployed hitherto by European countries. Each app is closely scrutinized both statically and dynamically by means of dynamic instrumentation. Depending on the level of examination, static analysis results are grouped in two axes. The first encompasses permissions, API calls, and possible connections to external URLs, while the second concentrates on potential security weaknesses and vulnerabilities, including the use of trackers, in-depth manifest analysis, shared software analysis, and taint analysis. Dynamic analysis on the other hand collects data pertaining to Java classes and network traffic. The results demonstrate that while overall these apps are well-engineered, they are not free of weaknesses, vulnerabilities, and misconfigurations that may ultimately put the user security and privacy at risk.
Homological algebra of modules over posets.

Homological algebra of modules over posets is developed, as closely parallel as possible to that of finitely generated modules over noetherian commutative rings, in the direction of finite presentations and resolutions. Centrally at issue is how to define finiteness to replace the noetherian hypothesis which fails. The tameness condition introduced for this purpose captures finiteness for variation in families of vector spaces indexed by posets in a way that is characterized equivalently by distinct topological, algebraic, combinatorial, and homological manifestations. Tameness serves both theoretical and computational purposes: it guarantees finite presentations and resolutions of various sorts, all related by a syzygy theorem, amenable to algorithmic manipulation. Tameness and its homological theory are new even in the finitely generated discrete setting of $\mathbb{N}^n$-gradings, where tame is materially weaker than noetherian. In the context of persistent homology of filtered topological spaces, especially with multiple real parameters, the algebraic theory of tameness yields topologically interpretable data structures in terms of birth and death of homology classes.
AUTSL: A Large Scale Multi-Modal Turkish Sign Language Dataset and Baseline Methods

Sign language recognition is a challenging problem where signs are identified by simultaneous local and global articulations of multiple sources, i.e. hand shape and orientation, hand movements, body posture, and facial expressions. Solving this problem computationally for a large vocabulary of signs in real life settings is still a challenge, even with the state-of-the-art models. In this study, we present a new large-scale multi-modal Turkish Sign Language dataset (AUTSL) with a benchmark and provide baseline models for performance evaluations. Our dataset consists of 226 signs performed by 43 different signers and 38,336 isolated sign video samples in total. Samples contain a wide variety of backgrounds recorded in indoor and outdoor environments. Moreover, spatial positions and the postures of signers also vary in the recordings. Each sample is recorded with Microsoft Kinect v2 and contains color image (RGB), depth, and skeleton modalities. We prepared benchmark training and test sets for user independent assessments of the models. We trained several deep learning based models and provide empirical evaluations using the benchmark; we used Convolutional Neural Networks (CNNs) to extract features, unidirectional and bidirectional Long Short-Term Memory (LSTM) models to characterize temporal information. We also incorporated feature pooling modules and temporal attention to our models to improve the performances. We evaluated our baseline models on AUTSL and Montalbano datasets. Our models achieved competitive results with the state-of-the-art methods on Montalbano dataset, i.e. 96.11% accuracy. In AUTSL random train-test splits, our models performed up to 95.95% accuracy. In the proposed user-independent benchmark dataset our best baseline model achieved 62.02% accuracy. The gaps in the performances of the same baseline models show the challenges inherent in our benchmark dataset. AUTSL benchmark dataset is publicly available at https://cvml.ankara. edu.tr.
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records

Recently, E-commerce platforms have extensive impacts on our human life. To provide an efficient platform, one of the most fundamental problem is how to balance the demand and supply in market segments. While conventional machine learning models have achieved a great success on data-sufficient segments, it may fail in a large-portion of segments in E-commerce platforms, where there are not sufficient records to learn well-trained models. In this paper, we tackle this problem in the context of market segment demand prediction. The goal is to facilitate the learning process in the target segments even facing a shortage of related training data by leveraging the learned knowledge from data-sufficient source segments. Specifically, we propose a novel algorithm, RMLDP, to incorporate a multi-pattern fusion network (MPFN) with a meta-learning paradigm. The multi-pattern fusion network considers both local and global temporal patterns for segment demand prediction. In the meta-learning paradigm, the transferable knowledge is regarded as the model parameter initializations of MPFN, which are learned from diverse source segments. Furthermore, we capture the segment relations by combining data-driven segment representation and segment knowledge graph representation and tailor the segment-specific relations to customize transferable model parameter initializations. Thus, even with limited data, the target segment can quickly find the most relevant transferred knowledge and adapt to the optimal parameters. Extensive experiments are conducted on two large-scale industrial datasets. The results show that our RMLDP outperforms a set of state-of-the-art baselines. In addition, RMLDP has also been deployed in Taobao, a real-world E-commerce platform. The online A/B testing results further demonstrate the practicality of RMLDP.
The center-mode instability of viscoelastic plane Poiseuille flow

A modal stability analysis shows that plane Poiseuille flow of an Oldroyd-B fluid becomes unstable to a `center mode' with phase speed close to the maximum base-flow velocity, $U_{max}$. The governing dimensionless groups are the Reynolds number $Re = \rho U_{max} H/\eta$, the elasticity number $E = \lambda \eta/(H^2\rho)$, and the ratio of solvent to solution viscosity $\eta_s/\eta$; here, $\lambda$ is the polymer relaxation time, $H$ is the channel half-width, and $\rho$ is the fluid density. For experimentally relevant values (e.g., $E \sim 0.1$ and $\beta \sim 0.9$), the predicted critical Reynolds number, $Re_c$, for the center-mode instability is around $200$, with the associated eigenmodes being spread out across the channel. In the asymptotic limit of $E(1 -\beta) \ll 1$, with $E$ fixed, corresponding to strongly elastic dilute polymer solutions, $Re_c \propto (E(1-\beta))^{-\frac{3}{2}}$ and the critical wavenumber $k_c \propto (E(1-\beta))^{-\frac{1}{2}}$. The unstable eigenmode in this limit is confined in a thin layer near the channel centerline. The above features are largely analogous to the center-mode instability in viscoelastic pipe flow (Garg et al., Phys. Rev. Lett., 121, 024502 (2018)), and suggest a universal linear mechanism underlying the onset of turbulence in both channel and pipe flows of suffciently elastic dilute polymer solutions.