Miracle-dz/UniFAR-FTUs huggingface dataset:
Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes

Recognizing unseen classes is an important task for real-world applications, due to: 1) it is common that some classes in reality have no labeled image exemplar for training; and 2) novel classes emerge rapidly. Recently, to address this task many zero-shot learning (ZSL) approaches have been proposed where explicit linear scores, like inner product score, are employed to measure the similarity between a class and an image. We argue that explicit linear scoring (ELS) seems too weak to capture complicated image-class correspondence. We propose a simple yet effective framework, called Implicit Non-linear Similarity Scoring (ICINESS). In particular, we train a scoring network which uses image and class features as input, fuses them by hidden layers, and outputs the similarity. Based on the universal approximation theorem, it can approximate the true similarity function between images and classes if a proper structure is used in an implicit non-linear way, which is more flexible and powerful. With ICINESS framework, we implement ZSL algorithms by shallow and deep networks, which yield consistently superior results.
Detection of the Mitochondrial Membrane Potential by the Cationic Dye JC-1 in L1210 Cells with Massive Overexpression of the Plasma Membrane ABCB1 Drug Transporter

JC-1, a cationic fluorescent dye when added to living cells, is known to be localized exclusively in mitochondria, particularly in good physiological conditions characterized by sufficient mitochondrial membrane potential (ΔΨ). The accumulation of JC-1 in these organelles leads to the formation J-aggregates (with a specific red fluorescence emission maximum at 590 nm), which is in addition to the typical green fluorescence of J-monomers (emission maximum of ∼529 nm). The lack of mitochondrial ΔΨ leads to the depression of JC-1 mitochondrial accumulation and a decrease in J-aggregate formation. Therefore, the ratio between the red and green fluorescence of cells loaded with JC-1 is often used for the detection of the mitochondrial membrane potential. However, JC-1 represents a suitable substrate of the multidrug transporter P-glycoprotein (P-gp). Therefore, the depression of the JC-1 content in intracellular space and particularly in the mitochondria to a level that is inefficient for J-aggregate formation could be expected in P-gp-positive cells. In the current paper, we proved this behavior on parental P-gp-negative L1210 (S) cells and their P-gp-positive variants obtained by either selection with vincristine (R) or transfection with the human gene encoding P-gp (T). P-glycoprotein inhibitors cyclosporine A and verapamil fail to restore JC-1 loading of the R and T cells to an extent similar to that observed in S cells. In contrast, the noncompetitive high affinity P-gp inhibitor tariquidar fully restored JC-1 accumulation and the presence of the typical red fluorescence of J-aggregates. In the presence of tariquidar, measurement of the JC-1 fluorescence revealed similar levels of mitochondrial membrane potential in P-gp-negative (S) and P-gp-positive cells (R and T).
Learning Portable Symbolic Representations

An open question in artificial intelligence is how to learn useful representations of the real world. One approach is to learn symbols, which represent the world and its contents, as well as models describing the effects on these symbols when interacting with the world. To date, however, research has investigated learning such representations for a single specific task. Our research focuses on approaches to learning these models in a domain-independent manner. We intend to use these symbolic models to build even higher levels of abstraction, creating a hierarchical representation which could be used to solve complex tasks. This would allow an agent to gather knowledge over the course of its lifetime, which could then be leveraged when faced with a new task, obviating the need to relearn a model every time a new unseen problem is encountered.
Recent Development of Genetic Code Expansion for Posttranslational Modification Studies

Nowadays advanced mass spectrometry techniques make the identification of protein posttranslational modifications (PTMs) much easier than ever before. A series of proteomic studies have demonstrated that large numbers of proteins in cells are modified by phosphorylation, acetylation and many other types of PTMs. However, only limited studies have been performed to validate or characterize those identified modification targets, mostly because PTMs are very dynamic, undergoing large changes in different growth stages or conditions. To overcome this issue, the genetic code expansion strategy has been introduced into PTM studies to genetically incorporate modified amino acids directly into desired positions of target proteins. Without using modifying enzymes, the genetic code expansion strategy could generate homogeneously modified proteins, thus providing powerful tools for PTM studies. In this review, we summarized recent development of genetic code expansion in PTM studies for research groups in this field.
Abstraction of Agents Executing Online and their Abilities in the Situation Calculus

We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog. We assume that we have both a high-level action theory and a low-level one that represent the agent's behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so.
Algorithms or Actions? A Study in Large-Scale Reinforcement Learning

Large state and action spaces are very challenging to reinforcement learning. However, in many domains there is a set of algorithms available, which estimate the best action given a state. Hence, agents can either directly learn a performance-maximizing mapping from states to actions, or from states to algorithms. We investigate several aspects of this dilemma, showing sufficient conditions for learning over algorithms to outperform over actions for a finite number of training iterations. We present synthetic experiments to further study such systems. Finally, we propose a function approximation approach, demonstrating the effectiveness of learning over algorithms in real-time strategy games.
Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection

Stochastic Boolean satisfiability (SSAT) is an expressive language to formulate decision problems with randomness. Solving SSAT formulas has the same PSPACE-complete computational complexity as solving quantified Boolean formulas (QBFs). Despite its broad applications and profound theoretical values, SSAT has received relatively little attention compared to QBF. In this paper, we focus on exist-random quantified SSAT formulas, also known as E-MAJSAT, which is a special fragment of SSAT commonly applied in probabilistic conformant planning, posteriori hypothesis, and maximum expected utility. Based on clause selection, a recently proposed QBF technique, we propose an algorithm to solve E-MAJSAT. Moreover, our method can provide an approximate solution to E-MAJSAT with a lower bound when an exact answer is too expensive to compute. Experiments show that the proposed algorithm achieves significant performance gains and memory savings over the state-of-the-art SSAT solvers on a number of benchmark formulas, and provides useful lower bounds for cases where prior methods fail to compute exact answers.
Deletion of the Fanconi Anemia C Gene in Mice Leads to Skeletal Anomalies and Defective Bone Mineralization and Microarchitecture

Fanconi anemia (FA) is a rare genetic disorder associated with a progressive decline in hematopoietic stem cells leading to bone marrow failure. FA is also characterized by a variety of developmental defects including short stature and skeletal malformations. More than half of children affected with FA have radial‐ray abnormalities, and many patients have early onset osteopenia/osteoporosis. Although many Fanconi anemia genes have been identified and a molecular pathway defined, the underlying mechanism leading to bone defects remains elusive. To understand the role of FA genes in skeletal development and bone microarchitecture, we evaluated bone physiology during embryogenesis and in adult FancA‐ and FancC‐deficient mice. We found that both FancA‐/‐ and FancC‐/‐ embryos have abnormal skeletal development shown by skeletal malformations, growth delay, and reduced bone mineralization. FancC‐/‐ adult mice present altered bone morphology and microarchitecture with a significant decrease in cortical bone mineral density in a sex‐specific manner. Mechanical testing revealed that male but not female FancC‐/‐ mice show reduced bone strength compared with their wild‐type littermates. Ex vivo cultures showed that FancA‐/‐ and FancC‐/‐ bone marrow–derived mesenchymal stem cells (BMMSC) have impaired differentiation capabilities together with altered gene expression profiles. Our results suggest that defective bone physiology in FA occurs in utero and possibly results from altered BMMSC function. These results provide valuable insights into the mechanism involved in FA skeletal defects. © 2018 American Society for Bone and Mineral Research.
DehazeGAN: When Image Dehazing Meets Differential Programming

Single image dehazing has been a classic topic in computer vision for years. Motivated by the atmospheric scattering model, the key to satisfactory single image dehazing relies on an estimation of two physical parameters, i.e., the global atmospheric light and the transmission coefficient. Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality. Inspired by differentiable programming, we re-formulate the atmospheric scattering model into a novel generative adversarial network (DehazeGAN). Such a reformulation and adversarial learning allow the two parameters to be learned simultaneously and automatically from data by optimizing the final dehazing performance so that clean images with faithful color and structures are directly produced. Moreover, our reformulation also greatly improves the GAN’s interpretability and quality for single image dehazing. To the best of our knowledge, our method is one of the first works to explore the connection among generative adversarial models, image dehazing, and differentiable programming, which advance the theories and application of these areas. Extensive experiments on synthetic and realistic data show that our method outperforms state-of-the-art methods in terms of PSNR, SSIM, and subjective visual quality.
SPECIAL REVIEW: PLATELETS AND CANCER—MECHANISM-BASED STRATEGY FOR CANCER PREVENTION / THERAPEUTICS TARGETING PLATELETS Aspirin, platelet inhibition and cancer prevention

Several lines of evidence are consistent with the hypothesis that activated platelets contribute to colorectal tumorigenesis and metastatization through direct cell–cell interactions and the release of different lipid and protein mediators, and microvesicles. This review examines the clinical pharmacology of low-dose aspirin as a basis for discussing the mechanisms underlying the contribution of platelets to neoplastic transformation and progression of cancer via the development of metastases.
Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification

We aim to significantly reduce the computational cost for classification of temporally untrimmed videos while retaining similar accuracy. Existing video classification methods sample frames with a predefined frequency over entire video. Differently, we propose an end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames like what we do. We make two main contributions. First, information is not equally distributed in video frames along time. An agent needs to watch more carefully when a clip is informative and skip the frames if they are redundant or irrelevant. The proposed approach enables the agent to adapt sampling rate to video content and skip most of the frames without the loss of information. Second, in order to have a confident decision, the number of frames that should be watched by an agent varies greatly from one video to another. We incorporate an adaptive stop network to measure confidence score and generate timely trigger to stop the agent watching videos, which improves efficiency without loss of accuracy. Our approach reduces the computational cost significantly for the large-scale YouTube-8M dataset, while the accuracy remains the same.
Three-Head Neural Network Architecture for Monte Carlo Tree Search

AlphaGo Zero pioneered the concept of two-head neural networks in Monte Carlo Tree Search (MCTS), where the policy output is used for prior action probability and the state-value estimate is used for leaf node evaluation. We propose a three-head neural net architecture with policy, state- and action-value outputs, which could lead to more efficient MCTS since neural leaf estimate can still be back-propagated in tree with delayed node expansion and evaluation. To effectively train the newly introduced action-value head on the same game dataset as for two-head nets, we exploit the optimal relations between parent and children nodes for data augmentation and regularization. In our experiments for the game of Hex, the action-value head learning achieves similar error as the state-value prediction of a two-head architecture. The resulting neural net models are then combined with the same Policy Value MCTS (PV-MCTS) implementation. We show that, due to more efficient use of neural net evaluations, PV-MCTS with three-head neural nets consistently performs better than the two-head ones, significantly outplaying the state-of-the-art player MoHex-CNN.
Curriculum Learning for Natural Answer Generation

By reason of being able to obtain natural language responses, natural answers are more favored in real-world Question Answering (QA) systems. Generative models learn to automatically generate natural answers from large-scale question answer pairs (QA-pairs). However, they are suffering from the uncontrollable and uneven quality of QA-pairs crawled from the Internet. To address this problem, we propose a curriculum learning based framework for natural answer generation (CL-NAG), which is able to take full advantage of the valuable learning data from a noisy and uneven-quality corpus. Specifically, we employ two practical measures to automatically measure the quality (complexity) of QA-pairs. Based on the measurements, CL-NAG firstly utilizes simple and low-quality QA-pairs to learn a basic model, and then gradually learns to produce better answers with richer contents and more complete syntaxes based on more complex and higher-quality QA-pairs. In this way, all valuable information in the noisy and uneven-quality corpus could be fully exploited. Experiments demonstrate that CL-NAG outperforms the state-of-the-arts, which increases 6.8% and 8.7% in the accuracy for simple and complex questions, respectively.
Endoplasmic reticulum stress and kidney dysfunction

Chronic kidney disease (CKD) affects millions of persons worldwide and constitutes a major public health problem. Therefore, understanding the molecular basis of CKD is a key challenge for the development of preventive and therapeutic strategies. A major contributor to chronic histological damage associated with CKD is acute kidney injury (AKI). At the cellular level, kidney injuries are associated with microenvironmental alterations, forcing cells to activate adaptive biological processes that eliminate the stressor and generate alarm signals. These signalling pathways actively participate in tissue remodelling by promoting inflammation and fibrogenesis, ultimately leading to CKD. Many stresses that are encountered upon kidney injury are prone to trigger endoplasmic reticulum (ER) stress. In the kidney, ER stress both participates in acute and chronic histological damages, but also promotes cellular adaptation and nephroprotection. In this review, we will discuss the implication of ER stress in the pathophysiology of AKI and CKD progression, and we will give a critical analysis of the current experimental and clinical evidence that support ER stress as a mediator of kidney damage.
Variable-Delay Controllability

In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms. We conclude by providing empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems.
Using multiple choice questions to examine the content of the qualifying law degree accurately and reliably: the experience of the Qualified Lawyers Transfer Scheme

The Qualified Lawyers Transfer Scheme (QLTS) provides a route for lawyers from other jurisdictions and barristers from England and Wales to qualify as solicitors in England and Wales. One of the three tests in the QLTS, the Multiple Choice Test (MCT), uses multiple choice questions to examine the syllabus of the qualifying law degree together with some pervasive subjects from the Legal Practice Course. This paper examines the MCT in detail. Particular attention is paid to the format and structure of the questions. We describe the detailed editing which each question goes through before it is used, the statistical analysis and review which take place after an exam, and the rationale for these processes. An explanation is given of what reliability and accuracy mean and how they are measured statistically. There is also an explanation of the “Angoff method” by which pass marks are set. Finally, the paper reports on the first four sittings of the MCT and their statistical results including their reliability and accuracy. Use of the multiple choice test, though well established for examining applied knowledge in medicine and in law in other jurisdictions, is a radical departure for the assessment of law in England and Wales. The experience of QLTS has shown that a carefully constructed multiple choice test of a suitable length can assess the qualifying law degree content both reliably and accurately.
Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI? s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI? s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.
SynKit: LTL Synthesis as a Service

Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.
A Tolerogenic Artificial APC Durably Ameliorates Experimental Autoimmune Encephalomyelitis by Directly and Selectively Modulating Myelin Peptide–Autoreactive CD4+ and CD8+ T Cells

In this study, a tolerogenic artificial APC (TaAPC) was developed to directly and selectively modulate myelin-autoreactive CD4+ and CD8+ T cells in the myelin oligodendrocyte glycoprotein (MOG)35–55 peptide–induced experimental autoimmune encephalomyelitis in C57BL/6J mice. Cell-sized polylactic-coglycolic acid microparticles were generated to cocouple target Ags (MOG40–54/H-2Db-Ig dimer, MOG35–55/I-Ab multimer), regulatory molecules (anti-Fas and PD-L1-Fc), and “self-marker” CD47-Fc and encapsulate inhibitory cytokine (TGF-β1). Four infusions of the TaAPCs markedly and durably inhibited the experimental autoimmune encephalomyelitis progression and reduced the local inflammation in CNS tissue. They circulated throughout vasculature into peripheral lymphoid tissues and various organs, but not into brain, with retention of 36 h and exerted direct effects on T cells in vivo and in vitro. Two infusions of the TaAPCs depleted 65–79% of MOG35–55-specific CD4+ and 46–62% of MOG40–54-specific CD8+ T cells in peripheral blood, spleen, and CNS tissues in an Ag-specific manner and regulatory molecule–dependent fashion; induced robust T cell apoptosis; inhibited the activation and proliferation of MOG peptide–reactive T cells; reduced MOG peptide–reactive Th1, Th17, and Tc17 cells; and expanded regulatory T cells. They also inhibited IFN-γ/IL-17A secretion and elevated IL-10/TGF-β1 production in splenocytes but not in CNS tissue. More importantly, the TaAPCs treatment did not obviously suppress the overall immune function of host. To our knowledge, this study provides the first experimental evidence for the capability of TaAPCs to directly modulate autoreactive T cells by surface presentation of multiple ligands and paracrine release of cytokine, thus suggesting a novel Ag-specific immunotherapy for the T cell–mediated autoimmune diseases.