Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Period Workload Balancing in Last-Mile Urban Delivery

In the daily dispatching of last-mile urban delivery, a delivery manager has to consider workload balance among couriers to maintain workforce morale. We consider two types of workload: incentive workload, which relates to the delivery quantity and affects a courier’s income, and effort workload, which relates to the delivery time and affects a courier’s health. Incentive workload has to be balanced over a relatively long period of time (a payroll cycle—a week or a month), whereas effort workload has to be balanced over a relatively short period of time (a shift or a day). We introduce a multi-period workload balancing problem under stochastic demand and dynamic daily dispatching, formulate it as a Markov decision process (MDP), and derive a lower bound on the optimal value of the MDP model. We propose a balanced penalty policy based on cost function approximation and use a hybrid algorithm combining the modified nested partitions method and the KN++ procedure to search for an optimal policy parameter. A comprehensive numerical study demonstrates that the proposed balanced penalty policy performs close to optimal on small instances and outperforms four benchmark policies on large instances, and provides insight into the impact of demand variation and a manager’s importance weighting of operating cost and workload balance.
ACL 2020 Workshop on Automatic Simultaneous Translation Challenges, Recent Advances, and Future Directions

Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the endto-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.
Towards Identifying Internet Applications Using Early Classification of Traffic Flow

Network traffic classification has been an interesting topic of research for many years. It plays a crucial role in many network applications including resource allocation, intrusion detection, and quality of service. Network traffic is essentially a sequence or flow of time-stamped packets that are exchanged between two devices. The traffic flow also contains payload data along with information about packet statistics such as size, inter-arrival time, and direction. As these statistics are obtained from the time-stamped packets, they form a Multivariate Time Series (MTS). Such an MTS needs to be classified as early as possible to identify an Internet application associated with the generated traffic flow. In this paper, we propose an Early traffic Flow Classification (EFC) approach for identifying Internet applications using MTS. The approach estimates application-wise minimum required packets from the training data by employing k-means clustering and Long Short Term Memory model. We also develop a class forwarding method to utilize correlation that exists among different packet statistics. Additionally, we collect a real-world traffic flow dataset to evaluate the effectiveness of the approach. Experimental results show that EFC approach requires only the first 15 packets of the flow to achieve an accuracy of more than 90%.
: DETERMINANTS AND

The development of digital technology provides opportunities for every country to increase its economic growth. Digital technologies are changing the workplace, providing a significant opportunity for nations and companies to boost productivity, efficiency, and growth. The speed of digital transformation will depend on a number of factors, including the economic and social development of a country, its placement within global supply chains, the availability of basic digital infrastructure, competition and new markets, societal acceptance of technologies, and the availability of ICT worker. This paper focuses on the last factor, which is the availability of ICT worker in Indonesia. The paper aims to confirm the determinants which affect the availability of ICT worker in Indonesia. According to this research, the availability of ICT worker in Indonesia is influenced by optimizing the utilization of ICT infrastructure, demographic conditions, economic structures, worker education levels in Indonesia, and the development of entrepreneurship in 33 provinces in Indonesia.
Implicit Structures for Graph Neural Networks

Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data. Due to the ﬁnite nature of the underlying recurrent structure, current GNN methods may struggle to capture long-range dependencies in underlying graphs. To overcome this diﬃculty, we propose a graph learning framework, called Implicit Graph Neural Networks (IGNN 1 ), where predictions are based on the solution of a ﬁxed-point equilibrium equation involving implicitly deﬁned “state” vectors. We use the Perron-Frobenius theory to derive suﬃcient conditions that ensure well-posedness of the framework. Leveraging implicit diﬀerentiation, we derive a tractable projected gradient descent method to train the framework. Experiments on a comprehensive range of tasks show that IGNNs consistently capture long-range dependencies and outperform the state-of-the-art GNN models.
Collaborative Bayesian Optimization with Fair Regret

Bayesian optimization (BO) is a popular tool for optimizing complex and costly-to-evaluate black-box objective functions. To further reduce the number of function evaluations, any party performing BO may be interested to collaborate with others to optimize the same objective function concurrently. To do this, existing BO algorithms have considered optimizing a batch of input queries in parallel and provided theoretical bounds on their cumulative regret reﬂecting inef-ﬁciency. However, when the objective function values are correlated with real-world rewards (e.g., money), parties may be hesitant to collaborate if they risk incurring larger cumulative regret (i.e., smaller real-world reward) than others. This paper shows that fairness and efﬁciency are both necessary for the collaborative BO setting. Inspired by social welfare concepts from economics, we pro-pose a new notion of regret capturing these properties and a collaborative BO algorithm whose convergence rate can be theoretically guaranteed by bounding the new regret, both of which share an adjustable parameter for trading off between fairness vs. efﬁciency. We empirically demonstrate the beneﬁts (e.g., increased fairness) of our algorithm using synthetic and real-world datasets.
Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning

Many organizations need large amounts of high quality data for their applications, and one way to acquire such data is to combine datasets from multiple parties. Since these organizations often own sensitive data that cannot be shared in the clear with others due to policy regulation and business competition, there is increased interest in utilizing secure multi-party computation (MPC). MPC allows multiple parties to jointly compute a function without revealing their inputs to each other. We present Cerebro, an end-to-end collaborative learning platform that enables parties to compute learning tasks without sharing plaintext data. By taking an end-to-end approach to the system design, Cerebro allows multiple parties with complex economic relationships to safely collaborate on machine learning computation through the use of release policies and auditing, while also enabling users to achieve good performance without manually navigating the complex performance tradeoffs between MPC protocols.
Associations between pesticide mixtures applied near home during pregnancy and early childhood with adolescent behavioral and emotional problems in the CHAMACOS study

Supplemental Digital Content is available in the text. Background: Studies suggest that exposure to pesticides during pregnancy and early childhood is associated with adverse child neurodevelopment. Research to date has focused primarily on exposure to single pesticides or pesticide classes in isolation; there are little data on the effect of exposure to pesticide mixtures on child and adolescent neurodevelopment. Methods: Using California’s Pesticide Use Reporting database, we estimated agricultural pesticide use within 1 km of the home during the prenatal and postnatal (ages 0–5 years) periods among participants in the Center for the Health Assessment for Mothers and Children of Salinas (CHAMACOS) birth cohort. We implemented a Bayesian Hierarchical linear mixed-effects model to examine associations with maternal- and youth-reported behavioral and emotional problems from the Behavior Assessment System for Children, 2nd edition (BASC-2) at ages 16 and 18 years (n = 593). Results: We observed mostly null associations between pesticide applications and neurobehavioral outcomes. There were some trends of modestly increased internalizing behaviors and attention problems in association with organophosphate insecticide use near the home during the prenatal period. In the postnatal period, a two-fold increase in glyphosate applications was associated with more youth-reported depression (β = 1.2, 95% credible intervals [CrI] = 0.2, 2.2), maternal-reported internalizing behaviors (β = 1.23, 95% CrI = 0.2, 2.3), and anxiety (β = 1.2, 95% CrI = 0.2, 2.3). We observed some protective associations with imidacloprid during the prenatal period, particularly in sex-specific analyses. Conclusions: We found only some subtle associations between some pesticides and neurobehavioral outcomes. This study extends previous work by considering potential exposure to mixtures of pesticides.
Chest ImaGenome Dataset for Clinical Reasoning

Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings. With the exception of a few expert-labeled small-scale datasets for specific findings, such as pneumonia and pneumothorax, most of the CXR deep learning models to date are trained on global"weak"labels extracted from text reports, or trained via a joint image and unstructured text learning strategy. Inspired by the Visual Genome effort in the computer vision community, we constructed the first Chest ImaGenome dataset with a scene graph data structure to describe $242,072$ images. Local annotations are automatically produced using a joint rule-based natural language processing (NLP) and atlas-based bounding box detection pipeline. Through a radiologist constructed CXR ontology, the annotations for each CXR are connected as an anatomy-centered scene graph, useful for image-level reasoning and multimodal fusion applications. Overall, we provide: i) $1,256$ combinations of relation annotations between $29$ CXR anatomical locations (objects with bounding box coordinates) and their attributes, structured as a scene graph per image, ii) over $670,000$ localized comparison relations (for improved, worsened, or no change) between the anatomical locations across sequential exams, as well as ii) a manually annotated gold standard scene graph dataset from $500$ unique patients.
Inclusion of rapeseed and pumpkin seed cakes in diets for Murciano-Granadina goats alters the fatty acid profile of milk

______________________________________________________________________________________ Abstract The objective of this research was to assess the effects of including oil-rich feedstuffs in diets for lactating goats on the fatty acid (FA) profile of their milk. Thirty-six Murciano-Granadina goats were randomly assigned to three treatment groups, namely a control diet (CTRL), a diet based on whole rapeseed (RS), and a diet based on pumpkin seed cake (PSC). The diets were composed of 1 kg hay (70 % Italian ryegrass, 30% alfalfa) and 1.24 kg concentrate, and were formulated to be isoenergetic and isonitrogenous. Milk yield and its contents of protein, fat and lactose did not differ significantly among the groups. However, including oil-rich feeds in the diet altered the fatty acid profile of the milk significantly, decreasing its saturated fatty acid (SFA) content and increasing its content of unsaturated fatty acids (UFAs). Effects on polyunsaturated fatty acids (PUFAs), conjugated linoleic acid (CLA), and the n-6 to n-3 ratio depended on the source of dietary lipids. The PSC augmented diet increased the relative amount of PUFAs and fatty acid methyl esters (FAME) in milk (+25 %) significantly In comparison with CTRL, whereas the RS diet produced a limited and statistically insignificant increase (+7.5%). The concentration of CLA was higher in milk from does fed the PSC diet, whereas the n-6 to n-3 ratio was lower in milk from does fed RS. These preliminary results form the basis for developing premium dairy products that are enriched in fatty acids that are more favourable for human health. ______________________________________________________________________________________
Indistinguishability of Quantum States is Independent of the Dimension of Quantum System

The distinguishability of quantum states is important in quantum information theory and has been considered by authors. However, there were no works considered whether a set of indistinguishable states become distinguishable by viewing them in a larger system. In this paper, we consider this question for LOCC1, PPT and SEP distinguishability of states. We use mathematical methods to show that if a set of states is indistinguishable in ⊗Kk=1C dk , then it is indistinguishable even viewing in ⊗Kk=1C dk+hk , where K, dk > 2, hk > 0 are integers. This show that LOCC1, PPT and SEP distinguishability of states are properties of states only and independent of the dimension of quantum system. We use this to give the maximal number of states that can be distinguished via LOCC1 and construct a LOCC indistinguishable basis of product states in a general system. Note that our results are also suitable for unambiguous discriminations. Also, we give a conjecture for other distinguishabilities and a framework by defining the Local-Global indistinguishable property.
The Paradoxical Profession: Project Management and the Contradictory Nature of Sustainable Project Objectives

Professions are undergoing a significant change in how they integrate environmental and social objectives into their core values. This article examines the situation in which those working in the project management profession are expected to work under contradictory sustainability constraints. In this article, we investigate the tensions project managers experience when addressing sustainable objectives. Results show that when tensions arise over sustainable objectives (temporality of objectives, organizational barriers, and lack of control), they are addressed only when anchored to an economic one in the form of a business case for sustainability. We also find that when matching traditional project objectives with sustainable ones is not possible, practitioners enact a set of reactions characterized as greenwashing, it can’t be one person, no space for sustainability in my job, other actors involved, or pushing back, depending on the specific project context. Adopting the paradox theory lens, we provide an alternative approach to the business case for sustainability. The practical contribution of this article lies in suggesting the need to find strategies to embrace paradoxical situations and we provide some suggestions to illustrate this.
A Review of Neurological Involvement in Patients with SARS-CoV-2 Infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative pathogen of the recent pandemic of coronavirus disease 19 (COVID-19). As the infection spreads, there is increasing evidence of neurological and psychiatric involvement in COVID-19. Headache, impaired consciousness, and olfactory and gustatory dysfunctions are common neurological manifestations described in the literature. Studies demonstrating more specific and more severe neurological involvement such as cerebrovascular insults, encephalitis and Guillain-Barré syndrome are also emerging. Respiratory failure, a significant condition that leads to mortality in COVID-19, is hypothesized to be partly due to brainstem impairment. Notably, some of these neurological complications seem to persist long after infection. This review aims to provide an update on what is currently known about neurological involvement in patients with COVID-19 due to SARS-CoV-2 infection. In this review, we demonstrate invasion routes of SARS-CoV-2, provide evidence to support the neurotropism hypothesis of the virus, and investigate the pathological mechanisms that underlie neurological complications associated with SARS-CoV-2.
CARTL: Cooperative Adversarially-Robust Transfer Learning

Transfer learning eases the burden of training a well-performed model from scratch, especially when training data is scarce and computation power is limited. In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain. Previous work focuses on the accuracy of the transferred model but neglects the transfer of adversarial robustness. In this work, we first show that transfer learning improves the accuracy on the target domain but degrades the inherited robustness of the target model. To address such a problem, we propose a novel cooperative adversarially-robust transfer learning (CARTL) by pre-training the model via feature distance minimization and fine-tuning the pre-trained model with non-expansive fine-tuning for target domain tasks. Empirical results show that CARTL improves the inherited robustness by about 28% at most compared with the baseline with the same degree of accuracy. Furthermore, we study the relationship between the batch normalization (BN) layers and the robustness in the context of transfer learning, and we reveal that freezing BN layers can further boost the robustness transfer.
Efficacy of the entomopathogenic fungus, Beauveria bassiana as biological control agent of black cutworm, Agrotis ipsilon hufnagel and compatibility with chemical insecticides

Beauveria bassiana and four chemical insecticides were tested for their potentiality against Agrotis ipsilon 3 instar larvae under laboratory conditions. In order to derive maximum benefits from applying both of entomopathogenic fungus and chemical insecticides in IPM program for controlling A. ipsilon larvae, compatibility of B. bassiana with the tested chemical insecticides were studied. It was found that, under laboratory conditions, except buprofezin which showed slight harmful effect on the fungus mycelial growth (51.73% growth inhibition), all other insecticides showed harmless effect. Also, the virulence of B. bassiana spores harvested from SDYA poisoned with LC90 of the tested insecticides was evaluated against A. ipsilon 3 instar larvae under laboratory conditions. All treatments dramatically lost their virulence except those spores harvested from thiamethoxam-poisoned SDYA which slightly affected negatively. Moreover, it was found that all tested insecticides at sub-lethal doses (LC50) were compatible with B. bassiana under semi-field conditions. So, the combination of the tested insecticides at the sub-lethal doses with B. bassiana in IPM programs was recommended.
Few-Shot Class-Adaptive Anomaly Detection with Model-Agnostic Meta-Learning

Anomaly detection in encrypted traffic is a growing problem, and many approaches have been proposed to solve it. However, those approaches need to be trained in the massive of normal traffic and specific-class abnormal traffic, so to achieve good results in that specific-class. For a new anomaly class with few labeled samples, the effectiveness of existing approaches will decline sharply. How to train a model using only a few anomaly samples to detect unseen new anomaly classes in training is a huge challenge. In this paper, we propose a Few-shot Class-adaptive Anomaly Detection framework (FCAD) with model-agnostic meta-learning (MAML) to meet this challenge. Given an input network flow, FCAD first extracts statistical features by feature extractor and feature selector, and time-series features using LSTM-based AutoEncoder. Then, FCAD designs a MAML-based few-shot anomaly detection model, relying on the episodic training paradigm and learning from the collection of K-way-M-shot classification tasks, which can mimic the few-shot regime faced at test time during training. Finally, FCAD uses the pre-trained model to adapt the new class by a few iterations steps. Our goal is to detect anomaly traffic in a before unseen anomaly class with only a few samples. A reliable solution to few-shot anomaly detection will have huge potential for real-world applications since it is expensive and arduous to collect a massive amount of data onto the new anomaly class; extensive experimental results demonstrate the effectiveness of our proposed approach.
Habitat selection by fruit flies (Diptera: Tephritidae) in a tropical agroecosystem in Papua New Guinea

Habitat selection by fruit flies (Diptera: Tephritidae) along disturbance gradients of tropical agroecosystems has ecological significance. There have been extensive studies of fruit flies in agricultural systems and in natural forests. Here we examined the species preference regarding three vegetation types: cocoa plantation, secondary forest and primary forest, using methyl eugenol (ME) and cue-lure (Cue). We also quantified the efficiency of the two para-pheromones for fruit fly sampling within different habitats. The total number of individuals collected were 685 respectively in cocoa plantation, 1338 in primary reserved forest and 1589 in secondary forest. Species diversity reached its highest value in cocoa plantation (H=3.59) followed by secondary forest (H=3.17) and least diverse in primary forest (H=2.94). Total species richness (pooled data per site) was identical in primary forest and secondary forest (10 species each) but slightly higher in cocoa plantation (12 species). No individuals of Bactrocera kirki and Bactrocera obliqua were trapped with ME but only in cue-lure. Vice versa, nil individuals of Bactrocera curvifera and Bactrocera laticaudus were attracted to cue-lure but only to ME. Dacus longicornis (1.09±0.162), Zeugodacus cucurbitae (3.00±0.61) and Zeugodacus sp.n (1.88±0.35) were more responsive to cue-lure than to ME (0.181±0.121, 1.750±0.463, 0.592±0.186). Temperature (Fvalue = 11.34, p<0.001) and vegetation (F-value = 3.33, p<0.05) had significant effect on the number of individuals trapped in cure-lure. Cue-lure abundance was positively correlated with rainfall, however, there was no significant interaction effect (F-value = 0.50, p>0.05). Cue-lure abundance was nonsignificantly correlated with relative humidity (r= -0.09, p>0.05). All three abiotic factors did not have any significant interaction with vegetation on ME abundance (p>0.05). This study finds that there is low species composition within a typical tropical agroecosystem and thus similar species can be widely distributed across a contiguous habitat. However, the abundance was significant among the three study habitats. Temperature, wet season and seasonal fruit availability exerted ecological impact on fruit fly population and their distribution among the three study sites.