Miracle-dz/UniFAR-FTUs huggingface dataset:
The “ industrial halo ” and its impact on the assessment of corporate reputation

Purpose – The gap that this research attempts to fill is to analyse the explanatory factor “ industry ” when assessingthereputationofacorporategroup.Inotherwords,thisresearchattemptstodemonstratetheimpactof the “ industrialhalo ” ontheassessmentofcorporatereputation,giventhat,todate,theacademicliteraturehasnot considered industry as an explanatory variable in the assessment of the reputation of private companies. Design/methodology/approach – A sample of 43 Spanish companies was used to analyse the relationship between the reputation of firms as measured by the Merco Empresas index, and the industries to which they belong, after controlling for company performance, size, turnover, public recognition of their leadership, and corporate responsibility. This involved conducting a cross-sectional analysis of the relationship between the variables for each year in the time period from 2005 to 2016. The available data were taken from the firms ’ annual financial reports and websites, as well as from the Merco. Findings – The paper shows the existence of industrial halos that account for the corporate reputation of businesses in Spain. It is also shown that industrial halos are not permanent over time, and that they tend to occur in years of crisis. Researchlimitations/implications – Itwouldhavebeendesirableforthisstudytohavehadsufficientdata to include other industries, but this was not possible. As for possible extensions, in addition to expanding the periodconsidered,otheranalyticaltechniques,suchaspaneldatamodels,couldbeappliedtoallowcomparison withtheresultsobtainedhere. Practical and social implications – The results of this study have some practical implications. Firstly, firms that publish corporate reputation rankings should be aware of the distortion that the industrial halo can produce, especially in times of uncertainty, and seek to correct for it in their measurements. And secondly, corporate groups themselves should assume that the reputation of the industry affects their individual reputation, and consequently, they should see the other companies in the industry not only as competitors but also as “ reputational allies ” . Theyshouldtherefore makecollectiveeffortsto improvein thisrespect, especially in the face of reputational crises. Originality/value – Thispaperprovidesabetterunderstandingoftherelationshipbetweenthereputation of a company and the industry to which it belongs, and of its permanence over time. This relationship has been little studied in the Spanish market to date.
DEVELOPMENT OF TEACHING MATERIALS WITH ELICITING ACTIVITIES MODELS BASED ON ETHNOMATHEMATICS TO IMPROVE MATHEMATICAL LITERACY ABILITY

This research is a development research (R & D) which aims to produce a mathematics teaching material with ethnomathematics-based eliciting activities. The method used in this study is a development research method with five stages as follows: (1) Preliminary Investigation, (2) Design, (3) Realization/Construction, (4) Test -Evaluation and Revision (Test, Evaluation and Revision) and (5) Implementation (Implementation) but in this study, development was only carried out up to the first 4 stages because the implementation stage required a long process and time. The results of the validation of teaching materials and other tools by mathematicians and media experts show an average score of 4.46 which is classified as very good. and students, then a trial was carried out to see the effectiveness of learning through a mathematical literacy ability test where the results showed an average completeness of 80.25% in the good category. The conclusion of this study is that mathematics teaching materials using the eliciting activities model based on ethnomathematics are categorized as good and appropriate for use in teaching mathematics in junior high schools.Keywords: teaching materials, eliciting activities models, ethnomathematics, mathematical literacy skills Abstrak Penelitian ini merupakan penelitian pengembangan (R & D) yang bertujuan untuk menghasilkan suatu bahan ajar matematika dengan eliciting actvities berbasis etnomatematika. Metode yang digunakan dalam penelitian ini merupakan metode penelitian pengembangan dengan lima tahapan sebagai berikut: (1) investigasi awal (Preliminary Investigation), (2) Perancangan (Design), (3) Realisasi/Konstruksi (Realization/Construction), (4) Tes-Evaluasi dan Revisi (Test, Evaluation and Revision) dan (5) Implementasi (Implementation) namun Pada penelitian ini, pengembangan hanya dilakukan sampai pada 4 tahapan awal karena pada tahap implementasi memerlukan proses dan waktu yang cukup lama. Hasil validasi bahan ajar beserta perangkat lainnya oleh ahli matematika dan ahli media menunjukkan rata-rata skor 4,46 yang tergolong dalam kategori sangat baik, hasil uji coba kepraktisan bahan ajar dan perangkat lainnya menunjukkan bahwa semuanya dalam kategori praktis hal ini ditunjukkan melalui data aktivitas guru dan siswa, selanjutnya dilakukan uji coba untuk melihat keefektifan pembelajaran melalui tes kemampuan literasi matematika dimana hasil menunjukkan rata-rata ketuntasan 80,25% dengan kategori baik. Kesimpulan dari penelitian ini adalah bahan ajar matematika dengan model eliciting activities berbasis etnomatematika dikategorikan baik dan layak untuk digunakan dalam pembelajaran matematika di sekolah menengah pertama.Kata Kunci: Bahan Ajar, Model Eliciting Activities, Etnomatematika, Kemampuan Literasi Matematika
Phase-head Initiated Structure Building: Its Implications for Feature-Inheritance, Transfer and Internal Merge

This paper explores an alternative way of structure building in minimalism and proposes that along with other operations in Narrow Syntax such as Feature-Inheritance and Transfer, structure building is also initiated only by phase heads. Consequently, this paper takes one step further Chomsky’s (2007, 2008, 2013) generalization that all operations in Narrow Syntax are restricted to the phase level. It further investigates the implications of phase-head initiated structure building for the motivations for Feature-Inheritance and Transfer (Chomsky 2007, 2008, Richards 2007) and shows 1) that no derivation can converge at the C-I interface without Feature-Inheritance (i.e., Feature-Inheritance is necessitated to satisfy interface conditions) and 2) that the operation Transfer is a natural by-product of (Internal) Merge.
Objective Monitoring of Loneliness Levels using Smart Devices: A Multi-Device Approach for Mental Health Applications

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individuals loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). We also collected participants loneliness feeling scales multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications
Detection of multidrug resistant organisms of concern including S. maltophilia and B. cepacia at a referral hospital in Kenya

It is important to monitor of antibiotic susceptibility patterns of bacteria in clinical settings periodically to ascertain the current trends as well as re-establish empirical therapy. This study aimed to determine bacterial contaminants and their antimicrobial susceptibility patterns from medical equipment, inanimate surfaces and clinical samples isolated from Thika Level V Hospital (TLVH). Three hundred and five samples were collected and comprised of urine, pus swabs, catheter swabs, stool and environmental samples. Bacterial identification and antimicrobial susceptibility testing were done on VITEK 2 and disc diffusion respectively. Coagulase negative Staphylococci (28 /160, 17.5%) were the most isolated species from patients followed by E. coli (22 /160 13.8%) and S. aureus (22/160, 13.8%). The bed rail was the most contaminated surface with S. aureus at (6/42)14.2%. The clinical sample that yielded the highest number of pathogens was pus (92/160). Trauma patients had the largest proportion of isolates (67/160, 41.8%). Bacteria recovered from this study demonstrated high levels of resistance especially enteric bacteria. Extended Spectrum Beta Lactamase phenotype was noted in 29/44 (65.9%) enteric isolates. Although ESBL genetic confirmatory studies are needed, this study shows that there is an urgent need for actions that mitigate the spread of antibiotics resistant bacteria.
Identification of Antibiotic Resistance Pattern and Antibiotic Resistance Gene of Staphylococcus aureus from Broiler Chicken Farm in Sukabumi and Cianjur Regency, West Java Province, Indonesia

Broiler chicken is one of the most common forms of protein in Indonesia. Sukabumi and Cianjur are two of the province of West Java’s top producers of broiler chickens. Farmers nonetheless use antibiotics for prevention, treatment, and growth enhancement. The objective of this investigation is to detect antibiotic resistance and the related resistance gene. The cloacal swab samples were determined to be Staphylococcus aureus by biochemical and molecular assays. Against the positive S. aureus strains, the antibiotics tetracycline, ampicillin, gentamicin, nalidixic acid, ciprofloxacin, erythromycin, and chloramphenicol were evaluated. The colony’s obvious inhibitory zone was evaluated in accordance with the 2018 Clinical and Laboratory Standard Institute recommendations (CLSI 2018). After extracting the DNA from resistant strains, the polymerase chain reaction (PCR) method was employed to identify the resistance gene. The total number of S. aureus isolates from both regions was 26, with 14 coming from Sukabumi and 12 from Cianjur. The vast majority were resistant to the vast majority of antibiotics used in broiler farms including several medicines. These samples carried the genes blaTEM, gyrA, tetA, and ermB for antibiotic resistance.
Reed–Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for all Rates Below Capacity

This paper considers the performance of Reed–Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori (bit-MAP) decoding. Its main result is that, for a fixed BMS channel, the family of binary RM codes can achieve a vanishing bit-error probability at rates approaching the channel capacity. This partially resolves a long-standing open problem that connects information theory and error-correcting codes. In contrast with the earlier result for the binary erasure channel, the new proof does not rely on hypercontractivity. Instead, it combines a nesting property of RM codes with new information inequalities relating the generalized extrinsic information transfer function and the extrinsic minimum mean-squared error.
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning

Self-supervised learning (SSL) has recently achieved tremendous empirical advancements in learning image representation. However, our understanding of the principle behind learning such a representation is still limited. This work shows that joint-embedding SSL approaches primarily learn a representation of image patches, which reflects their co-occurrence. Such a connection to co-occurrence modeling can be established formally, and it supplements the prevailing invariance perspective. We empirically show that learning a representation for fixed-scale patches and aggregating local patch representations as the image representation achieves similar or even better results than the baseline methods. We denote this process as BagSSL. Even with 32x32 patch representation, BagSSL achieves 62% top-1 linear probing accuracy on ImageNet. On the other hand, with a multi-scale pretrained model, we show that the whole image embedding is approximately the average of local patch embeddings. While the SSL representation is relatively invariant at the global scale, we show that locality is preserved when we zoom into local patch-level representation. Further, we show that patch representation aggregation can improve various SOTA baseline methods by a large margin. The patch representation is considerably easier to understand, and this work makes a step to demystify self-supervised representation learning.
Generative Graphical Inverse Kinematics

Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for many robot manipulators. Existing numerical solvers are broadly applicable but typically only produce a single solution and rely on local search techniques to minimize nonconvex objective functions. More recent learning-based approaches that approximate the entire feasible set of solutions have shown promise as a means to generate multiple fast and accurate IK results in parallel. However, existing learning-based techniques have a significant drawback: each robot of interest requires a specialized model that must be trained from scratch. To address this key shortcoming, we propose a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the sample efficiency of Euclidean equivariant functions and the generalizability of graph neural networks (GNNs). Our approach is generative graphical inverse kinematics (GGIK), the first learned IK solver able to accurately and efficiently produce a large number of diverse solutions in parallel while also displaying the ability to generalize -- a single learned model can be used to produce IK solutions for a variety of different robots. When compared to several other learned IK methods, GGIK provides more accurate solutions with the same amount of data. GGIK can generalize reasonably well to robot manipulators unseen during training. Additionally, GGIK can learn a constrained distribution that encodes joint limits and scales efficiently to larger robots and a high number of sampled solutions. Finally, GGIK can be used to complement local IK solvers by providing reliable initializations for a local optimization process.
Attention-based Modeling of Physical Systems: Improved Latent Representations

We propose attention-based modeling of quantities at arbitrary spatial points conditioned on related measurements at different locations. Our approach adapts a transformer-encoder to process measurements and read-out positions together. Attention-based models exhibit excellent performance across domains, which makes them an interesting candidate for modeling data irregularly sampled in space. We introduce a novel encoding strategy that applies the same transformation to the measurements and read-out positions, after which they are combined with encoded measurement values instead of relying on two different mappings. Efficiently learning input-output mappings from irregularly-spaced data is a fundamental challenge in modeling physical phenomena. To evaluate the effectiveness of our model, we conduct experiments on diverse problem domains, including high-altitude wind nowcasting, two-days weather forecasting, fluid dynamics, and heat diffusion. Our attention-based model consistently outperforms state-of-the-art models, such as Graph Element Networks and Conditional Neural Processes, for modeling irregularly sampled data. Notably, our model reduces root mean square error (RMSE) for wind nowcasting, improving from 9.24 to 7.98 and for a heat diffusion task from . 126 to . 084. We hypothesize that this superior performance can be attributed to the enhanced flexibility of our latent representation and the improved data encoding technique. To support our hypothesis, we design a synthetic experiment that reveals excessive bottlenecking in the latent representations of alternative models, which hinders information utilization and impedes training.
Constraining Dark Boson Decay Using Neutron Stars

Inspired by the well-known anomaly in the lifetime of the neutron, we investigated its consequences inside neutron stars. We first assessed the viability of the neutron decay hypothesis suggested by Fornal and Grinstein within neutron stars, in terms of the equation of state and compatibility with observed properties. This was followed by an investigation of the constraint information on neutron star cooling that can be placed on the decay rate of the dark boson into standard model particles, in the context of various BSM ideas.
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning

We consider a cross-silo federated learning (FL) setting where a machine learning model with a fully connected first layer is trained between different clients and a central server using FedAvg, and where the aggregation step can be performed with secure aggregation (SA). We present SRATTA an attack relying only on aggregated models which, under realistic assumptions, (i) recovers data samples from the different clients, and (ii) groups data samples coming from the same client together. While sample recovery has already been explored in an FL setting, the ability to group samples per client, despite the use of SA, is novel. This poses a significant unforeseen security threat to FL and effectively breaks SA. We show that SRATTA is both theoretically grounded and can be used in practice on realistic models and datasets. We also propose counter-measures, and claim that clients should play an active role to guarantee their privacy during training.
Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model

News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. Our model contributes to the literature in several directions: 1) we provide a model-embedded data-driven interpretation for the latent leaning of news outlets in terms of media bias; 2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered.
Linking Langevin equation to scaling properties of space plasma turbulence at sub-ion scales

Current understanding of the kinetic-scale turbulence in weakly-collisional plasmas still remains elusive. We employ a general framework in which the turbulent energy transfer is envisioned as a scale-to-scale Langevin process. Fluctuations in the sub-ion range show a global scale invariance, thus suggesting a homogeneous energy repartition. In this Letter, we interpret such a feature by linking the drift term of the Langevin equation to scaling properties of fluctuations. Theoretical expectations are verified on solar wind observations and numerical simulations thus giving relevance to the proposed framework for understanding kinetic-scale turbulence in space plasmas.
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning

Offline constrained reinforcement learning (RL) aims to learn a policy that maximizes the expected cumulative reward subject to constraints on expected cumulative cost using an existing dataset. In this paper, we propose Primal-Dual-Critic Algorithm (PDCA), a novel algorithm for offline constrained RL with general function approximation. PDCA runs a primal-dual algorithm on the Lagrangian function estimated by critics. The primal player employs a no-regret policy optimization oracle to maximize the Lagrangian estimate and the dual player acts greedily to minimize the Lagrangian estimate. We show that PDCA can successfully find a near saddle point of the Lagrangian, which is nearly optimal for the constrained RL problem. Unlike previous work that requires concentrability and a strong Bellman completeness assumption, PDCA only requires concentrability and realizability assumptions for sample-efficient learning.
Revisiting and Advancing Adversarial Training Through A Simple Baseline

In this paper, we delve into the essential components of adversarial training which is a pioneering defense technique against adversarial attacks. We indicate that some factors such as the loss function, learning rate scheduler, and data augmentation, which are independent of the model architecture, will influence adversarial robustness and generalization. When these factors are controlled for, we introduce a simple baseline approach, termed SimpleAT, that performs competitively with recent methods and mitigates robust overfitting. We conduct extensive experiments on CIFAR-10/100 and Tiny-ImageNet, which validate the robustness of SimpleAT against state-of-the-art adversarial attackers such as AutoAttack. Our results also demonstrate that SimpleAT exhibits good performance in the presence of various image corruptions, such as those found in the CIFAR-10-C. In addition, we empirically show that SimpleAT is capable of reducing the variance in model predictions, which is considered the primary contributor to robust overfitting. Our results also reveal the connections between SimpleAT and many advanced state-of-the-art adversarial defense methods.
Multi-Interactive-Modality Based Modeling for Myopia Pro-Gression of Adolescent Student

Myopia is a common visual disorder that affects millions of people worldwide and its prevalence has been increasing in recent years. Environmental factors, such as reading time, viewing distance, and ambient lighting, have been identified as potential factors in the development of myopia. In this study, we investigated the relationship between three major factors and myopia in 120 adolescents. By collecting environmental images of the adolescents in the learning state as well as retinal fundus images, we proposed an environmental visual load (EVL) model to extract the potential information in these images. Through experimental data analysis, we found that these three major factors are closely related to the severity of myopia, and that the simultaneous exacerbation of these factors sharply increases the myopia of the eye. Our results suggest that interventions targeting these environmental factors may help prevent and manage myopia.