Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Apparent Diffusion Coefficient Derived From Diffusion-Weighted Imaging Has the Potential to Avoid Unnecessary MRI-Guided Biopsies of mpMRI-Detected PI-RADS 4 and 5 Lesions

Objective The aim of this study was to evaluate the potential of diffusion-weighted imaging–derived apparent diffusion coefficient (ADC) measurements to obviate unnecessary biopsies in multiparametric MRI–detected PI-RADS 4 and 5 lesions. Materials and Methods This retrospective, institutional review board–approved study investigated 101 PI-RADS 4 and 5 prostate lesions (52 malignant, 49 benign) verified by in-bore MRI-guided biopsy in 101 men (mean age, 62.8 years). Two readers, who were not aware of the biopsy results independently and repeatedly measured minimum, mean, and maximum ADC from diffusion-weighted imaging measurements (in line with PI-RADS v2 recommendations) using a 2-dimensional region of interest drawn around the biopsied lesions. Diagnostic performance was evaluated using receiver operating characteristic statistics and reproducibility statistics were calculated. Results The best diagnostic performance (overall area under the receiver operating characteristic curve [AUC] R1: 0.801; R2: 0.796 peripheral zone AUC R1:0.814, R2: 0.805; transitional zone AUC R1:0.786, R2:0.779) and the tightest limits of interreader agreement (−8.6% to 9.9%) were found in minimum ADC values. Rule-in and rule-out thresholds for diagnosis of prostate cancer were identified, demonstrating a potential to avoid unnecessary biopsies in 32.7% (16/49). Conclusions Quantitative ADC measurement in multiparametric MRI–detected PI-RADS 4 and 5 lesions has the potential to avoid unnecessary MRI-guided biopsies in up to 33%.
Phosphate Binders Derived from Natural Ores Contain Many Kinds of Metallic Elements Besides Their Active Ingredient Metals

Most patients undergoing dialysis are required to take many phosphate binder pills to control hyperphosphatemia. Phosphate binders prescribed in Japan are classified into two types: metal‐based binders (Ca carbonate, lanthanum carbonate, ferric citrate hydrate, and sucroferric oxyhydroxide) and chemically synthesized polymers (sevelamer hydrochloride and bixalomer). The raw materials of metal‐based phosphate binders are natural ores; thus, such binders may contain several other metallic elements. We measured the elemental contents in six metal‐based phosphate binders using an inductively coupled plasma mass – spectrometry (ICP‐MS) method. As a result, despite being in small amounts, ore‐derived phosphate binders contained various elements besides their active ingredient metals: Na, Mg, P, Mn, Fe, Sr, Y, Ba, La, Nd, and Pb in three Ca‐based products; Mg, P, Se, Ce, and Gd in one La‐based product; and Na, Mg, Al, P, Ca, Ti, Cr, Mn, Co, Ni, Ge, Ba, and La in two Fe‐based products. These elements are considered to have originated from pharmaceutical bulk and from pharmaceutical additives. It is unlikely these elements are immediately harmful to patients. However, it should be emphasized that patients undergoing dialysis do not have a urinary excretion route and are administered many phosphate binder pills every day over a long period of time. In the future, pharmaceutical companies may have to disclose standard amounts and/or analytical values regarding the type and quantity of metallic elements in the final formulation or pharmaceutical bulk derived from natural ores.
Translation, cross-cultural adaptation, reliability, and validity of the Identification of Functional Ankle Instability questionnaire in Persian speaking participants with a history of ankle sprain

Abstract Purpose: To cross-culturally translate the Identification of Functional Ankle Instability (IdFAI) questionnaire into Persian and to assess its psychometric properties and factor structure. Methods: The Persian version of IdFAI questionnaire was prepared after a forward–backward translation and cultural adaptation process. One hundred and twenty patients with a history of lateral ankle sprain completed the Persian version of this questionnaire and the Cumberland Ankle Instability Tool (CAIT), Foot and Ankle Ability Measure, Foot and Ankle Outcome Score, Fear Avoidance Belief questionnaire and the Tampa Scale of Kinesiophobia in the first test session. Afterwards, 60 randomly selected patients completed the questionnaires in the second session. Psychometric testing which included test–retest reliability, internal consistency, standard error of measurement (SEM) and minimal metric detectable change (MMDC), weighted kappa coefficient and construct validity were performed using Spearman’s correlation coefficient and confirmatory factor analysis of the three-factor structure of the IdFAI. Results: The interclass correlation coefficient, Cronbach’s alpha, SEM and MMDC were 0.91, 0.95, 2.43, and 6.73 (95% confidence interval, 0.86–0.95) for the IdFAI, respectively. The repeatability of all the questions after one week was rated good to excellent (kappa = 0.60–0.93, p < 0.001). The IdFAI total score had strong correlation with CAIT measure, but had moderate correlation with other questionnaires. The results of factor analysis showed an adequate fit of the model to the data and goodness-of-various fit indices. Conclusions: The Persian version of IdFAI is a reliable and valid tool to identify patients with functional ankle instability which have a history of ankle sprain. Its original three-factor structure was replicated in this study. Implications for Rehabilitation The Persian version of the Identification of Functional Ankle Instability (IdFAI) questionnaire is a reliable and valid instrument in order to identify Iranian patients with functional ankle instability in both clinical practice and research. The Persian IdFAI questionnaire may be considered as a standardized clinical instrument that can be used to classify degree of ankle instability in Iranian Persian-speaking people with a history of lateral ankle sprain. People with a history of ankle sprain can be assessed using IdFAI questionnaire before and after rehabilitative interventions in an attempt to determine any change in their degree of ankle instability over time.
Assigning Personality/Profile to a Chatting Machine for Coherent Conversation Generation

Endowing a chatbot with personality is challenging but significant to deliver more realistic and natural conversations. In this paper, we address the issue of generating responses that are coherent to a pre-specified personality or profile. We present a method that uses generic conversation data from social media (without speaker identities) to generate profile-coherent responses. The central idea is to detect whether a profile should be used when responding to a user post (by a profile detector), and if necessary, select a key-value pair from the profile to generate a response forward and backward (by a bidirectional decoder) so that a personality-coherent response can be generated. Furthermore, in order to train the bidirectional decoder with generic dialogue data, a position detector is designed to predict a word position from which decoding should start given a profile value. Manual and automatic evaluation shows that our model can deliver more coherent, natural, and diversified responses.
Changes in patterns of neural activity underlie a time-dependent transformation of memory in rats and humans

The dynamic process of memory consolidation involves a reorganization of brain regions that support a memory trace over time, but exactly how the network reorganizes as the memory changes remains unclear. We present novel converging evidence from studies of animals (rats) and humans for the time-dependent reorganization and transformation of different types of memory as measured both by behavior and brain activation. We find that context-specific memories in rats, and naturalistic episodic memories in humans, lose precision over time and activity in the hippocampus decreases. If, however, the retrieved memories retain contextual or perceptual detail, the hippocampus is engaged similarly at recent and remote timepoints. As the interval between the timepoint increases, the medial prefrontal cortex is engaged increasingly during memory retrieval, regardless of the context or the amount of retrieved detail. Moreover, these hippocampal-frontal shifts are accompanied by corresponding changes in a network of cortical structures mediating perceptually-detailed as well as less precise, schematic memories. These findings provide cross-species evidence for the crucial interplay between hippocampus and neocortex that reflects changes in memory representation over time and underlies systems consolidation.
Desired, Expected, and Received Support: How Support Gaps Impact Affect Improvement and Perceived Stigma in the Context of Unintended Pregnancy

ABSTRACT Unintended pregnancies constitute potentially stigmatizing moments that mobilize desires for several types of support from multiple members of people’s networks. This study examines gaps among the support people desire, expect, and receive when coping with an unintended pregnancy. The support gaps people experience can influence their feelings about the pregnancy and their perceptions of stigma surrounding it. People (N = 203) who recently experienced an unintended pregnancy reported in an online survey the supportive communication they experienced from their romantic partner, mother/maternal figure, and close friend at the time of the unintended pregnancy. People experienced gaps among the amounts of support they desired, expected, and received, and the nature of gaps varied by the provider and type of support under consideration. Males and females perceived different gaps that varied according to their roles associated with an unintended pregnancy. The gaps people experienced contributed to their perceptions of stigma and affect improvement surrounding the pregnancy.
Code-VEP vs. Eye Tracking: A Comparison Study

Even with state-of-the-art techniques there are individuals whose paralysis prevents them from communicating with others. Brain–Computer-Interfaces (BCI) aim to utilize brain waves to construct a voice for those whose needs remain unmet. In this paper we compare the efficacy of a BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking, among the most popular modalities used. These results, on healthy individuals without paralysis, suggest that while eye tracking works well for some, it does not work well or at all for others; the latter group includes individuals with corrected vision or those who squint their eyes unintentionally while focusing on a task. It is also evident that the performance of the interface is more sensitive to head/body movements when eye tracking is used as the input modality, compared to using c-VEP. Sensitivity to head/body movement could be better in eye tracking systems which are tracking the head or mounted on the face and are designed specifically as assistive devices. The sample interface developed for this assessment has the same reaction time when driven with c-VEP or with eye tracking; approximately 0.5–1 second is needed to make a selection among the four options simultaneously presented. Factors, such as system reaction time and robustness play a crucial role in participant preferences.
Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval

Despite its great success, matrix factorization based cross-modality hashing suffers from two problems: 1) there is no engagement between feature learning and binarization; and 2) most existing methods impose the relaxation strategy by discarding the discrete constraints when learning the hash function, which usually yields suboptimal solutions. In this paper, we propose a novel multimodal hashing framework, referred as Unsupervised Deep Cross-Modal Hashing (UDCMH), for multimodal data search in a self-taught manner via integrating deep learning and matrix factorization with binary latent factor models. On one hand, our unsupervised deep learning framework enables the feature learning to be jointly optimized with the binarization. On the other hand, the hashing system based on the binary latent factor models can generate unified binary codes by solving a discrete-constrained objective function directly with no need for a relaxation step. Moreover, novel Laplacian constraints are incorporated into the objective function, which allow to preserve not only the nearest neighbors that are commonly considered in the literature but also the farthest neighbors of data, even if the semantic labels are not available. Extensive experiments on multiple datasets highlight the superiority of the proposed framework over several state-of-the-art baselines.
Coordination Among Lipid Droplets, Peroxisomes, and Mitochondria Regulates Energy Expenditure Through the CIDE-ATGL-PPARα Pathway in Adipocytes

Metabolic homeostasis is maintained by an interplay among tissues, organs, intracellular organelles, and molecules. Cidea and Cidec are lipid droplet (LD)–associated proteins that promote lipid storage in brown adipose tissue (BAT) and white adipose tissue (WAT). Using ob/ob/Cidea−/−, ob/ob/Cidec−/−, and ob/ob/Cidea−/−/Cidec−/− mouse models and CIDE-deficient cells, we studied metabolic regulation during severe obesity to identify ways to maintain metabolic homeostasis and promote antiobesity effects. The phenotype of ob/ob/Cidea−/− mice was similar to that of ob/ob mice in terms of serum parameters, adipose tissues, lipid storage, and gene expression. Typical lipodystrophy accompanied by insulin resistance occurred in ob/ob/Cidec−/− mice, with ectopic storage of lipids in the BAT and liver. Interestingly, double deficiency of Cidea and Cidec activated both WAT and BAT to consume more energy and to increase insulin sensitivity compared with their behavior in the other three mouse models. Increased lipolysis, which occurred on the LD surfaces and released fatty acids, led to activated β-oxidation and oxidative phosphorylation in peroxisomes and mitochondria in CIDE-deficient adipocytes. The coordination among LDs, peroxisomes, and mitochondria was regulated by adipocyte triglyceride lipase (ATGL)-peroxisome proliferator–activated receptor α (PPARα). Double deficiency of Cidea and Cidec activated energy consumption in both WAT and BAT, which provided new insights into therapeutic approaches for obesity and diabetes.
"Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI

Conversational AI is of growing importance since it enables easy interaction interface between humans and computers. Due to its promising potential and alluring commercial values to serve as virtual assistants and/or social chatbots, major AI, NLP, and Search & Mining conferences are explicitly calling-out for contributions from conversational studies. It is an active research area and of considerable interest. To build a conversational system with moderate intelligence is challenging, and requires abundant dialogue data and interdisciplinary techniques. Along with the Web 2.0, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversations. In general, conversational systems can be categorized into 1) task-oriented systems which aim to help users accomplish goals in vertical domains, and 2) social chat bots which can converse seamlessly and appropriately with humans, playing the role of a chat companion. In this paper, we focus on the survey of non-task-oriented chit-chat bots.
Biased Random Walk based Social Regularization for Word Embeddings

Nowadays, people publish a lot of natural language texts on social media. Socialized word embeddings (SWE) has been proposed to deal with two phenomena of language use: everyone has his/her own personal characteristics of language use and socially connected users are likely to use language in similar ways. We observe that the spread of language use is transitive. Namely, one user can affect his/her friends and the friends can also affect their friends. However, SWE modeled the transitivity implicitly. The social regularization in SWE only applies to one-hop neighbors and thus users outside the one-hop social circle will not be affected directly. In this work, we adopt random walk methods to generate paths on the social graph to model the transitivity explicitly. Each user on a path will be affected by his/her adjacent user(s) on the path. Moreover, according to the update mechanism of SWE, fewer friends a user has, fewer update opportunities he/she can get. Hence, we propose a biased random walk method to provide these users with more update opportunities. Experiments show that our random walk based social regularizations perform better on sentiment classification.
Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding

This paper focuses on scene graph completion which aims at predicting new relations between two entities utilizing existing scene graphs and images. By comparing with the well-known knowledge graph, we first identify that each scene graph is associated with an image and each entity of a visual triple in a scene graph is composed of its entity type with attributes and grounded with a bounding box in its corresponding image. We then propose an end-to-end model named Representation Learning via Jointly Structural and Visual Embedding (RLSV) to take advantages of structural and visual information in scene graphs. In RLSV model, we provide a fully-convolutional module to extract the visual embeddings of a visual triple and apply hierarchical projection to combine the structural and visual embeddings of a visual triple. In experiments, we evaluate our model on two scene graph completion tasks: link prediction and visual triple classification, and further analyze by case studies. Experimental results demonstrate that our model outperforms all baselines in both tasks, which justifies the significance of combining structural and visual information for scene graph completion.
The Evolution of Price Competition Game on Complex Networks

The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.
Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; the optimal number of clusters for representing residential electricity use profiles is determined; and an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.
A Novel Strategy for Active Task Assignment in Crowd Labeling

Active learning strategies are often used in crowd labeling to improve task assignment. However, these strategies require prohibitive computation time yet still cannot improve the assignment to the utmost, because they simply evaluate each possible assignment and then greedily select the optimal one. In this paper, we first derive an efficient algorithm for assignment evaluation. Then, to overcome the uncertainty of labels, we develop a novel strategy that modulates the scope of the greedy task assignment with posterior uncertainty and keeps the evaluation optimistic. The experiments on two popular worker models and four MTurk datasets show that our strategy achieves the best performance and highest computation efficiency.
Algorithms For Treatment of Major Depressive Disorder: Efficacy and Cost-Effectiveness

Abstract In spite of multiple new treatment options, chronic and treatment refractory courses still are a major challenge in the treatment of depression. Providing algorithm-guided antidepressant treatments is considered an important strategy to optimize treatment delivery and avoid or overcome treatment-resistant courses of major depressive disorder (MDD). The clinical benefits of algorithms in the treatment of inpatients with MDD have been investigated in large-scale, randomized controlled trials. Results showed that a stepwise treatment regimen (algorithm) with critical decision points at the end of each treatment step based on standardized and systematic measurements of response and an algorithm-guided decision-making process increases the chances of achieving remission and optimizes prescription behaviors for antidepressants. In conclusion, research in MDD revealed that systematic and structured treatment procedures, the diligent assessment of response at critical decision points, and timely dose and treatment type adjustments make the substantial difference in treatment outcomes between algorithm-guided treatment and treatment as usual.
Scanpath Prediction for Visual Attention using IOR-ROI LSTM

Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.