Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of P Glycoprotein in Absorption of Novel Antimalarial Drugs (cid:1)

Bidirectional transport of four novel antimalarial compounds was determined using Caco-2 cell monolayers. P glycoprotein-mediated efﬂux was greatest for pyronaridine (5 to 20 (cid:1) M) and low for naphthoquine (5 (cid:1) M). With 20 (cid:1) M naphthoquine, net efﬂux was blocked, suggesting saturation of the transporter. Piperaquine and dihydroartemisinin were not transported by the system. Permeability glycoprotein (P-gp) ATP-dependent transporters inﬂuence the passage of many drugs across epithelial bar-riers in the intestine, brain, liver, and kidney (1) and may alter their pharmacokinetic and/or pharmacodynamic properties. Several antimalarial drugs substrates and/or of P-gp In the intestine, P-gp has a secretory which can contribute to low bioavailability and substrate-inhib-itor
Tak (cid:1) aful demand: a review of selected literature

Purpose – This paper aims to provide a systematic review of literature on the demand for tak (cid:1) aful (Islamic insurance) from articles published from January 2009 to June 2019. The review aims to synthesise and segmentpreviouslypublished researchtoidentify thegaps andprovide futureresearch direction. Design/methodology/approach – A systematic review of the literature was conducted. Past research was analysed,andcontent comparisons based onresearchfocus, contextandmethodologywere evaluated. Findings – It was found that not much has been written and published on tak (cid:1) aful demand in quality journals. The ﬁ rst two articles were published in 2009, but it was only in 2017 that coverage of the topic rapidly expanded. Although no article was found to have been published in 2018 on tak (cid:1) aful demand, there was one published article on the topic in 2019. This paper also found that not much attention has been given to tak (cid:1) aful demandfrom thecorporate sector. Research limitations/implications – The de ﬁ ned rule for document searching and selection excluded out-of-scope documents that might be relevant. Furthermore, as this paper concentrates exclusively on articles published in English journals, the possibility that other relevant works do appear elsewhere in a different language is notdenied. Practical implications – Factors determining tak (cid:1) aful demand are provided, and general directions are discussed,which managers canuse to developmarket sharefurther. Originality/value – Such an extensive review of literature on tak (cid:1) aful demand has not been done before. Other than revealing ambiguities, gaps and contradictions in the literature, this paper sketches an avenue for further research. It also provides information and guidance for other researchers wishing to embark on research on tak (cid:1) aful demand.
Adoptive T Cell Transfer-Based Cancer Immunotherapy is a Promising Strategy for Cancer Treatment

there are limitations to its successful implementation. TIL-based therapy is an individualized treatment that requires surgical removal of tumor tissues for TIL cultivation. Furthermore, few medical centers worldwide provide TIL-based therapy as it requires a highly skilled medical staff to isolate and cultivate TILs. To overcome these barriers, genetically modified cancer-specific T cells, such as T Cell Receptor (TCR)and Chimeric Antigen Receptor (CAR)-transduced T cells are being developed to augment ACT-mediated immunotherapeutic responses against various types of cancer and have already shown encouraging therapeutic effects in clinical trials [13-17,20]. TCRs expressed on T cells can be genetically engineered in vitro to specifically recognize and kill cancer cells [15]. Cancer regression in patients with metastatic melanoma following administration of autologous T cells genetically engineered to express a TCR against MART1 was first reported in 2006 by Rosenberg et al. at the NIH [21]. TCR-transduced T cells against several tumor antigens including MART1, CEA, gp100, NY-ESO-1, and MAGEA3 have been tested in clinical trials [15]. The results from these clinical trials have shown great promise in treating various types of cancers including metastatic melanoma, metastatic colorectal cancer, metastatic synovial cell sarcoma, and epithelial malignancies. Importantly, CAR-transduced T cell based immunotherapy has been producing exciting clinical results. The concept of CAR was firstly introduced by Eshhar et al [22] and the CAR structure is composed of an extracellular single-chain variable fragment (scFv) of an antibody, a transmembrane domain, and intracellular signaling domains derived from molecules involved in T cell signaling. Although CAR-transduced T cells have been used to treat patients with various types of cancer in clinical trials, the most promising results from CAR-transduced T cell therapy have been with CD19-based targeting of B cell malignancies [23-27]. Several recent clinical trials performed by NIH, University of Pennsylvania, Memorial Sloan-Kettering Cancer Center have demonstrated positive clinical outcomes with anti-CD19 CAR T cell therapy in patients with B cell malignancies [6].
Quantum Bits and Quantum Secrets: How Quantum Physics is revolutionizing Codes and Computers

A concise, "no-frills" introduction to quantum computation and quantum cryptography for non-experts. Rather than concentrating on stories about scientists and philosophical concepts, Quantum Bits and Quantum Secrets: How Quantum Physics is revolutionizing Codes and Computers provides a step-by-step approach to quantum information. Starting from the idea of quantum cryptography, it presents the basic principles of quantum mechanics and explains how this can be used to make cryptography absolutely secure against eavesdropping. Subsequently, the most important quantum algorithms are explained and technical problems in realizing quantum computers are discussed, followed by a presentation of recent experimentssome of which are found here in a textbook for the first time. Easily accessible for undergraduates and graduatesespecially those studying subjects other than physicswho need an introduction to this rapidly developing topic.
CROSS - CULTURAL RESEARCH OF STRATEGIES AND EFFICIENCY IN VISUAL SEMANTIC SEARCH in competence assessment.

The influence of linguistic background on cognitive task performance was identified as one of prospective research areas in the early 80s. New technologies evoke a new interest to investigation in this area. However, most of the studies on linguistic experience and cognitive skills have been conducted on an English-language sample. It becomes obvious that the identification of similarities and differences in language processing should be studied in systems with different language organization, including different writing systems: hieroglyphic, syllabic and alphabetic writing. The current study involved participants of three language groups (Russian, Chinese and Japanese, n=64) who took part in an eye-tracking experiment on semantic visual search. The participants were asked to look for meaningful English words in 18 different letter matrices (15*15), search success and oculomotor parameters were registered with the SMI RED system. The results indicate significant differences in most of the considered characteristics. Data show that different aspects of linguistic experience (native language, the level of linguistic competence, current proportion of language use) influence word search strategies. Task performance seems to be associated with specific oculomotor patterns, indicating a high level of cognitive processing with longer fixations on the relevant information and faster glides along irrelevant noise. The findings not only clarify the influence of linguistic experience on the cognitive architecture of semantic search but also sets directions for technology results, the experience of letter-by-letter gathering of words persists. use it perfectly in difficult
Invariants of real Lefschetz fibrations

In this note we introduce certain invariants of real Lefschetz fibrations. We call these invariants {\em real Lefschetz chains}. We prove that if the fiber genus is greater than 1, then the real Lefschetz chains are complete invariants of real Lefschetz fibrations with only real critical values. If however the fiber genus is 1, real Lefschetz chains are not sufficient to distinguish real Lefschetz fibrations. We show that by adding a certain binary decoration to real Lefschetz chains, we get a complete invariant.
CIA_A_238769 1241..1248

Xiaochuan Huo* Ming Yang* Ning Ma Feng Gao Dapeng Mo Xiaoqing Li Anxin Wang Yongjun Wang Zhongrong Miao 1Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 2China National Clinical Research Center for Neurological Diseases, Beijing, People’s Republic of China; 3Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
Constructing Robust Emotional State-based Feature with a Novel Voting Scheme for Multi-modal Deception Detection in Videos

Deception detection is an important task that has been a hot research topic due to its potential applications. It can be applied in many areas, from national security (e.g., airport se- curity, jurisprudence, and law enforcement) to real-life appli-cations (e.g., business and computer vision). However, some critical problems still exist and are worth more investigation. One of the signiﬁcant challenges in the deception detection tasks is the data scarcity problem. Until now, only one multimodal benchmark open dataset for human deception detec- tion has been released, which contains 121 video clips for deception detection (i.e., 61 for deceptive class and 60 for truthful class). Such an amount of data is hard to drive deep neural network-based methods. Hence, those existing models often suffer from overﬁtting problems and low generalization ability. Moreover, the ground truth data contains some unus- able frames for many factors. However, most of the literature did not pay attention to these problems. Therefore, in this pa- per, we design a series of data preprocessing methods to deal with the aforementioned problem ﬁrst. Then, we propose a multi-modal deception detection framework to construct our novel emotional state-based feature and use the open toolkit openSMILE to extract the features from the audio modality. We also design a voting scheme to combine the emotional states information obtained from visual and audio modalities. Finally, we can determine the novel emotion state transfor- mation (EST) feature with our self-designed algorithms. In the experiment, we conduct the critical analysis and compar- ison of the proposed methods with the state-of-the-art multimodal deception detection methods. The experimental results show that the overall performance of multi-modal deception detection has a signiﬁcant improvement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221 to 0.9265.
Internet-based Social Engineering Attacks, Defenses and Psychology: A Survey

Social engineering attacks are a major cyber threat because they often serve as a first step for an attacker to break into an otherwise well-defended network, steal victims' credentials, and cause financial losses. The problem has received due amount of attention with many publications proposing defenses against them. Despite this, the situation has not improved. In this paper, we aim to understand and explain this phenomenon by looking into the root cause of the problem. To this end, we examine the literature on attacks and defenses through a unique lens we propose -- {\em psychological factors (PFs) and techniques (PTs)}. We find that there is a big discrepancy between attacks and defenses: Attacks have deliberately exploited PFs by leveraging PTs, but defenses rarely take either of these into consideration, preferring technical solutions. This explains why existing defenses have achieved limited success. This prompts us to propose a roadmap for a more systematic approach towards designing effective defenses against social engineering attacks.
Outcome Adaptive Propensity Score Methods for Handling Censoring and High-Dimensionality: Application to Insurance Claims

Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, recent studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimates, while including variables that are predictive of only the outcome can improve efficiency. In this paper, we propose a flexible approach to incorporating outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability (OP) as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, among multiple treatment groups. Simulation studies indicate that incorporating OP for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer.
INTUITIONISTIC SAHLQVIST THEORY FOR DEDUCTIVE SYSTEMS

Sahlqvist theory is extended to the fragments of the intuitionistic propositional calculus that include the conjunction connective. This allows us to introduce a Sahlqvist theory of intuitionistic character amenable to arbitrary protoalgebraic deductive systems. As an application, we obtain a Sahlqvist theorem for the fragments of the intuitionistic propositional calculus that include the implication connective and for the extensions of the intuitionistic linear logic.
Covariate-Assisted Community Detection on Sparse Networks

Community detection is an important problem when processing network data. Traditionally, this is done by exploiting the connections between nodes, but connections can be too sparse to detect communities in many real datasets. Node covariates can be used to assist community detection; see Binkiewicz et al. (2017); Weng and Feng (2022); Yan and Sarkar (2021); Yang et al. (2013). However, how to combine covariates with network connections is challenging, because covariates may be high-dimensional and inconsistent with community labels. To study the relationship between covariates and communities, we propose the degree corrected stochastic block model with node covariates (DCSBM-NC). It allows degree heterogeneity among communities and inconsistent labels between communities and covariates. Based on DCSBM-NC, we design the adjusted neighbor-covariate (ANC) data matrix, which leverages covariate information to assist community detection. We then propose the covariate-assisted spectral clustering on ratios of singular vectors (CA-SCORE) method on the ANC matrix. We prove that CA-SCORE successfully recovers community labels when 1) the network is relatively dense; 2) the covariate class labels match the community labels; 3) the data is a mixture of 1) and 2). CA-SCORE has good performance on synthetic and real datasets. The algorithm is implemented in the R(R Core Team (2021)) package CASCORE.
Can Cooling and Heating Functions Be Modeled with Homogeneous Radiation Fields?

Cooling and heating functions describe how radiative processes impact the thermal state of a gas as a function of its temperature and other physical properties. In a most general case the functions depend on the detailed distributions of ionic species and on the radiation spectrum. Hence, these functions may vary on a very wide range of spatial and temporal scales. In this paper, we explore cooling and heating functions between 5 ≤ z ≤ 10 in simulated galaxies from the Cosmic Reionization On Computers project. We compare three functions: (1) the actual cooling and heating rates of hydrodynamic cells as a function of cell temperature, (2) the median cooling and heating functions computed using median interstellar medium (ISM) properties (median ISM), and (3) the median of the cooling and heating functions of all gas cells (instantaneous). We find that the median ISM and instantaneous approaches to finding a median cooling and heating function give identical results within the spread due to cell-to-cell variation. However, the actual cooling (heating) rates experienced by the gas at different temperatures in the simulations do not correspond to either summarized cooling (heating) functions. In other words, the thermodynamics of the gas in the simulations cannot be described by a single set of a cooling plus a heating function with a spatially constant radiation field that could be computed with common tools, such as CLOUDY.
Calculation of the magnon drag force induced by an electric current in ferromagnetic metals

Magnon drag effect induced by an applied electric field in ferromagnetic metals is theoretically studied by a microscopic calculation of the force on magnons arising from magnon emission/absorption and scattering due to driven electrons. It is shown that magnon scattering contribution dominates over the emission/absorption one in a wide temperature regime in good metals with long elastic lifetime $\taue$, as the latter has a relative suppression factor of $(\Deltasd\taue)^{-2}$ due to the electron spin flip by the magnon, where $\Deltasd$ is the $sd$ exchange interaction energy. Spin-transfer efficiency is discussed including the magnon drag effect.
Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures

In our multicultural world, affect-aware AI systems that support humans need the ability to perceive affect across variations in emotion expression patterns across cultures. These systems must perform well in cultural contexts without annotated affect datasets available for training models. A standard assumption in affective computing is that affect recognition models trained and used within the same culture (intracultural) will perform better than models trained on one culture and used on different cultures (intercultural). We test this assumption and present the first systematic study of intercultural affect recognition models using videos of real-world dyadic interactions from six cultures. We develop an attention-based feature selection approach under temporal causal discovery to identify behavioral cues that can be leveraged in intercultural affect recognition models. Across all six cultures, our findings demonstrate that intercultural affect recognition models were as effective or more effective than intracultural models. We identify and contribute useful behavioral features for intercultural affect recognition; facial features from the visual modality were more useful than the audio modality in this study's context. Our paper presents a proof-of-concept and motivation for the future development of intercultural affect recognition systems, especially those deployed in low-resource situations without annotated data.
Towards Privacy-Preserving, Real-Time and Lossless Feature Matching

Most visual retrieval applications store feature vectors for downstream matching tasks. These vectors, from where user information can be spied out, will cause privacy leakage if not carefully protected. To mitigate privacy risks, current works primarily utilize non-invertible transformations or fully cryptographic algorithms. However, transformation-based methods usually fail to achieve satisfying matching performances while cryptosystems suffer from heavy computational overheads. In addition, secure levels of current methods should be improved to confront potential adversary attacks. To address these issues, this paper proposes a plug-in module called SecureVector that protects features by random permutations, 4L-DEC converting and existing homomorphic encryption techniques. For the first time, SecureVector achieves real-time and lossless feature matching among sanitized features, along with much higher security levels than current state-of-the-arts. Extensive experiments on face recognition, person re-identification, image retrieval, and privacy analyses demonstrate the effectiveness of our method. Given limited public projects in this field, codes of our method and implemented baselines are made open-source in https://github.com/IrvingMeng/SecureVector.
Robust Change Detection Based on Neural Descriptor Fields

The ability to reason about changes in the environment is crucial for robots operating over extended periods of time. Agents are expected to capture changes during operation so that actions can be followed to ensure a smooth progression of the working session. However, varying viewing angles and accumulated localization errors make it easy for robots to falsely detect changes in the surrounding world due to low observation overlap and drifted object associations. In this paper, based on the recently proposed category-level Neural Descriptor Fields (NDFs), we develop an object-level online change detection approach that is robust to partially overlapping observations and noisy localization results. Utilizing the shape completion capability and SE(3)-equivariance of NDFs, we represent objects with compact shape codes encoding full object shapes from partial observations. The objects are then organized in a spatial tree structure based on object centers recovered from NDFs for fast queries of object neighborhoods. By associating objects via shape code similarity and comparing local object-neighbor spatial layout, our proposed approach demonstrates robustness to low observation overlap and localization noises. We conduct experiments on both synthetic and real-world sequences and achieve improved change detection results compared to multiple baseline methods. Project web-page: ? http://yilundu.github. io/ndf_change
GoodFATR: A Platform for Automated Threat Report Collection and IOC Extraction

To adapt to a constantly evolving landscape of cyber threats, organizations actively need to collect Indicators of Compromise (IOCs), i.e., forensic artifacts that signal that a host or network might have been compromised. IOCs can be collected through open-source and commercial structured IOC feeds. But, they can also be extracted from a myriad of unstructured threat reports written in natural language and distributed using a wide array of sources such as blogs and social media. This work presents GoodFATR an automated platform for collecting threat reports from a wealth of sources and extracting IOCs from them. GoodFATR supports 6 sources: RSS, Twitter, Telegram, Malpedia, APTnotes, and ChainSmith. Good-FATR continuously monitors the sources, downloads new threat reports, extracts 41 indicator types from the collected reports, and filters generic indicators to output the IOCs. We propose a novel majority-vote methodology for evaluating the accuracy of indicator extraction tools, and apply it to compare 7 popular tools with GoodFATR’s indicator extraction module. We run GoodFATR over 15 months to collect 472,891 reports from the 6 sources; extract 1,043,932 indicators from the reports; and identify 655,971 IOCs. We analyze the collected data to identify the top IOC contributors and the IOC class distribution. Finally, we present a case study on how GoodFATR can assist in tracking cybercrime relations on the Bitcoin blockchain.