Miracle-dz/UniFAR-FTUs huggingface dataset:
The amphibians of Mont Sangbe National Park, Ivory Coast

I investigated the amphibian fauna ofMont Sangbe National Park, Ivory Coast, during several field trips in 2000 and 2001. The park is unique for West Africa in combining a variety of different habitat types, e.g. rain forest, mountainous habitats and different savanna formations. In total I recorded 45 anuran species and calculated that the occurrence of 10 additional species in the park is likely. About half of the recorded species are endemic to West Africa. Five species are local endemics, restricted to an area stretching from western Ivory Coast into neighboring Guinea and Liberia ( Conraua sp., Petropedetes natator, Astylosternus occidentalis, Hyperolius chlorosteus, H. zonatus). These and some other forest species (Amnirana albolabris, Leptopelis hyloides, L. occidentalis) reach their northernmost Ivorian distribution in Mont Sangbe National Park.
The Mixing Efﬁciency of Stratiﬁed Turbulent Boundary Layers

The mixing efﬁciency observed in stratiﬁed turbulent boundary layers is considered within the framework of the Monin–Obukhov similarity theory. It is shown that the efﬁciency within the layer increases with distance from the boundary. Near the boundary, the efﬁciency is proportional to the distance from the boundary scaled with the Monin– Obukhovlength.Farfromtheboundary,theefﬁciencyrelaxestoavaluethatdependsontheoverallthicknessofthelayerrelativetotheMonin–Obukhovlayer.Thisvalueapproaches1/6whenthethicknessislargerthan1/2oftheMonin– Obukhovlength.ThesameanalysisshowsthatthebuoyancyReynoldsnumbercannotbeusedtounequivocallypredict the efﬁciency. The 2 1/2 scaling between the efﬁciency and buoyancy Reynolds number that has been observed in ﬁeld measurements and experiments is shown to depend on an extra dimensional scale and thus is not universal.
Innovation and High-Quality Development of Enterprises—Also on the Effect of Innovation Driving the Transformation of China’s Economic Development Model

Taking the A-share listed companies in Shanghai and Shenzhen from 2013 to 2020 as the research object, this paper discusses the impact of innovation on high-quality development at the enterprise level and evaluates the effectiveness of the current transformation of innovation driving China’s economic development mode. It is found that innovation inhibits the improvement of total factor productivity of enterprises overall and hinders the high-quality development of enterprises. This result also shows that China’s innovation driven economic development mode transformation has not achieved actual effect. The reason is that China is still in the dilemma that low-quality innovation occupies the dominant position of enterprise innovation. The inhibitory effect of low-quality innovation masks the promoting effect of high-quality innovation, which makes the net effect of innovation on enterprise productivity being inhibitory, and this inhibition is characterized by a loss of efficiency, which is caused by a mismatch of resources. Heterogeneity analysis shows that the inhibition effect of innovation is stronger in eastern enterprises, non-state-owned enterprises and the early implementation of innovation driven strategy. It is also found that the promotion effect of high-quality innovation on executive compensation is stronger than that of low-quality innovation, which indicates that carrying out low-quality innovation is an irrational and short-sighted behavior. The study provides important insights for China to optimize and improve innovation policies and to promote the shift of the overall innovation structure to high-quality innovation.
Eﬀect of Dental Treatment on the Body Mass Index of Children Aged 3 6 Years Diagnosed with Early Childhood Caries: A Longitudinal Case-Control Study

Background: Dental caries is one of the most common childhood problems, which is associated with other diseases, such as malnutrition. Objectives: This study aimed to investigate the eﬀect of early childhood caries (ECC) treatment on the body mass index (BMI) of children, aged 3 - 6 years. Methods: In this longitudinal case-control study, 96 children, aged 3 - 6 years, were recruited and divided into three groups (n = 32 per group). A questionnaire was completed for each child, and the decayed, missing, and ﬁlled Teeth (dmft) index was measured via clinical examination, according to the World Health Organization (WHO) criteria. Anthropometric measurements were performed in each group to assess BMI at baseline and after six months. The ﬁrst two groups were diagnosed with ECC. For group I, a comprehensivedentaltreatmentwasperformedundergeneralanesthesiaatbaseline,whileforgroupII,notreatmentwasapplied during six months. Group III consisted of caries-free children. Data were analyzed using ANOVA, independent t -test, paired t -test, Mann-Whitney U test, and Kruskal-Wallis test (P < 0.05). Results: Themeanandstandarddeviationof thedmftindexwere7.09 ± 2.40,5.19 ± 1.72,andzeroingroupsI,II,andIII,respectively. TheBMIchangesaftersixmonthswere0.11 ± 0.42,0.00 ± 0.39,and-0.04 ± 0.64kg/m 2 ingroupsI,II,andIII,respectively. Themean BMI signiﬁcantly changed in the three groups within six months (P = 0.034). In group I, the BMI changes were signiﬁcantly greater than the other two groups (P = 0.004). Conclusions: Although a signiﬁcant negative relationship was found between ECC and BMI, oral hygiene and nutritional behavior were the main confounding variables. Based on the ﬁndings, treatment of ECC had a signiﬁcant positive eﬀect on BMI; therefore, it is important for healthcare professionals to identify and treat children at risk.
Medical Communication Perceived Self-Efficacy (ME-CO) Scale: Construction and Validation of a New Measuring Instrument from a Socio-Cognitive Perspective

The study presents the validation of a scale measuring physicians’ efficacy beliefs about their ability to manage issues related to communication with patients. Specifically, the tool focused on three fundamental phases of the clinical interview: collecting information, returning information to patients, and creating and maintaining a relationship with them. The research included two studies. Study 1 generated an item pool based on the literature review and developed a self-report questionnaire administered to a pilot sample of 150 physicians (MAge = 49.36; SD = 1.98). The responses were subjected to exploratory analysis. In total, 636 physicians (MAge = 47.99; SD = 8.68) took part in Study 2. Exploratory and confirmatory analyses yielded a final version of the tool consisting of an eight-factor structure with 31 items. Findings provided evidence of the robust psychometric properties of the scale and its usefulness in assessing physicians’ self-efficacy and defining effective interventions aimed at strengthening the doctors’ communication skills. The scale detected different aspects of physicians’ communication self-efficacy (asking questions, active listening, giving information, communicating an inauspicious diagnosis, non-verbal communication, recognition of patient’s clues and suggestions, information checking, and empathy).
Measurement of CP Asymmetry in B0 --> K+K-K0(S) Decays

We present preliminary measurements of the CP asymmetry parameters and CP content in B0-->K+K-Ks decays, with phiKs events excluded. In a sample of 227M BB pairs collected by the BABAR detector at the PEPII B-Factory at SLAC, we find the CP parameters to be S = -0.42 +/- 0.17 +/- 0.04 and C = 0.10 +/- 0.14 +/- 0.06, where the first error is statistical and the second is systematic. Extracting the fraction of CP -even final states from angular moments f(CP-even) = 0.89 +/- 0.08 +/- 0.06, and setting C=0, we determine sin(2beta) = 0.55 +/- 0.22 +/- 0.04 +/- 0.11, where the last error is due to uncertainty on the CP content.
Molecular processes and turbulent magnetic fields in the solar atmosphere

Coherent scattering in the solar atmosphere leads to the formation of the linearly polarized solar spectrum, just like Rayleigh scattering leads to the polarization of the blue sky. One of the most prominent features of the linearly polarized solar spectrum is the CN violet system as it is also in the unpolarized spectrum. This thesis is devoted to the modeling and interpretation of this system in both spectra and developing it into a very sensitive tool for studying the magnetic fields and the temperature structure of the solar atmosphere. The understanding of the solar magnetic field structure is very important as it is connected with and even controls most of the solar activity phenomena. Zeeman effect diagnostics allows to measure strong directed magnetic fields which only cover about 1% of the solar atmosphere. The remaining part is occupied by weak entangled magnetic fields with mixed polarity, which might significantly contribute to the overall solar magnetic energy. These fields are invisible to the Zeeman effect due to signal cancellation. Therefore the discovery of the linearly polarized solar spectrum opened a new epoch in solar physics. The polarization due to the scattering processes is modified by weak entangled magnetic fields via the Hanle effect and thus, provides us with a unique possibility to access and study such “hidden” magnetic fields. Molecular lines are very useful for probing magnetic fields as, due to their strong temperature sensitivity, different molecules sample different, narrow layers of the solar atmosphere. Therefore the extension of the atomic Hanle effect to molecular lines can provide the 3D structure of the solar turbulent magnetic field. Moreover, due to the broad range of magnetic sensitivities within narrow spectral regions molecular lines can be used for employing the differential Hanle effect technique, which allows dramatically reduced model dependence of the obtained magnetic
Herschel reveals a T_dust-unbiased selection of z~2 ULIRGs

Using Herschel PACS and SPIRE observations of Lockman Hole-North and GOODS-N as part of the HerMES project, we explore the far-IR properties of a sample of mid-IR selected starburst dominated ultra-luminous infrared galaxies (ULIRGs) at z ~ 2. The selection of the sample is based on the detection of the stellar bump that appears in the SED of star-forming galaxies at 1.6um. We derive robust estimates of infrared luminosities (L_IR) and dust temperatures (T_d) of the population and find that while the luminosities in our sample span less than an order of magnitude (12.24< log(L_IR/Lo) < 12.94), they cover a wide range of dust temperatures (25< T_d < 62 K). Galaxies in our sample range from those that are as cold as high-z sub-millimeter galaxies (SMGs) to those that are as warm as optically faint radio galaxies (OFRGs) and local ULIRGs. Nevertheless, our sample has median T_d=42.3 K, filling the gap between SMGs and OFRGs, bridging the two populations. We demonstrate that a significant fraction of our sample would be missed from ground based (sub)mm surveys (850-1200um) showing that the latter introduce a bias towards the detection of colder sources. We conclude that Herschel} observations, confirm the existence of high-z ULIRGs warmer than SMGs, show that the mid-IR selection of high-z ULIRGs is not T_d-dependent, reveal a large dispersion in T_d of high-z ULIRGs, and provide the means to characterize the bulk of the ULIRG population, free from selection biases introduced by ground based (sub)mm surveys.
Transformation strategies in community engagement

This paper examines initiatives developed in Malaysian libraries to enhance library roles in promoting knowledge and technology exchange as well as community wellbeing. It explores how libraries in Malaysia are transforming society through community engagement and highlights initiatives undertaken by libraries that promote community access to collections, services, and events. The success of the initiatives is demonstrated in seven transformation strategy themes, namely; (i) embedded services; (ii) bridges and web technology; (iii) strategic and institutional partnering; (iv) rural libraries; (v) community libraries; (vi) asset-based community development; and (vii) international librarianship. While it is often cited that public libraries are those that serve the community at large, this paper provides the perspective from other types of library viewpoints, emphasizing that such community outreach services should not be limited to public libraries. The library science community at large, and especially academic libraries, must play a role in community engagement.
Impact of Maternal Over-nutrition during Pregnancy on Maternal Oxidative Stress and Fetal Skeletal/Visceral Anomalies of the Rats

The maternal food supply, nutrition and metabolism leads to lifelong consequences for the health of the infants. Experimental models have been developed to explore these repercussions. We intended (1) to verify the influence of a maternal overnutrition on the maternal oxidative stress in pregnant rat, (2) to evaluate if the maternal oxidative stress interferes on the fetal growth, and (3) to analyze external, skeletal and visceral fetal anomalies. On day 0 of pregnancy, the rats were randomly distributed into two groups (n=21 rats/group): Control group - fed regular diet; Overnutrition group – fed high caloric diet, which was made by mixing the supplemented ingredients to a previously triturated Purina Chow. On day 21 of pregnancy, the rats were euthanized to evaluate reproductive outcomes and fetal development, and to obtain blood samples for maternal metabolic parameters. The fetal weights were significantly reduced after overnutrition treatment during pregnancy. The overnoutrished rats presented higher malonaldehyde-MDA and thiol group concentrations and superoxide dismutase (SOD) activity whereas glutathione peroxidase (GSH-Px) activity was lower. The fetuses from overnutrition group presented higher frequency of skeletal and visceral anomalies. Thus, the overnutrition caused metabolic changes in the maternal organism, particularly in oxidative metabolism, which impaired the fetal growth and development, showing harmful effect of a hypercaloric diet during pregnancy.
mode in a

We analyze non-linear transverse mode coupling in a Kerr medium placed in an optical cavity and its inﬂuence on bistability and diﬀerent kinds of quantum noise reduction. Even for an input beam which is perfectly matched to a cavity mode, the non-linear coupling produces an excess noise in the ﬂuctuations of the output beam. Intensity squeezing seems to be particularly robust with respect to mode coupling while quadrature squeezing is more sensitive. However it is possible to ﬁnd a mode the quadrature squeezing of which is not aﬀected by the coupling.
Reinforcement Learning with a Near Optimal Rate of Convergence

We consider the problem of model-free reinforcement learning in the Markovian decision processes (MDP) under the PAC ("probably approximately correct") model. We introduce a new variant of Q-learning, called speedy Q-learning (SQL), to address the problem of the slow convergence in the standard Q-learning algorithm, and prove PAC bounds on the performance of SQL. The bounds show that for any MDP with n state-action pairs and the discount factor \gamma \in [0, 1) a total of O(n \log(n/\delta)/((1 − \gamma)^4\epsilon^2)) step suffices for the SQL algorithm to converge to an \epsilon-optimal action-value function with probability 1 − \delta. We also establish a lower-bound of \Omega(n \log(1/\delta)/((1 − \gamma)^2\epsilon^2)) for all reinforcement learning algorithms, which matches the upper bound in terms of \epsilon, \delta and n (up to a logarithmic factor). Further, our results have better dependencies on \epsilon and 1 −\gamma, and thus, are tighter than the best available results for Q-learning. SQL also improves on existing results for the batch Q-value iteration, so far considered to be more efficient than the incremental methods like Q-learning.
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback

We consider online no-regret learning in unknown games with bandit feedback, where each player can only observe its reward at each time -- determined by all players' current joint action -- rather than its gradient. We focus on the class of \textit{smooth and strongly monotone} games and study optimal no-regret learning therein. Leveraging self-concordant barrier functions, we first construct a new bandit learning algorithm and show that it achieves the single-agent optimal regret of $\tilde{\Theta}(n\sqrt{T})$ under smooth and strongly concave reward functions ($n \geq 1$ is the problem dimension). We then show that if each player applies this no-regret learning algorithm in strongly monotone games, the joint action converges in the \textit{last iterate} to the unique Nash equilibrium at a rate of $\tilde{\Theta}(nT^{-1/2})$. Prior to our work, the best-known convergence rate in the same class of games is $\tilde{O}(n^{2/3}T^{-1/3})$ (achieved by a different algorithm), thus leaving open the problem of optimal no-regret learning algorithms (since the known lower bound is $\Omega(nT^{-1/2})$). Our results thus settle this open problem and contribute to the broad landscape of bandit game-theoretical learning by identifying the first doubly optimal bandit learning algorithm, in that it achieves (up to log factors) both optimal regret in the single-agent learning and optimal last-iterate convergence rate in the multi-agent learning. We also present preliminary numerical results on several application problems to demonstrate the efficacy of our algorithm in terms of iteration count.
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle to learn friendly representations to detect the open intent with prior knowledge of only known intents. Secondly, there is a lack of an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this article presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments demonstrate the effectiveness of the proposed distance-aware and boundary learning strategies. Compared to state-of-the-art methods, our framework achieves substantial improvements on three benchmark datasets. Furthermore, it yields robust performance with varying proportions of labeled data and known categories.
On the Asymptotic Performance of Matching Mechanisms with Afﬁrmative Actions

Afﬁrmative action policies, albeit controversial, attempt giving disadvantaged social groups preferential treatments to close the racial, ethnic, or socioeconomic gaps among different groups in our societies. This paper studies the asymptotic performance of two conventional matching mechanisms, the top trading cycles mechanism (TTCM) and the immediate acceptance mechanism (IAM), in the context of school choice markets with afﬁrmative actions. We show that there exists no clear welfare dominance relationship between the quota-based afﬁrmative action policy and its reserve-based counterpart for minority students under the TTCM, in the sense that these two afﬁrmative actions induce different matching outcomes with non-negligible probability even in a sequence of random markets under relatively restricted regularity conditions. Given the possible preference manipulations under the IAM, we further characterize the asymptotically equivalent sets of Nash equilibrium outcomes of the IAM with these two afﬁrmative actions when the market becomes sufﬁciently large. As the transition from one afﬁrmative action policy to the other could possibly evoke substantial socioeconomic costs to local communities, we conclude that the IAM is more cost-effective compared to the TTCM in large school choice markets with afﬁrmative actions, in the sense that it is unnecessary to identify the different welfare effects of these two afﬁrmative actions under the IAM if the policymaker can assure a sufﬁcient supply of popular schools.
On Bridging Generic and Personalized Federated Learning

Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributions diverge from each other. This divergence further leads to a dilemma:"Should we prioritize the learned model's generic performance (for future use at the server) or its personalized performance (for each client)? "These two, seemingly competing goals have divided the community to focus on one or the other, yet in this paper we show that it is possible to approach both at the same time. Concretely, we propose a novel federated learning framework that explicitly decouples a model's dual duties with two prediction tasks. On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them. On the other hand, we formulate the personalized predictor as a lightweight adaptive module that is learned to minimize each client's empirical risk on top of the generic predictor. With this two-loss, two-predictor framework which we name Federated Robust Decoupling (Fed-RoD), the learned model can simultaneously achieve state-of-the-art generic and personalized performance, essentially bridging the two tasks.
Lower Bounds for the MMSE via Neural Network Estimation and Their Applications to Privacy

The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable Y ∈ R given another random variable X ∈ R d is of much interest in a variety of statistical settings. In the context of estimation-theoretic privacy, the MMSE has been proposed as an information leakage measure that captures the ability of an adversary in estimating Y upon observing X . In this paper we establish provable lower bounds for the MMSE based on a two-layer neural network estimator of the MMSE and the Barron constant of an appropriate function of the conditional expectation of Y given X . Furthermore, we derive a general upper bound for the Barron constant that, when X ∈ R is post-processed by the additive Gaussian mechanism and Y is binary, produces order optimal estimates in the large noise regime. In order to obtain numerical lower bounds for the MMSE in some concrete applications, we introduce an efﬁcient optimization process that approximates the value of the proposed neural network estimator. Overall, we provide an effective machinery to obtain provable lower bounds for the MMSE.
Optical and Infrared Observations of Anomalous X-ray Pulsars

The detection of optical/infrared counterparts to Anomalous X-ray Pulsars (AXPs) has greatly increased our understanding of these systems. Models for the AXP phenomenon were based upon their X-ray emission, and all but the magnetar model made predictions for the optical/infrared that have now been falsified. With detections in hand, detailed studies of the optical/infrared to X-ray flux ratios, variability, and the spectral energy distributions have become possible. We present new data on two AXPs taken with Keck and Magellan, and compare the results with predictions made in the context of the magnetar model, in which the emission is due to ion currents flowing in the>10e14 G magnetosphere of young neutron stars.