Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerated Discovery of Sustainable Building Materials

Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements. Recent advances in artificial intelligence have enabled machines to generate highly plausible artifacts, such as images of realistic looking faces. Semi-supervised generative models allow generation of artifacts with specific, desired characteristics. In this work, we use Conditional Variational Autoencoders (CVAE), a type of semi-supervised generative model, to discover concrete formulas with desired properties. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate CVAEs can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.
The carboxy-terminus, a key regulator of protein function

Abstract All proteins end with a carboxyl terminus that has unique biophysical properties and is often disordered. Although there are examples of important C-termini functions, a more global role for the C-terminus is not yet established. In this review, we summarize research on C-termini, a unique region in proteins that cells exploit. Alternative splicing and proteolysis increase the diversity of proteins and peptides in cells with unique C-termini. The C-termini of proteins contain minimotifs, short peptides with an encoded function generally characterized as binding, posttranslational modifications, and trafficking. Many of these activities are specific to minimotifs on the C-terminus. Approximately 13% of C-termini in the human proteome have a known minimotif, and the majority, if not all of the remaining termini have conserved motifs inferring a function that remains to be discovered. C-termini, their predictions, and their functions are collated in the C-terminome, Proteus, and Terminus Oriented Protein Function INferred Database (TopFIND) database/web systems. Many C-termini are well conserved, and some have a known role in health and disease. We envision that this summary of C-termini will guide future investigation of their biochemical and physiological significance.
Microblog Hashtag Generation via Encoding Conversation Contexts

Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces

Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example, variational autoencoders with autoregressive decoders often collapse into autodecoders, where they learn to ignore the encoder input. In this work, we demonstrate that adding an auxiliary decoder to regularize the latent space can prevent this collapse, but successful auxiliary decoding tasks are domain dependent. Auxiliary decoders can increase the amount of semantic information encoded in the latent space and visible in the reconstructions. The semantic information in the variational autoencoder's representation is only weakly correlated with its rate, distortion, or evidence lower bound. Compared to other popular strategies that modify the training objective, our regularization of the latent space generally increased the semantic information content.
Genetic differentiation in the southern population of the Fathead Minnow Pimephales promelas Rafinesque (Actinopterygii: Cyprinidae)

The North American cyprinid Pimephales promelas is a species with a wide distribution range, occurring in distinct hydrographic basins in Mexico, Canada, and the United States. Previous morphological and meristic analyses of P. promelas concluded that at least three subspecies exist in the midwestern and northeast region of the United States. No studies have been carried out on the Mexican population of P. promelas, but the findings of cryptic diversity in United States populations of this species, as well as in other codistributed fish species in Mexico could be an indication that Mexican populations of P. promelas consist of cryptic species. Using the mitochondrial gene cyt b and the first intron of the S7 ribosomal protein-coding nuclear gene we carried out phylogenetic and phylogeographic analyses of populations of P. promelas across its distribution range in northwestern Mexico. Using this information were analyzed the structure and differentiation level between populations of P. promelas from distinct river basins in the region in identifying cryptic diversity. Twenty-four sequences were obtained for cyt b, and 30 for S7, which included the two heterozygous alleles. The results revealed the existence of four well-differentiated lineages: (1) Yaqui in the Pacific slope; (2) Santa Maria, and (3) Casas Grandes in the Guzman Basin; and (4) Nazas+Conchos in Chihuahua state. This challenges the current taxonomy of P. promelas. Differences in the relationships between markers and the small sample size for the Santa Maria population (n = 1), indicate that our results must be corroborated with more data and morphological analyses. Biogeographic analysis of these findings suggest that the evolutionary history of P. promelas is associated with the fragmentation of the ancestral Rio Grande river system since Miocene in northwestern Mexico consistent with findings for codistributed fish species.
Gene–environment interactions between HPA-axis genes and stressful life events in depression: a systematic review

Abstract Objective: Depression is a disorder caused by genetics and environmental factors. The aim of this study was to perform a review investigating the interaction between genetic variations located in genes involved in hypothalamus–pituitary–adrenal axis (HPA-axis) and stressful life events (SLEs) in depression. Methods: In this systematic review, we selected articles investigating the interaction between genes involved in the HPA-axis, such as Arginine Vasopressin (AVP), Angiotensin Converting Enzyme (ACE), Corticotrophin Releasing Hormone (CRH), Corticotrophin Releasing Hormone Receptor 1 (CRHR1), Corticotrophin Releasing Hormone Receptor 2 (CRHR2), FK506 binding protein (FKBP5), Nuclear Receptor subfamily 3 group C member 1 (NR3C1), Nuclear Receptor subfamily 3 group C member 2 (NR3C2), and SLE. The literature search was conducted using the Pubmed, Embase, and PsychINFO databases in adherence with the PRISMA guidelines. Results: The search yielded 48 potentially relevant studies, of which 40 were excluded following screening. Eight studies were included in the final review. A total of 97 single nucleotide polymorphisms (SNPs) were examined in the eight included studies. The most prevalent gene was FKBP5, and the best studied polymorphism was FKBP5:rs1360780. Two of the five studies reported significant gene–environment (G × E) interactions between rs1360780 and SLE. Overall, four studies reported significant G × E interactions between FKBP5, CRH, or CRHR1 and SLE, respectively. No significant G × E interactions were found for the remaining genes. Conclusions: Our results suggest that genetic variation in three genes in the HPA-axis possibly moderate the effects of SLEs in depression.
From clinic to mechanism: Proteomics‐based assessment of angiogenesis in adrenal pheochromocytoma

Adrenal pheochromocytoma (PCC) is a very rare tumor that stems from chromaffin cells, which can develop into malignant tumor. During the operation, abundant blood vessels were often observed in PCC than other adrenal tumors, which increases the difficulty and risk of the surgery. Therefore, it is important to investigate the mechanism of PCC angiogenesis. Twelve surgical specimens of PCC from Ruijin Hospital, Shanghai Jiaotong University were grouped into high and low microvessel density (MVD) group. They were also divided into rich blood supply and nonenriched blood supply group, according to computed tomography (CT) manifestation. Comparative proteomic analysis based on liquid chromatography–tandem mass spectrometry (LC–MS/MS) and bioinformatics analysis revealed that 206 proteins differentially regulated in the high MVD group compared with low MVD group (p < 0.05). Besides, 61 proteins were discovered to be significantly changed when the 12 samples were grouped according to CT manifestation. By intersecting the differentially changed protein from MVD and CT grouping, 25 proteins were filtered out, with pathological function. COX4I2 was verified to be increased gradually with angiogenesis with increasing severity, and PLAT was shown to be decreased with angiogenesis in PCC, by quantitative reverse‐transcription polymerase chain reaction and immunohistochemistry. The quantitative proteomics result indicated that the tumor angiogenesis in PCC is associated with hypoxia. COX4I2 and PLAT were highly correlated with blood supply in PCC which contribute to angiogenesis in PCC, which could be used as biomarkers to better indicate tumor angiogenesis, or targets to regress tumor angiogenesis as treatment.
Fast Regularity-Constrained Plane Reconstruction

Man-made environments typically comprise planar structures that exhibit numerous geometric relationships, such as parallelism, coplanarity, and orthogonality. Making full use of these relationships can considerably improve the robustness of algorithmic plane reconstruction of complex scenes. This research leverages a constraint model requiring minimal prior knowledge to implicitly establish relationships among planes. We introduce a method based on energy minimization to reconstruct the planes consistent with our constraint model. The proposed algorithm is efficient, easily to understand, and simple to implement. The experimental results show that our algorithm successfully reconstructs planes under high percentages of noise and outliers. This is superior to other state-of-the-art regularity-constrained plane reconstruction methods in terms of speed and robustness.
BERTSel: Answer Selection with Pre-trained Models

Recently, pre-trained models have been the dominant paradigm in natural language processing. They achieved remarkable state-of-the-art performance across a wide range of related tasks, such as textual entailment, natural language inference, question answering, etc. BERT, proposed by Devlin this http URL. , has achieved a better marked result in GLUE leaderboard with a deep transformer architecture. Despite its soaring popularity, however, BERT has not yet been applied to answer selection. This task is different from others with a few nuances: first, modeling the relevance and correctness of candidates matters compared to semantic relatedness and syntactic structure; second, the length of an answer may be different from other candidates and questions. In this paper. we are the first to explore the performance of fine-tuning BERT for answer selection. We achieved STOA results across five popular datasets, demonstrating the success of pre-trained models in this task.
simTPM: User-centric TPM for Mobile Devices (Technical Report)

Trusted Platform Modules are valuable building blocks for security solutions and have also been recognized as beneficial for security on mobile platforms, like smartphones and tablets. However, strict space, cost, and power constraints of mobile devices prohibit an implementation as dedicated onboard chip and the incumbent implementations are software TPMs protected by Trusted Execution Environments. In this paper, we present simTPM, an alternative implementation of a mobile TPM based on the SIM card available in mobile platforms. We solve the technical challenge of implementing a TPM2.0 in the resource-constrained SIM card environment and integrate our simTPM into the secure boot chain of the ARM Trusted Firmware on a HiKey960 reference board. Most notably, we address the challenge of how a removable TPM can be bound to the host device’s root of trust for measurement. As such, our solution not only provides a mobile TPM that avoids additional hardware while using a dedicated, strongly protected environment, but also offers promising synergies with co-existing TEE-based TPMs. In particular, simTPM offers a user-centric trusted module. Using performance benchmarks, we show that our simTPM has competitive speed with a reported TEE-based TPM and a hardware-based TPM.
Sharp Bounds for Oscillatory Integral Operators with Homogeneous Polynomial Phases

Abstract We obtain sharp $L^{p}$ bounds for oscillatory integral operators with generic homogeneous polynomial phases in several variables. The phases considered in this paper satisfy the rank one condition that is an important notion introduced by Greenleaf, Pramanik, and Tang. Under certain additional assumptions, we can establish sharp damping estimates with critical exponents to prove endpoint $L^{p}$ estimates.
An intruder detection algorithm for vision based sense and avoid system

Intruder detection is a crucial problem in vision based Sense and Avoid (SAA) system. In this paper a deep feature learning based intruder detection algorithm is proposed. The intruder detection algorithm contains four parts: obtaining the test samples, creating the overcomplete dictionary, deep feature learning and determining the region of the intruder. The sliding window technique is adopted to obtain the test samples. The K-means Singular Value Decomposition (K-SVD) is used for overcomplete dictionary training. We employ the deep feature learning method on the basis of the dictionary for feature extraction. The support vector machine (SVM) is used to select the region of interest (ROI), and the region of the intruder is finally determined by merging the overlapping ROIs. The experiment results indicate that the algorithm is robust under different weather and illumination conditions and different angles of view.
Investigating the conformational response of the Sortilin receptor upon binding endogenous peptide- and protein ligands by HDX-MS

Sortilin is a multifunctional transmembrane neuronal receptor involved in sorting of neurotrophic factors and apoptosis signalling. So far, structural characterization of Sortilin and its endogenous ligands has been limited to crystallographic studies of Sortilin in complex with the neuropeptide Neurotensin. Here, we use hydrogen/deuterium exchange mass spectrometry to investigate the conformational response of Sortilin to binding biological ligands including the peptides Neurotensin and the Sortilin propeptide and the proteins Progranulin and pro-Nerve growth factor-β. The results show that the ligands employ two binding sites inside the cavity of the β-propeller of Sortilin. However, ligands have distinct differences in their conformational impact on the receptor. Interestingly, the protein ligands induce conformational stabilization in a remote membrane-proximal domain, hinting at an unknown conformational link between the ligand binding region and this membrane-proximal region of Sortilin. Our findings improves our molecular understanding of Sortilin and how it mediates diverse ligand-dependent functions important in neurobiology.
Incidence, clinical features, treatment and outcome of primary central nervous system lymphoma in Norway A ten-year national survey

The incidence of primary central nervous system lymphoma (PCNSL) has been reported to increase in some parts of the world, while being stable in other regions. In an attempt to characterize the incidence rate, clinical features, treatment, outcome, and prognostic factors of PCNSL in Norway, we report our experience in a large unselected series of patients. Clinical features, histological diagnosis, radiological findings, treatment, and outcome of all patients diagnosed with PCNSL in Norway in the years 1989–1998 were registered. During the 10-year period 58 new cases of histologically verified PCNSL were registered in Norway. The annual incidence rate of PCNSL was on average 1.34 cases per million people with a non-significant increasing trend (p=0.069). For patients diagnosed before death (n=45) the estimated survival following histological diagnosis was 55%, 47%, and 23% at 1, 2, and 5 years, respectively. In Cox-regression analysis age, WHO performance status and treatment had independent prognostic impact on survival. In the studied decade, there was a non-significant trend towards increased incidence of PCNSL, perhaps due to increased availability of diagnostic imaging, especially magnetic resonance imaging.
Patch-based 3D Human Pose Refinement

State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run. In this paper, we develop a post-processing step to refine 3D human pose estimation from body part patches. Using local patches as input has two advantages. First, the fine details around body parts are zoomed in to high resolution for preciser 3D pose prediction. Second, it enables the part appearance to be shared between poses to benefit rare poses. In order to acquire informative representation of patches, we explore different input modalities and validate the superiority of fusing predicted segmentation with RGB. We show that our method consistently boosts the accuracy of state-of-the-art 3D human pose methods.
Nanoscale Communication: State-of-Art and Recent Advances

The engineering community is witnessing a new frontier in the communication industry. Among others, the tools provided by nanotechnologies enable the development of novel nanosensors and nanomachines. On the one hand, nanosensors are capable of detecting events with unprecedented accuracy. On the other hand, nanomachines are envisioned to accomplish tasks ranging from computing and data storing to sensing and actuation. Recently, in vivo wireless nanosensor networks (iWNSNs) have been presented to provide fast and accurate disease diagnosis and treatment. These networks are capable of operating inside the human body in real time and will be of great benefit for medical monitoring and medical implant communication. Despite the fact that nanodevice technology has been witnessing great advancements, enabling the communication among nanomachines is still a major challenge.
Frame measures for infinitely many measures

For every frame spectral measure $ \mu $, there exists a discrete measure $ \nu $ as a frame measure. Since if $ \mu $ is not a frame spectral measure, then there is not any general statement about the existence of frame measures $ \nu $ for $ \mu $, we were motivated to examine Bessel and frame measures. We construct infinitely many measures $ \mu $ which admit frame measures $ \nu $, and we show that there exist infinitely many frame spectral measures $ \mu $ such that besides having a discrete frame measure, they admit continuous frame measures too.
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs

Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making. We model patient-clinician interactions as partially observable Markov decision processes (POMDPs) and optimize sequential treatment based on belief states inferred from history sequence. To facilitate inference, we build a variational generative model and boost state representation with a recurrent neural network (RNN), incorporating an auxiliary loss from sequence auto-encoding. Meanwhile, we optimize a continuous policy of drug levels with an actor-critic method where policy gradients are obtained from a stablized off-policy estimate of advantage function, with the value of belief state backed up by parallel best-first suffix trees. We exploit our methodology in optimizing dosages of vasopressor and intravenous fluid for sepsis patients using a retrospective intensive care dataset and evaluate the learned policy with off-policy policy evaluation (OPPE). The results demonstrate that modelling as POMDPs yields better performance than MDPs, and that incorporating heuristic search improves sample efficiency.
Understanding adherence to hand hygiene recommendations: the theory of planned behavior.

BACKGROUND Most health care workers (HCWs) are aware of the rationale for hand hygiene procedures, yet failure to adhere to guidelines is common. Little is known about factors that motivate HCWs to practice hand hygiene. PURPOSE The purposes of this study were to (1) estimate adherence to hand hygiene recommendations; (2) describe relationships among motivational factors, adherence, and intensity of nursing unit activity; and (3) test an explanatory model for adherence to hand hygiene guidelines based on the theory of planned behavior (TPB). METHOD A longitudinal, observational design was used to collect data from 120 registered nurses employed in critical care and postcritical care units. Nurses provided information about motivational factors and intentions and a self-report of the proportion of time they followed guidelines. At least 2 weeks later, the nurses' hand hygiene performance was observed while they provided patient care. Structural equation modeling was used to test the TPB-based model. RESULTS Rate of adherence to recommendations for 1248 hand hygiene indications was 70%. The correlation between self-reported and observed adherence to handwashing recommendations was low (r = 0.21). TPB variables predicted intention to handwash, and intention was related to self-reported hand hygiene. Intensity of activity in the nursing unit, rather than TPB variables, predicted observed adherence to hand hygiene recommendations. CONCLUSIONS The limited association between self-reported and observed hand hygiene scores remains an enigma to be explained. Actual hand hygiene behavior may be more sensitive to the intensity of work activity in the clinical setting than to internal motivational factors.