Miracle-dz/UniFAR-FTUs huggingface dataset:
AO-Net: Efficient Neural Network for Ambient Occlusion

Screen space based ambient occlusion is widely applied in real-time 3D applications due to its high efficiency, however, it frequently exhibits artifacts including banding and blurring. In this paper, we propose AO-Net, a learning-based method for fast and high-quality ambient occlusion generation. Our neural network is built upon kernel prediction-based architecture with careful input screen space feature selection, leading to a light-weight and compact solution. Experiment results indicate that our approach can achieve visual quality to a level in comparable with ray-traced solutions, meanwhile maintaining real-time performance. In addition, our method can be easily integrated into existing rendering pipelines and shows robustness for unseen scenes.
Relationship between oxytocin receptor (OXTR) gene polymorphism and obsessive compulsive disorder in Chinese Han

Recent research has shown that genetic variations in the oxytocin receptor (OXTR) may be related to variations in subtypes of obsessivecompulsive disorder (OCD). We aimed to explore the relationship between different subtypes of OCD and the genetic variation between rs1316193 and rs4686301 of the OXTR. In this case-control study, 92 OCD patients and 92 healthy controls were included in the OCD and control groups, respectively. The Y-BOCS scale was used to assess the severity of the OCD symptoms. The fasting peripheral blood samples were collected to extract DNA. rs4686301 and rs13316193 were genotyped using restriction fragment length polymorphism analysis techniques. Whether the gene frequency of the locus and the distribution of allele frequency were related to OCD were further study by TaqMan allele typing. The rs4686301 locus differed significantly between behavior and control groups. The genotype frequency and allele frequency at the rs4686301 locus were statistically significant between behavior and control groups (P<0.05). There was significant difference in the genotype frequency at the rs13316193 locus between behavior and control groups (P<0.05). The rs4686301 polymorphism of the OXTR may affect the clinical subtype of OCD. The rs13316193 polymorphism of the OXTR may be a risk factor for obsessive-compulsive behavior. Liu W / Arch Clin Psychiatry. 2021;48(5): 216-220
Relationship between paternal postnatal depression and its predictors factors among Iranian fathers

Background: Paternal postnatal depression (PPND) is widely overlooked despite being the most common mental health disorder among fathers in the postpartum period, and it affects all aspects of family life, particularly the relationship with the spouse and children. Objective: To determine the predictor factors of PPND. Methods: A descriptive-analytical study was performed on 400 fathers of children aged six weeks to one-year referring to the healthcare centers. Data were collected using a demographic characteristics form and the Edinburgh Postnatal Depression Scale (EPDS). Data analysis was performed using SPSS software version 22. Results: The mean EPDS score was 6/54± 4.3, the mean age of the studied fathers was 35 ± 5.4, and 57.7% had experienced more than one pregnancy with their spouses. There was a significant relationship (P>0.05) between the education level and the EPDS score. According to linear regression, the most influential factors on EPDS score included satisfaction with life and general health, as well as an unwanted pregnancy amongst maternal fertility characteristics. Conclusion: Determining the predictor/related factors with paternal postnatal depression helped us to identify atrisk fathers promptly, to manage earlier, and to prevent the adverse effects of PPND on family health and relationship. Yazdanpanahi Z / Arch Clin Psychiatry. 2021;48(3):162-167
Explainable Projection: Cross-lingual semantic role labeling

Semantic role labeling (SRL) is a central task 001 in many applications, e.g., machine transla-002 tion, question answering, summarization, and 003 more recently, complex tasks such as stance 004 detection. However, cross-lingual projection of 005 SRL labels has remained a thorny problem in 006 NLP. The scarcity of semantically annotated 007 corpora makes it difficult to build semantic 008 role labelers, particularly for languages where 009 hand-annotated labels are not readily available. 010 We leverage semantic isomorphism at the level 011 of predicate-argument structure to induce SRL 012 systems from unlabeled bilingual corpora. We 013 demonstrate that this approach yields explain-014 able representations that readily project to new 015 languages. Our novel contribution is the use of 016 a simple word-to-word alignment followed by a 017 First Come First Assign (FCFA) algorithm and 018 a handful of linguistically-informed constraints 019 specified at the predicate-argument level. These 020 constraints provide a systematic mapping to 021 semantic-role divergence categories that serve 022 as the basis for analysis of our FCFA approach. 023 A two-step process rapidly produces explain-024 able SRL output: simple alignment followed by 025 application of FCFA. This approach yields SRL 026 projection results that are comparable to state 027 of the art performance (XSRL), but without 028 relying on complex transformer-based scoring 029 schemes for multi-word alignments. 030
Presence of intra-aneurysmal thrombi with delayed coil migration after embolization of an aneurysmal subarachnoid hemorrhage: illustrative case

BACKGROUND Coil migration is a rare, but notable complication of endovascular treatment. Risk factors include communicating segment aneurysms, aneurysmal shape, and technical factors. Although cerebral blood flow obstruction caused by early coil migration requires urgent coil removal, delayed coil migration is often asymptomatic, making it difficult to determine a treatment strategy. OBSERVATIONS A 47-year-old woman was referred to the institute with acute-onset headache. She was diagnosed with subarachnoid hemorrhage due to rupture of the right internal carotid artery–posterior communicating artery aneurysm and underwent endovascular coil embolization. Following the procedure, the patient showed no obvious complications; however, 14 days later, images showed coil migration to the distal side, leading to surgical removal. Right frontotemporal craniotomy was performed, and the remaining coil was removed. The aneurysm was clipped again, and blood flow was confirmed. The patient was discharged 12 days after the craniotomy with transient oculomotor nerve palsy. At the 15-month follow-up, there was no aneurysm recurrence and the oculomotor nerve palsy showed improvement. LESSONS Retrieval of the migrated coil by craniotomy is an effective remedial measure; however, intraoperative complications are common. Early detection, established protocols, and prompt treatment decisions are important for preventing undesirable outcomes.
Uncovering interesting attributed anomalies in large graphs

Graph is a fundamental model for capturing entities and their relations in a wide range of applications. Examples of real-world graphs include the Web, social networks, communication networks, intrusion networks, collaboration networks, and biological networks. In recent years, with the proliferation of rich information available for real-world graphs, vertices and edges are often associated with attributes that describe their characteristics and properties. This gives rise to a new type of graphs, namely attributed graphs. Anomaly detection has been extensively studied in many research areas, and finds important applications in real-world tasks such as financial fraud detection, spam detection and cyber security. Anomaly detection in large graphs, especially graphs annotated with attributes, is still under explored. Most of existing work in this aspect focuses on the structural information of the graphs. In this thesis, we aim to address the following questions: How do we define anomalies in large graphs annotated with attributive information? How to mine such anomalies efficiently and effectively? A succinct yet fundamental anomaly definition is introduced: given a graph augmented with vertex attributes, an attributed anomaly refers to a constituent component of the graph, be it a vertex, an edge, or a subgraph, exhibiting abnormal features that deviate from the majority of constituent components of the same nature, in a combined structural and attributive space. For example in a social network, assume there exists a group of people, most of whom share similar taste in movies, whereas the majority of social groups in this network tend to have very diverse interests in movies; or in a collaboration network, there exists a group of closely connected experts that possess a set of required expertise, and such a group occurs scarcely in this network; we consider the groups in both scenarios as "anomalous". Applications of this research topic abound, including target marketing, recommendation systems, and social influence analysis. The goal of this work therefore is to create efficient solutions to effectively uncover interesting anomalous patterns in large attributed graphs. In service of this goal, we have developed several frameworks using two types of approaches: (1) combinatorial methods based on graph indexing and querying; (2) statistical methods based on probabilistic models and network regularization.
Probabilistic Amplitude Shaping

Probabilistic amplitude shaping (PAS) proposed in Böcherer, Steiner, Schulte [24] is a practical architecture for combining non-uniform distributions on higher-order constellations with off-the-shelf forward error correction (FEC) codes. PAS consists of a distribution matcher (DM) that imposes a desired distribution on the signal point amplitudes, followed by systematic FEC encoding, preserving the amplitude distribution. FEC encoding generates additional parity bits, which select the signs of the signal points. At the receiver, FEC decoding is followed by an inverse DM. PAS quickly had a large industrial impact, in particular in fiber-optic communications. This monograph details the practical considerations that led to the invention of PAS and provides an information-theoretic assessment of the PAS architecture. Because of the separation into a shaping layer and an FEC layer, the theoretic analysis of PAS requires new tools. On the shaping layer, the cost penalty and rate loss of finite length DMs is analyzed. On the FEC layer, achievable FEC rates are derived. Using mismatched decoding, achievable rates are studied for decoding metrics of practical importance. Combining the findings, it is shown that PAS with linear codes is capacity-achieving on a class of discrete input channels. Open questions for future study are discussed. Georg Böcherer (2023), “Probabilistic Amplitude Shaping”, Foundations and Trends® in Communications and Information Theory: Vol. 20, No. 4, pp 390–511. DOI: 10.1561/0100000111. ©2023 G. Böcherer Full text available at: http://dx.doi. org/10.1561/0100000111
Advances in the pathogenesis and personalised treatment of paediatric asthma

The diversity of pathology of severe paediatric asthma demonstrates that the one-size-fits-all approach characterising many guidelines is inappropriate. The term “asthma” is best used to describe a clinical syndrome of wheeze, chest tightness, breathlessness, and sometimes cough, making no assumptions about underlying pathology. Before personalising treatment, it is essential to make the diagnosis correctly and optimise basic management. Clinicians must determine exactly what type of asthma each child has. We are moving from describing symptom patterns in preschool wheeze to describing multiple underlying phenotypes with implications for targeting treatment. Many new treatment options are available for school age asthma, including biological medicines targeting type 2 inflammation, but a paucity of options are available for non-type 2 disease. The traditional reliever treatment, shortacting β2 agonists, is being replaced by combination inhalers containing inhaled corticosteroids and fast, longacting β2 agonists to treat the underlying inflammation in even mild asthma and reduce the risk of asthma attacks. However, much decision making is still based on adult data extrapolated to children. Better inclusion of children in future research studies is essential, if children are to benefit from these new advances in asthma treatment.
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition

Conditional independence provides a way to understand causal relationships among the variables of interest. An underlying system may exhibit more fine-grained causal relationships especially between a variable and its parents, which will be called the local independence relationships. One of the most widely studied local relationships is Context-Specific Independence (CSI), which holds in a specific assignment of conditioned variables. However, its applicability is often limited since it does not allow continuous variables: data conditioned to the specific value of a continuous variable contains few instances, if not none, making it infeasible to test independence. In this work, we define and characterize the local independence relationship that holds in a specific set of joint assignments of parental variables, which we call context-set specific independence (CSSI). We then provide a canonical representation of CSSI and prove its fundamental properties. Based on our theoretical findings, we cast the problem of discovering multiple CSSI relationships in a system as finding a partition of the joint outcome space. Finally, we propose a novel method, coined neural contextual decomposition (NCD), which learns such partition by imposing each set to induce CSSI via modeling a conditional distribution. We empirically demonstrate that the proposed method successfully discovers the ground truth local independence relationships in both synthetic dataset and complex system reflecting the real-world physical dynamics.
Model-free Causal Reinforcement Learning with Causal Diagrams

We present a new model-free causal reinforcement learning approach that utilizes the structure of causal diagrams, which could be learned during causal representation learning and causal discovery. Unlike the majority of approaches in causal reinforcement learning that focus on model-based approaches and off-policy evaluations, we explore another direction: online model-free meth-ods. We achieve this by extending a causal sequential decision-making formulation with factored Markov decision process (FMDP) and MDP with unobserved confounders (MDPUC), and by incorporating the concept of action as intervention. The choice of extending MDPUC addresses the issue of bidirectional arcs in learned causal diagrams. The action as intervention idea allows for the incorporation of high-level action models into the action space in an RL environment as a vector of interventions to the causal variables. We also present a value decomposition method and utilize the value decomposition network architecture popular in multi-agent reinforcement learning, showing encouraging preliminary evaluation results.
Causal Learning through Deliberate Undersampling

Domain scientists interested in causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems. A common and plausible assumption is that higher measurement frequencies are the only way to gain more informative data about the underlying dynamical causal structure. This assumption is a strong driver for designing new, faster instruments, but such instruments might not be feasible or even possible. In this paper, we show that this assumption is incorrect: there are situations in which we can gain additional information about the causal structure by measuring more slowly than our current instruments. We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.
Online Learning for Right-Sizing Serverless Function Invocations

—Serverless computing relieves developers from the burden of allocating and managing resources for their cloud applications, providing ease-of-use to the users and the opportunity to optimize resource utilization to the providers. However, the lack of visibility into user functions limits providers’ ability to right-size the functions. Thus, providers resort to simplifying assumptions, ignoring input variability, and coupling different resource types (CPU, memory, network), resulting in widely varying function performance and resource efficiency. To provide users with predictable performance and costs for their function executions, we need to understand how these factors contribute to function performance and resource usage. In this paper, we first conduct a deep study of commonly deployed serverless functions on an open-source serverless computing framework. Our analysis provides key insights to guide the design of a resource allocation framework for serverless systems, including the need to provision resources per invocation, account for function semantics, and decouple resources. We then present Lachesis , a resource allocation framework that builds on the insights we found and leverages online learning to right-size a function invocation. Our experiments show that Lachesis can increase speedup by 2.6x while decreasing idle cores by 82% compared to static allocation decisions made by users.
Computational Modeling and Hemodynamic Simulations Virtual Stenting and Porous Media Techniques

Aneurysm Morphological Characteristics For each aneurysm, the aneurysm size and the neck width were measured using threedimensional (3D) rotational angiography images. Aneurysm size was defined as the maximum perpendicular height of the aneurysm sac, and was categorized as small (≤10 mm) or large (>10 mm), in accordance with Dhar et al. Neck width was categorized as narrow (≤4 mm) or wide (>4 mm). 3
A description of the risk factors related to fragility fractures in adults and their associated costs

Objective : to identify bone fragility risk factors associated with increased total fracture care costs at a clinic in Medellín, Colombia. Design : an observational study with retrospective and prospective measurements taken from the medical charts of patients admitted for fractures and followed until discharge. Frame of reference: Hospital Alma Mater de Antioquia, Medellín, Colombia . Participants: four hundred fifty-two patients diagnosed with fragility fractures on admission. Main measurements: the prevalence of bone fragility risk factors, a description of the total care cost by risk factor and an estimate of the association between the risk factors and total costs. Results: Diabetes (24.3%) and active or passive smoking (21%) were the most prevalent fragility care was more costly for active or passive smokers (Md: COP 7,484,185), and those 75 years old or older (Md: COP 7,057,678). According to the significant adjusted estimates (p<0.05), the median cost for active or passive smokers exceeds that of nonsmokers by more than COP 2,300,000, and every year of age increases the median cost by more than COP 90,000. Conclusions : this study emphasized that bone fragility is a public health problem. Factors like active or passive smoking and age were found to increase fragility fracture care costs, implying more complications and need for services. This adds to the evidence for strengthening monitoring programs to reduce the morbidity, mortality and direct costs of this disease in Colombia. (Acta Med Colomb 2022; 47. DOI: https://doi.org/10.36104/amc.2022.2351).
An optimization technique for image compression

In this work we consider an optimization method to image file compression based on fuzzy logic. This optimization is realized thanks to wavelet transform in a pre-compression phase. Thanks to this approach in the pre-processing phase the wavelet transform is used to fix some relevant parameters like as the best compression box and other tolerance parameter (locally defined). With respect to the traditional approach by using wavelet this method is better from time consuming point of view. While with respect to a standard fuzzy logic approach the presented method has the advantage to perform compression with different ratio on different box optimized with respect to each subimage.
Achieving Scalability in OLAP Materialized View Selection (Extended Version)

The goal of on-line analytical processing (OLAP) is to quickly answer queries from large amounts of data residing in a data warehouse. Materialized view selection is an optimization problem encountered in OLAP systems. Published work on the problem of materialized view selection presents solutions scalable in the number of possible views. However, the number of possible views is exponential relative to the number of database dimensions. A truly scalable solution must be polynomial time relative to the number of dimensions. We present such a solution, our Polynomial Greedy Algorithm. Complexity analysis proves scalability, and a performance study verifies the result. Empirical evidence demonstrates benefits close to existing algorithms. We conclude the Polynomial Greedy Algorithm functions effectively where existing algorithms fail dramatically.
İŞ GÜVENLİĞİ UZMANLARININ TEKNİK VE İSTİHDAM EDİLEBİLİRLİK BECERİLERİ: İŞ İLANLARI ÜZERİNE BİR ANALİZ

İş Güvenliği Uzmanlarının Teknik ve İstihdam Edilebilirlik Becerileri: İş İlanları Üzerine Bir Analiz Hatice ÖZDEMİR, Ramazan Kayabaşı, Mehmet Akif ERKAN, İbrahim CÜNDÜBEYOĞLU Öz İş sağlığı ve güvenliğinin giderek artan önemi, iş güvenliği uzmanlık mesleğinin gelişimini etkilerken, mesleğe bakış açısını da şekillendirmektedir. Bu çalışmada işverenlerin talep ettiği teknik ve istihdam edilebilirlik becerilerini belirlemek amacıyla iş güvenliği uzmanı için verilen iş ilanları analiz edilmiştir. Çalışmada, kariyer.net ve yenibiris.com siteleri üzerinden ulaşılan 166 iş ilanı içerik analizi yöntemi kullanılarak incelenmiştir. Verilen ilan başlığında %68,1 ile “iş güvenliği uzmanı” en çok aranan pozisyon olmuştur. İlanlarda; %33,6’sında lisans mezunu, %23,5’inde C sınıfı sertifika ve %41,0’inde 1-3 yıl tecrübe talep edilmiştir. “İş sağlığı ve güvenliği mevzuatlarına hâkim olmak”, “saha uygulamalarını kontrol etmek ve denetim faaliyetlerini yürütmek” ve “kayıt, raporlama ve dokümantasyon faaliyetlerine hâkim olmak” öne çıkan teknik beceridir. “Bilgisayar kullanma becerisi”, “takım çalışması”, “sözlü ve yazılı olarak kendini ifade edebilme” ve “problem çözme” becerileri en fazla talep edilen istihdam edilebilirlik becerileridir. İş güvenliği uzmanlarının teknik ve istihdam edilebilirlik becerilerinin tecrübeyle ilişkisi ayrıca araştırılmıştır. Tecrübesiz uzmanların işe alımında istihdam edilebilirlik becerileri, tecrübeli uzmanların işe kabul edilmelerinde teknik beceriler anahtar konumdadır. Araştırmamız iş güvenliği alanında kariyer hedefi olanlara, akademisyenlere, işverenlere ve öğrencilere teknik ve istihdam edilebilirlik becerileri konusunda yol gösterecektir. Jel Kodları: J24, J28, E24 1Öğretim Görevlisi, Kayseri Üniversitesi, ORCID:0000-0003-4449-2433, haticeozdemir@kayseri.edu. tr 2 Öğretim Görevlisi, Kayseri Üniversitesi, ORCID: 0000-0001-6195-7445, rkayabasi@kayseri.edu. tr 3 Öğretim Görevlisi, Kayseri Üniversitesi, ORCID: 0000-0001-9083-6159, akiferkan@kayseri.edu. tr 4 İş Güvenliği Uzmanı, Kayseri Üniversitesi, ORCID: 0000-0002-5199-6720, icundubeyoglu@kayseri.edu. tr