Miracle-dz/UniFAR-FTUs huggingface dataset:
CIGMO: L EARNING CATEGORICAL INVARIANT DEEP GENERATIVE MODELS FROM GROUPED DATA

Images of general objects are often composed of three hidden factors: category (e.g., car or chair), shape (e.g., particular car form), and view (e.g., 3D orientation). While existing disentangling models can discover either a category or shape factor separately from a view factor, such models typically cannot capture the structure of general objects that the diversity of shapes is much larger across categories than within a category. Here, we propose a novel generative model called CIGMO, which can learn to represent the category, shape, and view factors at once only with weak supervision. Concretely, we develop mixture of disentangling deep generative models, where the mixture components correspond to object categories and each component model represents shape and view in a category-specific and mutually invariant manner. We devise a learning method based on variational autoencoders that does not explicitly use label information but uses only grouping information that links together different views of the same object. Using several datasets of 3D objects including ShapeNet, we demonstrate that our model often outperforms previous relevant models including state-of-the-art methods in invariant clustering and one-shot classification tasks, in a manner exposing the importance of categorical invariant representation.
The Intellectual Capital and its Impact on the Sustainable Development of the SML-Sized Enterprises in Poland

Purpose: The main goal of the article is to analyze the level of differentiation of awareness and knowledge of managers of small, medium and large enterprises within the scope of the essence and meaning of intellectual capital and the influence of its elements on the sustainable development of enterprises in Poland. Design/Methodology/Approach: The paper exploits a data set of 1067 companies operating in Poland (employing at least 10 employees). In order to test the research hypotheses, the analysis of variations for fuzzy numbers (FANOVA) was implemented. Findings: Although the majority of enterprises in Poland do not implement intellectual capital management strategies, managers are aware of the essence and significance of intellectual capital. The differentiation of the responses of managers based on the size of the enterprise was not statistically significant. Practical implications: This paper recommends companies to invest in intellectual capital that according to managers’ assessment has a significant impact on their sustainability. Originality/Value: This research contributes to enrich the theoretical framework for the Polish context regarding intellectual capital and it allows contrasting the evidence with other studies at national levels.
University of Birmingham Steroid sulphatase and its inhibitors

: Steroid sulphatase (STS), involved in the hydrolysis of steroid sulphates, plays an im ‐ portant role in the formation of both active oestrogens and androgens. Since these steroids signifi ‐ cantly impact the proliferation of both oestrogen ‐ and androgen ‐ dependent cancers, many research groups over the past 30 years have designed and developed STS inhibitors. One of the main con ‐ tributors to this field has been Prof. Barry Potter, previously at the University of Bath and now at the University of Oxford. Upon Prof. Potter’s imminent retirement, this review takes a look back at the work on STS inhibitors and their contribution to our understanding of sulphate biology and as potential therapeutic agents in hormone ‐ dependent disease. A number of potent STS inhibitors have now been developed, one of which, Irosustat (STX64, 667Coumate, BN83495), remains the only one to have completed phase I/II clinical trials against numerous indications (breast, prostate, endome ‐ trial). These studies have provided new insights into the origins of androgens and oestrogens in women and men. In addition to the therapeutic role of STS inhibition in breast and prostate cancer, there is now good evidence to suggest they may also provide benefits in patients with colorectal and ovarian cancer, and in treating endometriosis. To explore the potential of STS inhibitors further, a number of second ‐ and third ‐ generation inhibitors have been developed, together with single molecules that possess aromatase–STS inhibitory properties. The further development of potent STS inhibitors will allow their potential therapeutic value to be explored in a variety of hormone ‐ de ‐ pendent cancers and possibly other non ‐ oncological conditions.
Supplemental Document for “Matrix Completion with Model-free Weighting”

Proof of Lemma 1. We first list two properties of max norm as follows. (i) As shown in Srebro & Shraibman (2005), ‖B‖∗ ≤ √ n1n2‖B‖max. (ii) By an equivalent definition of max norm due to Lee et al. (2008) (also see equation (8) in Jalali & Srebro (2012)), we have ‖C ◦B‖ ≤ ‖C‖‖B‖max. Together with the duality of nuclear norm, we can show that |〈C ◦B,B〉| ≤ ‖C ◦B‖‖B‖∗ ≤ ‖C‖‖B‖max‖B‖∗ (S1) ≤ √ n1n2‖C‖‖B‖max.
Ultra-Fresh Osteochondral Allograft Transplantation Supported by Artificial Intelligence Algorithms-Case Report of a 14-Year-Old Patient

Surgical treatment of cartilage surface lesions is similar to tumor surgery in many ways. Depending on the extent, depth, and nature of the lesion, different pathways can lead to a solution. In our case, we performed an “ultra-fresh” osteochondral allograft transplantation due to an extensive osteochondral lesion in the lateral femur condyle of a 14-year-old patient. One of the main limitations of this process is to create a precisely sized and fitting graft, and thereby establishing the best congruence possible. In order to achieve this, MRI-based artificial intelligence algorithms were developed and this was used for donor-recipient matching. Postoperative follow-up of the patient was performed according to current international protocols. Although we do not have longterm experience with the use of artificial intelligence in this form, we believe that matching the most appropriate donor-recipient pairs during transplantation may affect the long-term outcomes of surgery.
T ARGET T RAINING : T RICKING A DVERSARIAL A TTACKS T O F AIL

Recent adversarial defense approaches have failed. Untargeted gradient-based attacks cause classifiers to choose any wrong class. Our novel white-box defense tricks untargeted attacks into becoming attacks targeted at designated target classes. From these target classes, we derive the real classes. The Target Training defense tricks the minimization at the core of untargeted, gradientbased adversarial attacks: minimize the sum of (1) perturbation and (2) classifier adversarial loss. Target Training changes the classifier minimally, and trains it with additional duplicated points (at 0 distance) labeled with designated classes. These differently-labeled duplicated samples minimize both terms (1) and (2) of the minimization, steering attack convergence to samples of designated classes, from which correct classification is derived. Importantly, Target Training eliminates the need to know the attack and the overhead of generating adversarial samples of attacks that minimize perturbations. Without using adversarial samples and against an adaptive attack aware of our defense, Target Training exceeds even default, unsecured classifier accuracy of 84.3% for CIFAR10 with 86.6% against DeepFool attack; and achieves 83.2% against CW-L2(κ=0) attack. Using adversarial samples, we achieve 75.6% against CW-L2(κ=40). Due to our deliberate choice of low-capacity classifiers, Target Training does not withstand L∞ adaptive attacks in CIFAR10 but withstands CW-L∞(κ=0) in MNIST. Target Training presents a fundamental change in adversarial defense strategy.
Predation potentiality of Notopterus notopterus on JE vector, Culex vishnui

The present laboratory bioassay was designed to determine the predatory potentiality of the bronze featherback, Notopterus notopterus (Actinopterygii: Osteoglossiformes) against the larvae of Culex vishnui, vector of the zoonotic disease Japanese Encephalitis. The objectives of the present study includes: analysis of the functional response of the fish species against mosquito immature, determination of functional response parameters and the effect of habitat complexity on consumption efficacy of the predatory fish. The results of the investigation revealed that consumption rate of predator increased with time from the lowest number of 650 larvae on the 1 day to 1039 larvae on 7 day. The functional response was Type II having handling time of 0.15 min and attack rate of 2.95. Habitat heterogeneity also showed a significant effect on the consumption rate of the fish. The result of the study suggests that N. notopterus can be effectively used in fields to reduce the population of Cx. vishnui.
A study on image captioning considering its imageability

We propose an imageability-aware image captioning method tailoring generated captions to various applications. In this study, we ﬁrst extend an existing image captioning dataset by augmenting its captions. Then, an imageability score for each augmented caption is calculated. A modiﬁed image captioning model is trained using this extended dataset to generate captions tailored to a speciﬁed imageability score. The evaluation shows the possibility that the extended dataset and the proposed method can generate imageability-aware captions.
Predictive Role of Activated Leukocyte Cell Adhesion Molecule in Colon Cancer

Aim: Colon cancer is one of the most common and deadly cancers worldwide. Activated Leukocyte Cell Adhesion Molecule (ALCAM) has been found to be associated with various malignancies. We investigated the expression of ALCAM in colon cancer patients, as well as its association with tumor size. Materials and Methods: A total of 90 people, 50 patients with stage II colon cancer and 40 healthy controls were included in the study. Serum ALCAM and Carcinoembryonic Antigen (CEA) levels were measured in both groups. Tumor size was evaluated in the patient group. Results: Circulating ALCAM levels were higher in patients with colon cancer than in the control group. Serum ALCAM levels were especially elevated in patients with larger tumor size. Conclusion: ALCAM is overexpressed in colon cancer, and these results suggest that serum ALCAM levels may have a predictive role in the prognosis of this disease.
International Dental Research

Aim: Materials used in orthodontic treatment can cause corrosion and metal ion release to the mouth. This study was aimed to examine the changes in the friction coefficients and corrosion resistance of the modified Ni-Ti arch wires. Methodology: Our sample consisted of 16 Ni-Ti arch wires (0.016 × 0.022 inches). Coating bathes involving Ni-Ti, Ni-Ti-Mo, and Ni-Ti-Cr were prepared for arch wires coating. New surfaces were analyzed by Scanning Electron Microscope (SEM), and the crystallographic structure was determined by X-rays. The friction coefficient of the coated wires was determined by nanoindentation. Artificial saliva was prepared as a corrosive condition. Impedance measurements were performed with an electrochemical analyzer. The AC impedance spectroscopy measurements were performed after 4 hours, 7, 15, 30, 45, and 60 days. Results: All groups were more resistant than the control group (-447 mV) [group 1 (-388 mV), group 2 (-328 mV), group 3 (-327 mV), group 4 (-427 mV), group 5 (-395 mV), group 6 (-366 mV), and group 7 (-380 mV)]. Friction coefficients of group 5 (0.252) and group 6 (0.265) were observed to be lower than those of the control group (0.288). Conclusion: In the results of our study, it was shown that the surface properties of Ni-Ti wires could be electrochemically improved. The coated wires (group 5 and group 6) had a remarkable corrosion resistance and reduced friction. The protocols that we used should help provide betterquality surfaces.
On the quantification of the mental image of visual concepts for multi-modal applications

The semantic gap is the lack of coincidence between the information one can extract from data and its interpretation. It is an yet solved issue for multimedia applications like image captioning, where it is often challenging to select the best fitting wording out of a group of candidates. To create a measurement for the perceived differences between words, this doctoral research proposes the idea of analyzing crawled image data to gain a better semantic understanding of language and vision. Abstract words have a broad mental image due to them being less visually defined, while concrete words with a rather narrow visual feature space are visually easier to grasp. The core goal of this research is to approximate this perceived abstractness of those words as a metric. The thesis proposes two methods, looking at both relative and absolute measurements. For the relative method, a data-driven approach is proposed, while the absolute measurements train a machine-learned model on existing data from Psycholinguistics. Each method is evaluated using crowd-sourced data and compared to related approaches. With this, the thesis presents methods to analyze the mental image of words from different angles, targeting a way to quantify the semantic gap between vision and language.
Comparison of Text Classification Algorithms of People Sentiments on Twitter (Case Transjakarta)

— Nowadays social media is one to express things that are thought and felt by the community. One of the things that’s much talked about is responses from the consumer of products or services. This is very useful for companies to find out the level of satisfaction of their products or services. Twitter is one of the most widely used social media by users. With this fact, it's really interesting for companies to use the data on Twitter for the company's progress generally in customer relations. In this study an analysis of public sentiments towards the use of Transjakarta. This study divides community sentiments into three classes, positive, neutral and negative. For data taken from Twitter with the results of research from June to July 2019 by dividing the data into training data and testing data. The amount of training data is 144 tweets and testing data are 36 tweets. Then for the text classification uses 3 algorithms, namely naïve bayes, k-nearest neighbor and logistic regression. Then after the results are obtained, next is to compare the performance levels of three methods by finding the highest f-measurement value using micro average formula. Micro average is chosen because it’s the best for calculating imbalanced datasets. The results show the naïve bayes method has the best f-measurement with 0.861 value. For the next largest f-measurement value is the logistic regression method with an f-measurement value of 0.833, and the last is the k-nearest neighbor method with an f-measurement value of 0.806
Streamflow Models of Imo River for Regional Water Resources Allocation

---------------------------------------------------------Abstract------------------------------------------------------Developed Streamflow Models for Imo River is presented based on the statistical method of least squares. Four streamflow models of Imo River are developed to assist allocation of water resources within the South-East region of Nigeria mostly concern by the river basin, using streamflow data from Umuna, Obigbo, Umuopara and Ndimoko streamflow gauging stations. The models represent nonlinear relationship between annual maximum discharge and annual maximum stage. High and positive values of correlation coefficients (r) ranging between 0.953 to 0.998 and coefficients of determination (r) ranging between 0.908 to 0.997 were obtained indicating good curve fitting. The models developed will serve the useful purpose of predicting streamflow events and in turn flood prediction, erosion and sedimentation control, drainage, water resources allocation and management, design and operation of hydraulic structures, irrigation, habitat protection, recreational use of water, and pollution abatement. The four streamflow models of Imo River can also be used to predict similar river characteristics with that of Imo River. The research will play an important role in ensuring that future allocation of water resources in the localities are scientifically based and efficiently used so as to satisfy the needs of both human and natural systems.
Comparison of artificial intelligence methods for predicting compressive strength of concrete

Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.
Strategic Ambiguity in Global Games Strategic Ambiguity in Global Games*

In incomplete information games with ambiguous information, rational behavior depends on fundamental ambiguity (ambiguity about states) and strategic ambiguity (ambiguity about others’ actions). We study the impact of strategic ambiguity in global games, which is evident when one of the actions yields a constant payoff. Ambiguous-quality information makes more players choose this action, whereas (unambiguous) low-quality information makes more players choose an ex-ante best response to the uniform belief over the opponents’ actions. If the ex-ante best-response action yields a constant payoff, sufficiently ambiguous-quality information makes most players choose this action, thus inducing a unique equilibrium, whereas sufficiently low-quality information generates multiple equilibria. In applications to financial crises, we demonstrate that news of more ambiguous quality triggers a debt rollover crisis, whereas news of less ambiguous quality triggers a currency crisis. JEL classification numbers: C72, D81, D82.
Significant volatile fatty acids production by using primary and digested sludge as raw materials in anaerobic fermentation

The use of waste as a renewable raw material is highly studied for development of next-generation technologies to produce fuels, chemicals, and materials; and one of the main waste stream generated worldwide causing environmental impacts due to its lack of management is the sludge from wastewater treatment plants, which can be valued as volatile fatty acids (VFA). Therefore, the present study explored the production of VFA from primary and digested sludge by the evaluation of different operative conditions in an anaerobic fermentation process. The experimental design managed three independent variables; organic loading (OL) rate was evaluated in two levels (6 gVS/L and 4 gVS/L) for primary sludge (PS) and (10gVS/L and 14 gVS/L) for digested sludge (DS), pH values of 9.0, 10.0 and 11.0; this was evaluated under mesophilic conditions, a temperature around 35°C, for both residues. The results showed that the highest total VFA production was accomplished by using primary sludge, with an OL of 14gVS/L, a pH of 11.5 and at 35°C (5496±0.02 mg COD/L at day 6). VFA yield is strongly affected by the pH and the inhibition of methane production. Likewise, the high content of proteins and the OL of the essays performed with PS resulted in significant production of VFA, indicating a strong difference from the DS assays.
ICARUS: Attacking low Earth orbit satellite networks

Internet service based on low Earth orbit satellites is generating immense excitement in the networking community due to its potential for global low-latency connectivity. Despite the promise of LEO satellite networks, the security of their operation has so far been largely neglected. In this context, we present ICARUS, a new class of denial of service attacks on LEO networks. ICARUS turns these networks’ key benefits into vulnerabilities: an adversary can leverage the direct global accessibility to launch an attack from numerous locations, while the quest for low latency constrains routing, and provides predictability to the adversary. We explore how the adversary can exploit other unique features, including the path structure of such networks, and the public knowledge of the locations and connectivity of the satellite-routers. We find that a small amount of attack bandwidth can hamper communications between large terrestrial areas. Finally, we lay out open problems in this direction, and provide a framework to enable further research on attacks and defenses in this context.
THE RELATIONSHIP BETWEEN DEPRESSION, GENDER, AND SCHOOL TYPE AMONG SENIOR SECONDARY SCHOOL STUDENTS IN THE SOUTH SENATORIAL ZONE OF BAUCHI STATE, NIGERIA *AHMED SADIQ *AGBO DAVID OKLO AND **LUKMAN IBRAHIM JAHUN

] Abstract Depression has been considered to be the major psychiatric disease of the 20th century. It is an emotion that everyone will have experienced at some point in their lives. Depression strikes rich and poor, young and old, men and women, the famous and the unknown. Hence, this study was carried out to examine the relationship between depression, gender, and school type among senior secondary school students in the South Senatorial Zone of Bauchi State. Ex-post facto design was used in the study. The population of the study consists of 75 senior secondary schools with about 57,480 public senior secondary school students in the South Senatorial Zone of Bauchi State. 381 students were selected as sample based on the principles of Research Advisor’s (2006) table. The sampling technique used in this study was ‘Stratified Random THE RELATIONSHIP BETWEEN DEPRESSION, GENDER, AND SCHOOL TYPE AMONG SENIOR SECONDARY SCHOOL STUDENTS IN THE SOUTH SENATORIAL ZONE OF BAUCHI STATE, NIGERIA
THYROID DYSFUNCTION AND MORTALITY IN CRITICALLY ILL PATIENTS IN A TERTIARY CARE HOSPITAL IN JAIPUR, RAJASTHAN INDIA

Background: Person without any history of thyroid illness may have multiple changes in their thyroid hormone levels during critical illness, These changes are termed as euthyroid sick syndrome (ESS). This change correlates with the severity of the illness and its outcomes in critically ill patients. Aims: To find out the thyroid profile in critically ill patients and its association of thyroid illness in with ICU mortality. Material methods: A total of 100 critically ill patients from ICU were selected as cases and estimation of Thyroid profile done. Study conducted in department of medicine Mahatma Gandhi hospital Jaipur, Rajasthan. Estimation done with Chemiluminescence. Results: Out of 100 patients 37 patients expired. Mean FT3,FT4,TSH levels in survivors was 3.08 ± 0.75 pg/ml, 1.22 ± 0.47 ng/dl and 2.54 ± 1.06 μIU/ml respectively and in non survivors group it was 1.95 ± 0.85 pg/ml, 0.92 ± 0.37 ng/dl and 1.38 ± 0.79 μIU/ml respectively. T3, T4, TSH significantly (p value <0.001) low in critically ill patients. Conclusion: Euthyroid Sick Syndrome and mortality in ICU patients had significant association.