Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Hyperbola Detection and Apex Extraction Using Convolutional Neural Network on GPR Data

Ground Penetrating Radar (GPR) is a non-destructive geophysical method used for subsurface mapping. It is frequently used for detecting buried cylindrical objects such as underground pipes and cables. Buried cylindrical objects show a hyperbolic signal pattern on a radargram. The typical shape of the hyperbolic reflections depends on the depth and material of the buried objects and the surrounding materials. In many cases, detecting buried cylindrical objects is quite a time-consuming task, thus limiting further interpretation procedures. In this paper, we propose a new method for automating hyperbola detection and apex extraction on radargram. Our work consists of two modules that take radargram as input in a form of images. In the first module, we used the Faster-RCNN to extract the hyperbola segments as a set of rectangular boundary boxes. The network was trained using synthetic radargram data simulated by the gprMax software. The second module is to estimate the coordinates of the hyperbola apex using an image processing technique. We had correctly detected all hyperbola on the simulated radargram from the test set. For the test on field radargram, the framework is capable of processing radargram that is similar to the simulated radargram data. The problem with the second module occurs on interference hyperbola as the searching window is disturbed by the noise. Apart from those problems, by using these two modules, the detection of buried cylindrical objects using GPR can be automated with a minimal amount of time.
Modelling of Motion of a Volvox Colony using Many-Body System and Its Qualitative Comparison with Observations

Approximately 500-60,000 volvox cells constructed a colony with a spherical or elliptical hollow form. Each cell can use its flagella to move. For a colony of hundred cells to perform a direct motion, there must be a coordination of the flagella. In this work, we assume that the cells already form the spherical colony and can perform some simple motion types, i.e., translational (TRAN), roll (ROLL), spin (SPIN), and superposition of the motions, and neglect detail of each flagellum. It is possible to map each cell in the colony using angular positions with a constant radius R and different θ _i and ϕ_i, where i = 1, 2,…, N with N is the number of cells constructing a colony. The center of the colony is R = (x, y, z). The TRAN mode can be obtained, wherex(t) = x(0) + υxt, y(t) = y(0) + υyt, z(t) = z(0) +υzt, ROLLmode with θ(t) = ω(0) + ωθt and SPIN with ϕ(t) = ω(0) + ωϕt. Through the available observation video, the model of volvox colony motion is also shown to have a qualitative agreement.
Condenser Vacuum Degree Prediction Model with Multi-View Information Fusion

Vacuum degree is a crucial factor for the operation of thermoelectric generating set. Existing approaches typically use machine learning algorithms to link the relationship between unit operating data and condenser vacuum degree by focusing on the temporal information within the data, while ignoring the frequency information implied in the historical condenser vacuum degree. To fully use of frequency information and further improve prediction accuracy, we propose a novel condenser vacuum degree prediction model with multi-view information fusion. In specific, the implicit frequency information in the historical vacuum degree sequence is explored via a combination of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). Furthermore, Transformer encoder is used to extract the temporal information from the unit operating data. Finally, the two views information are fused for condenser vacuum degree prediction. Extensive experiments conducted on the real data collected from a power plant demonstrate the superiority of the proposed method over several state-of-the-art methods.
I NDUCTIVE B IASES FOR R ELATIONAL T ASKS

Current deep learning approaches have shown good in-distribution performance but struggle in out-of-distribution settings. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as required in many intelligence tests. In contrast, our brains are remarkably flexible at such tasks, an attribute that is likely linked to anatomical constraints on computations. Inspired by this, recent work has explored how enforcing that relational representations remain distinct from sensory representations can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by “partitioned” representations of relations and sensory details. We investigate inductive biases that ensure abstract relations are learned and represented distinctly from sensory data across several neural network architectures and show that they outperform existing architectures on out-of-distribution generalization for various relational tasks. These results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures’ robustness when performing relational computations.
Paths through fixed vertices in edge-colored graphs

Chaines alternees passant par des sommets donnes dans des graphes aretes-colores. Nous etudions le probleme de trouver dans un graphe aretes-colore une chaine alternee joignant deux sommets donnes et passant par des sommets donnes (une chaine est alternee si deux aretes adjacentes arbitraires ont des couleurs differentes). Plus precisement nous demontrons que ce probleme est NP-complet dans le cas de graphes 2-aretes-colores. Ensuite nous montrons que le probleme de l'existence d'une telle chaine est polynomial dans le cas ou l'on se restreint aux graphes complets 2-aretes-colores. Nous etudions egalement le probleme de trouver une (s,t)-chaine (c'est-a-dire une chaine de longueur s+t qui se partage en deux sous-chaines monochromatiques de couleurs differentes) joignant deux sommets donnes et passant par des sommets donnes, dans un graphe complet aretes-colore.
Evaluating the Effect of Communication and Shared Knowledge Between IS and Line Organizations on IS Performance

Intensified competition, splintered mass market, shortened product life cycles, and advanced technology and automation let companies to increase the IT investment to meet the changes. Although IT investment increased, IS did not show the visible outcome. One of the major interests of IS managers is how to demonstrate the business value of the firm’s investment in information technology. This paper proposes the revised model of Nelson & Cooprider[13] that adds communication as an antecedent of regarding shared knowledge between IS and line groups. Knowledge can be shared through mutual trust, mutual influence and communication between these two groups. The revised model including communication is tested empirically using LISREL. The results show that shared knowledge has an effect on IS performance mediating with mutual trust, mutual influence and communication. Thus, IS managers should develop mutual trust, mutual influence and communication between these groups to achieve more shared knowledge, which proceeds higher IS performance.
Nu-Oxidative system in aged skeletal muscle

Aging is an inevitable biological process that is characterized by a general decline in the physiological and biochemical functions of the major systems. In the case of the neuromuscular system, reductions in strength and mobility cause a deterioration in motor performance, impaired mobility and disability. At the cellular level, aging is caused by a progressive decline in mitochondrial function that results in the accumulation of reactive oxygen species (ROS). As the level of oxidative stress in skeletal muscle increases with age, the age-process is characterized by an imbalance between an increase in ROS production in the organism, and antioxidant defences as a whole. We have reviewed the literature on oxidative stress in aging human skeletal muscles, and to assesss the impact of differences in physiological factors (sex, fiber composition, muscle type and function).
SEMINAL TRACT

Sudden obstruction of a coronary vessel is ordinarily due to a thrombus, but an embolus is occasionally found. Treatment consists in absolute rest in bed, morphin for relief of the pain and the use of digitalis. lasodilators are contra-indicated, since there is already a fall in blood pressure. J. Francisco; F. F. Sacramento; Angeles; and to 90-45. systoles but no in the heart a very uncontrollable and upon going to the contrary to instructions. on the of the fifth day, after from the On the basis of his persistent cardiac pain, pericardial rub, fall in blood pressure, and dis-turbed rhythm, the diagnosis was made of obstruction of one of the coronary branches.
PUBLIC PRIVATE SECTOR WAGE DIFFERENTIALS IN PAKISTAN

Ghulam Sarwar, Bushra Abid Assistant Professor, Department of Business Administration, University of Sargodha, Sargodha, Pakistan MPhil Scholar, Department of Economics, The University of Lahore, Sargodha Campus, Pakistan ABSTRACT: This study explored the Public-Private sector earning differentials for male and female employees in Pakistan. The study utilized nationally representative Labour Force Survey data (2010-11). The Ordinary Least Square and Oaxaca Blinder wage decomposition methods were used to obtain the robust estimates and decompose the earning gaps into the portion that is explained by the differences in productive endowments and the unexplained part due to unknown factors. The findings reveal that public sector employees have higher wage premium. Furthermore, public-private wage differential is higher for females than for males. Our results also show that the explained part of public-private wage differential for males is larger than explained part for females. Female wages in private sector are low as compare to public sector.
APOEε4 Genotype and Hypertension Modify Eight-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study

We investigated individual differences in longitudinal trajectories of brain aging in cognitively-normal healthy adults from the Seattle Longitudinal Study covering 8-years of longitudinal change (across 5occasions) in cortical thickness in 249 midlife and older adults (52-95 years-old). We aimed to: understand true brain change; examine the influence of salient risk factors that modify an individual’s rate of cortical thinning; and compare cross-sectional age-related differences in cortical thickness to longitudinal within-person cortical thinning. We used Multivariate Multi-Level Modeling to simultaneously model dependencies among five lobar composites (Frontal, Parietal, Temporal, Occipital, Cingulate) and account for the longitudinal nature of the data. Results indicate 1) all five lobar composites significantly atrophied across 8-years, showing nonlinear longitudinal rate of cortical thinning decelerated over time, 2) longitudinal thinning was significantly altered by hypertension and APOEε4, varying by location: frontal and cingulate thinned more rapidly in APOEε4 carriers. Notably, thinning of parietal and occipital cortex showed synergistic effect of combined risk factors, where individuals who were both APOEε4 carriers and hypertensive had significantly greater 8-year thinning than those with either risk factor alone or neither risk factor, 3) longitudinal thinning was three-times greater than cross-sectional estimates of age-related differences in thickness in parietal and occipital cortices.
2-Deoxyglucose and Cytoehalasin D Modulate Aldolase Mobility in Living 313 Cells

Approximately 23 % of the glycolytic enzyme aldolase in the perinuclear region of Swiss 3T3 cells is immobile as measured by FRAP. Previous studies suggest that the immobile fraction may be associated with the actin cytoskeleton (Pagliaro, L. and D. L. Taylor. 1988. J. Cell Biol. 107:981-991), and it has been proposed that the association of some glycolytic enzymes with the cytoskeleton could have functional significance, perhaps involving a fundamental relationship between glycolysis, cytoplasmic organization, and cell motility. We have tested the effect of a key glycolytic inhibitor and an actin cytoskeletal modulator on the mobility of aldolase in living cells directly, using fluorescent analog cytochemistry and FRAP. We report here that the competitive hexokinase inhibitor 2-deoxyglucose releases the bound fraction of aldolase in 3T3 cells within 10 min, and that this process is reversible upon washout of the inhibitor. A similar result is produced with the actin-binding agent, cytochalasin D. These results are consistent with models in which glycolytic enzymes are not exclusively diffusion-limited, soluble proteins, but may exist partially in the solid phase of cytoplasm. Such organization has significant implications for both the modulation of cytoplasmic structure and for cellular
THE TREATMENT OF "ANXIEY-DEPRESSION" VIA POSITIVE REINFORCEMENT AND RESPONSE COST1

A target behavior program, structured within a token economy project, was imple-mented to modify the behavior of an institutionalized patient who exhibited excessive rates of crying and no smiling responses. To affect both responses concurrently, token costs were made contingent upon crying and token payments and/or social reinforce-ments were provided for smiling. The results indicated both the feasibility of elimi-nating "anxiety-depression" within an institutional environment and the efficacy of the treatment procedures 14 months after discharge.
Trade Reforms and Current Account Imbalances: When Does the General Equilibrium Effect Overturn a Partial Equilibrium Intuition?

Permanent shocks such as trade liberalizations are hard to discuss in a standard dynamic Hechscher-Ohlin model due to potential interest rate over-determination. We make three contributions. First, we introduce an endogenous discount factor which solves the problem of interest rate over-determination. Second, we show that trade liberalization in a developing country generally leads to capital outflow, whereas it produces an opposite pattern for developed countries. Therefore, efficient trade reforms can contribute to global current account imbalances. Third, following a trade reform, the current account imbalance first widens and then falls back to zero. As an application, this model predicts an inverse-V-shape pattern for current account imbalances following China’s accession to the WTO: Even without policy distortions, China’s current account imbalance should rise for a few years before it starts to shrink.
A descriptive analysis of calls to the NSW Teratogen Information Service regarding use of anti-infectives during pregnancy

Background: MotherSafe is a free telephone-based counseling service for Australian consumers and health care providers concerned about drug exposures during pregnancy and breastfeeding. Anti-infectives are the most commonly prescribed drugs for pregnant women. This study aims to provide a retrospective, descriptive analysis of prospectively collected calls received by MotherSafe regarding anti-infective exposures during pregnancy between 2000 and 2020. Aggregate data were examined by type of caller, reason for call, pregnancy category and exposure type. Inductive thematic analysis of the comments recorded by MotherSafe counsellors at the time of call was undertaken. Results: Over the study period, 25,890 calls related to exposure to anti-infectives during pregnancy (antibiotic, antiviral and antifungal medications). Calls from patients were dominated by low-risk exposures (pregnancy category A) drugs while calls from health care professionals related to drugs with limited human information (pregnancy category B3). Analysis of MotherSafe counsellor comments revealed over 200 instances of concerns relating to health care professional advice to the patient. Three themes emerged-incorrect or conflicting advice, poor counselling, and refusal to treat, prescribe or dispense. It is likely that these comments are biased to the negative as patients would not call MotherSafe if they are happy with HCP advice. However, the findings are concerning as they reveal an underlying lack of knowledge in some health care professionals which may have led to undertreatment of patients. This study reinforced the importance of Teratogen Information Services such as MotherSafe in providing counselling and clear communication of evidence-based information to guide decision-making, reducing potential emotional distress in pregnant women and optimizing maternal, pregnancy and infant outcomes.
Review on Bi–Sr–Ca–Cu–O whiskers

We review the current status of the growth, characterization and applications of Bi–Sr–Ca–Cu–O (BSCCO) whiskers from the materials science point of view. Our analysis leads to the conclusion that reconsideration of the importance of the whisker studies is necessary; whiskers can play a major role in studies of the growth mechanism and defects, properties control and their understanding, and new devices and applications. Arguments favourable to the idea that BSCCO whiskers can possibly have a significant impact on future synthesis of nano-objects in this system or other high-Tc superconducting systems, as well as in investigation and understanding of the growth processes under elevated magnetic fields, are discussed. Emphasis is made on superconducting whiskers, but non-superconducting ones are also briefly addressed.
Cleavage of interleukin 1l8 (IL-1,8) precursor to produce active IL-1,8 by a conserved extracellular cysteine protease from Streptococcus pyogenes

Streptococcal pyrogenic exotoxin B (SPE B), a conserved extracellular cysteine protease expressed by the human pathogenic bacterium Streptococcus pyogenes, was purified and shown to cleave inactive human interleukin 1,B precursor (pIL-l13) to produce biologically active IL-1p8. SPE B cleaves pIL-1, one residue amino-terminal to the site where a recently characterized endogenous human cysteine protease acts. IL-113 resulting from cleavage of pIL-1(8 by SPE B induced nitric oxide synthase activity in vascular smooth muscle cells and killed cells ofthe human melanoma A375 line. Two additional naturally occurring SPE B variants cleaved pIL-1,B in a simiar fashion. By demonstrating that SPE B catalyzes the formation of biologically active IL-1lB from inactive pIL-l1B, our data add a further dimension to an emerging theme in microbial pathogenesis that bacterial and viral virulence factors act directly on host cytokine pathways. The data also contribute to an enlarging literature demonstrating that microbial extracellular cysteine proteases are important in host-parasite interactions. lane 3, pIL-1,B alone; lane 4, pIL-18 plus SPE B. Incubations were conducted for 30 min at 37°C. The cleavage products were resolved by SDS/PAGE, transferred to nitrocellulose, and probed with carboxyl-terminal-specific monoclonal antibody (22). The human recombinant pIL-l, substrate used in these assays was purified from a cell lysate of an Escherichia coli transformant containing the pIL-1P structural gene, by sequential column chromatography with Q-Sepharose, Procion Red-agarose, and phenyl-Sepharose CL-4B (22). The im-munoreactive proteins greater and less than -33 kDa in lane 3 are produced in the process used to make pIL-l, and are copurified with it. The lower band in lanes 2 and 4 has an apparent molecular mass of -10 kDa. The 18.5-kDa product is converted to the "18-kDa molecule (not the -10-kDa molecule) upon further incubation.
BioRED: a rich biomedical relation extraction dataset

Abstract Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for biomedical RE only focus on relations of a single type (e.g. protein–protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then, we present a first-of-its-kind biomedical relation extraction dataset (BioRED) with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene–disease; chemical–chemical) at the document level, on a set of 600 PubMed abstracts. Furthermore, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including Bidirectional Encoder Representations from Transformers (BERT)-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a rich dataset can successfully facilitate the development of more accurate, efficient and robust RE systems for biomedicine. Availability: The BioRED dataset and annotation guidelines are freely available at https://ftp.ncbi. nlm.nih. gov/pub/lu/BioRED/.
Regret guarantees for model-based reinforcement learning with long-term average constraints

We consider the problem of constrained Markov Decision Process (CMDP) where an agent interacts with an ergodic Markov Decision Process. At every interaction, the agent obtains a reward and incurs K costs. The agent aims to maximize the long-term average reward while simultaneously keeping the K long-term average costs lower than a certain threshold. In this paper, we propose CMDP-PSRL, a posterior sampling based algorithm using which the agent can learn optimal policies to inter-act with the CMDP. We show that with the assumption of slackness, characterized by κ , the optimization problem is feasible for the sampled MDPs. Further, for MDP with S states, A actions, and mixing time T M , we prove that following CMDP-PSRL algorithm, the agent can bound the regret of not accumulating rewards from an optimal policy by ˜ O ( T M S √ AT ) . Further, we show that the violations for any of the K constraints is also bounded by ˜ O ( T M S √ AT ) . To the best of our knowledge, this is the ﬁrst work that obtains a ˜ O ( √ T ) regret bounds for ergodic MDPs with long-term average constraints using a posterior sampling method.
Controlling Conditional Language Models without Catastrophic Forgetting

Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve a large number of tasks. However, due to their generic training methodology, these models often fail to meet some of the downstream requirements (e.g., hallucinations in abstractive summarization or style violations in code generation). This raises the important question of how to adapt pre-trained generative models to meet all requirements without destroying their general capabilities ("catastrophic forgetting"). Recent work has proposed to solve this problem by representing task-specific requirements through energy-based models (EBMs) and approximating these EBMs using distributional policy gradients (DPG). Despite its effectiveness, this approach is however limited to unconditional distributions. In this paper, we extend DPG to conditional tasks by proposing Conditional DPG (CDPG). We evaluate CDPG on four different control objectives across three tasks (translation, summarization and code generation) and two pretrained models (T5 and GPT-Neo). Our results show that fine-tuning using CDPG robustly moves these pretrained models closer towards meeting control objectives and -- in contrast with baseline approaches -- does not result in catastrophic forgetting.