Ethical transnational corporate activity at home and abroad: A proposal for reforming continuous disclosure obligations in Australia and the United States

Multinational Corporations establish operations in states with lower legal and ethical standards in areas including the environment, wages, labor standards, human rights, corruption, and company taxation. Corporate law scholars cannot be indifferent to the horrific consequences of these lax standards. From contributing to rapes and violent incidents stemming from trade in conflict minerals in the Congo to the killing of workers due to poor conditions in garment manufacturing units in Bangladesh, multinational corporations exploit conditions in developing countries abroad without disclosing their actions at home. We advance a normative argument to clarify and strengthen the existing model of disclosure-based regulation to hold MNCs accountable. We argue that, since the core expectations held by shareholders of companies are the same whether they are operating within our borders or externally, a harmonization of disclosure obligations imposed by law would be a more flexible and less costly solution. We posit that a broader reading of the disclosure obligations of companies under existing legislation like the Reg. S-K in the United States, the continuous disclosure rules under * Dean and Professor of Law, University of Newcastle Law School. Sandeep Gopalan would like to thank Terrie Troxel, Jack Tatom, Professor Bill Wilhelm, and the Networks Financial Institute at Indiana State University College of Business for their valuable support in conducting research for this article. We are also grateful to Audrey Son, Bassam Khawaja, and the editorial staff of the Columbia Human Rights Law Review for their excellent editorial work. ** Solicitor and doctoral candidate, University of Newcastle Law School. 2 COLUMBIA HUMAN RIGHTS LAW REVIEW [46.2:1 the Australian Corporations Act 2001, and listing rules such as those adopted by the Australian Securities Exchange and the New York Stock Exchange would require the disclosure of material corporate practices outside our national borders.

Corvids congregate to breeding colonies of a burrow-nesting seabird

Egg predation is a major cause of reproductive failure among birds, and can compromise the viability of affected populations. Some egg predators aggregate near colonially breeding birds to exploit the seasonal increase of prey resources. We investigated spatial and temporal variations in the abundance of an egg predator (little raven Corvus mellori; Corvidae) to identify whether ravens aggregate spatially or temporally to coincide with any of three potential prey species: burrow-nesting little penguin (Eudyptula minor; Spheniscidae), short-tailed shearwater (Ardenna tenuirostris; Procellariidae), and surface-nesting silver gull (Chroicocephalus novaehollandiae; Laridae). We derived spatially explicit density estimates of little ravens using distance sampling along line transects throughout a calendar year, which encompassed little penguin, short-tailed shearwater and silver gull breeding and non-breeding seasons. High raven abundance coincided temporally with penguin and gull egg laying periods but not with that of shearwaters. The spatial distribution of raven density corresponded with the little penguin colony but not with shearwater or gull colonies. Thus, the presence of little penguin eggs in burrows correlated strongly with little raven activity, and this implies that little ravens may have learnt to exploit the plentiful subsurface food resource of little penguin eggs. Corvid management may be required to maintain the viability of this socially and economically important penguin colony.

Influence of intrinsic variation on foraging behaviour of adult female Australian fur seals

Phenotypic variation and individual experience can create behavioural and/or dietary variation within a population. This may reduce intra-specific competition, creating a buffer to environmental change. This study examined how intrinsic variation affects foraging behaviour of Australian fur seals. Foraging movements of 29 female Australian fur seals were recorded using FastLoc GPS and dive behaviour recorders. For each individual, body mass, flipper length and axis length were recorded, a tooth was sampled to determine age, and milk was collected for diet analysis. Clustering of fatty acid dietary analysis revealed 5 distinct groups in the population. Behaviour was described using 19 indices, which were then reduced to 7 principal components (>80% of the behavioural variation). Bayesian mixed effect models were developed to describe the relationship between these components and intrinsic variation. No association was found between diet and age or body shape; however, age had a negative relationship with component 1 (27% of variation). Older females spent less time at-sea and foraged nearer to the colony. Age had an effect on component 5 (7% of variation), which represented haul-outs and dive depth; older females made fewer visits to haul-out sites and dived deeper to the benthos. This suggests that as animals age they are able to utilise prior knowledge to exploit nearby foraging sites that younger animals are either unaware of, or have yet to gain the experience required to efficiently utilise. Mass had a negative effect on components representing the directedness of a foraging trip, suggesting heavier individuals were more likely to travel directly to a foraging site.

Noisy neighbours at the frog pond: effects of invasive cane toads on the calling behaviour of native Australian frogs

Invasive species can disrupt the communication systems that native biota use for reproductive interactions. In tropical Australia, invasive cane toads (Rhinella marina) breed in many of the same waterbodies that are used by native frogs, and males of both the invader and the native taxa rely on vocal signals to attract mates. We conducted playback experiments to test the hypothesis that calls of toads may influence the calling behaviour of frogs (Limnodynastes convexiusculus and Litoria rothii). Male L. convexiusculus adjusted their calling rate and the variance in inter-call interval in response to a variety of sounds, including the calls of cane toads as well as those of other native frog species, and other anthropogenic noise, whereas L. rothii did not. Within the stimulus periods of playbacks, male L. convexiusculus called more intensely during long silent gaps than during calling blocks. Thus, males of one frog species reduced their calling rate, possibly to minimise energy expenditure during periods of acoustic interference generated by cane toads. In spite of such modifications, the number of overlapping calls (within stimulus periods) did not differ significantly from that expected by chance. In natural conditions, the calls of cane toads are continuous rather than episodic, leaving fewer gaps of silence that male frogs could exploit. Future work could usefully quantify the magnitude of temporal (e.g. diel and seasonal) and spatial overlap between calling by toads and by frogs and the impact of call-structure shifts on the ability of male frogs to attract receptive females.

Exploiting side information in Bayesian nonparametric models and their applications

 My research is to exploit side information into advanced Bayesian nonparametric models. We have developed some novel models for data clustering and medical data analysis and also have made our methods scalable for large-scale data. I have published my research in several journal and conference papers.

Avoiding competition? Site use, diet and foraging behaviours in two similarly sized geese wintering in China

Competition may occur when two species with similar feeding ecologies exploit the same limited resources in time and space. In recent years, the Eastern Tundra Bean Goose Anser fabalis serrirostris and Greater White-fronted Goose Anser albifrons frontalis have increased in wintering numbers at Shengjin Lake, China. To examine the potential for coexistence and possible avoidance strategies, we studied (1) their habitat use, (2) foraging behaviours and (3) diets of birds foraging in mixed- and single-species flocks. Both species extensively exploited sedge meadows, where they showed considerable overlap in spatial distribution and diet. The percentage feeding time and diet of both species were unaffected by the presence of the other. Greater White-fronted Geese appeared diurnal sedge meadow specialists, almost never feeding in other habitats. Eastern Tundra Bean Geese were less selective, exploiting other habitats, which they increasingly exploited at night in mid-winter. The use of alternative habitats and night feeding may have avoided interspecific competition. While the specialised feeding ecology of Greater White-fronted Geese may make them particularly vulnerable to loss of sedge meadow habitat, Eastern Tundra Bean Geese may be able to adjust because of their use of alternative habitats and a less restricted diet.

Out of sight but not out of mind: corvids prey extensively on eggs of burrow-nesting penguins

Context Egg depredation is a major cause of reproductive failure among birds and can drive population declines. In this study we investigate predatory behaviour of a corvid (little raven; Corvus mellori) that has only recently emerged, leading to widespread and intense depredation of eggs of a burrow-nesting seabird (little penguin; Eudyptula minor). Aims The main objective of this study was to measure the rate of penguin egg depredation by ravens to determine potential threat severity. We also examined whether penguin burrow characteristics were associated with the risk of egg depredation. Ravens generally employ two modes of predatory behaviour when attacking penguin nests; thus we examined whether burrow characteristics were associated with these modes of attack. Methods Remote-sensing cameras were deployed on penguin burrows to determine egg predation rates. Burrow measurements, including burrow entrance and tunnel characteristics, were measured at the time of camera deployment. Key results Overall, clutches in 61% of monitored burrows (n≤203) were depredated by ravens, the only predator detected by camera traps. Analysis of burrow characteristics revealed two distinct types of burrows, only one of which was associated with egg depredation by ravens. Clutches depredated by ravens had burrows with wider and higher entrances, thinner soil or vegetation layer above the egg chamber, shorter and curved tunnels and greater areas of bare ground and whitewash near entrances. In addition, 86% were covered by bower spinach (Tetragonia implexicoma), through which ravens could excavate. Ravens used two modes to access the eggs: they attacked through the entrance (25% of burrow attacks, n≤124); or dug a hole through the burrow roof (75% of attacks, n≤124). Burrows that were subject to attack through the entrance had significantly shorter tunnels than burrows accessed through the roof. Conclusions The high rates of clutch loss recorded here highlight the need for population viability analysis of penguins to assess the effect of egg predation on population growth rates. Implications The subterranean foraging niche of a corvid described here may have implications for burrow-nesting species worldwide because many corvid populations are increasing, and they exhibit great capacity to adopt new foraging strategies to exploit novel prey. Journal compilation

Childhood obesity and cognitive achievement

 Obese children tend to perform worse academically than normal-weight children. If poor cognitive achievement is truly a consequence of childhood obesity, this relationship has significant policy implications. Therefore, an important question is to what extent can this correlation be explained by other factors that jointly determine obesity and cognitive achievement in childhood? To answer this question, we exploit a rich longitudinal dataset of Australian children, which is linked to national assessments in math and literacy. Using a range of estimators, we find that obesity and body mass index are negatively related to cognitive achievement for boys but not girls. This effect cannot be explained by sociodemographic factors, past cognitive achievement or unobserved time-invariant characteristics and is robust to different measures of adiposity. Given the enormous importance of early human capital development for future well-being and prosperity, this negative effect for boys is concerning and warrants further investigation.

Ultra-wideline 14N solid-state NMR as a method for differentiating polymorphs: glycine as a case study

Nitrogen-14 solid-state NMR (SSNMR) is utilized to differentiate three polymorphic forms and a hydrochloride (HCl) salt of the amino acid glycine. Frequency-swept Wideband, Uniform Rate, Smooth Truncated (WURST) pulses were used in conjunction with Carr-Purcell Meiboom-Gill refocusing, in the form of the WURST-CPMG pulse sequence, for all spectral acquisitions. The ¹⁴N quadrupolar interaction is shown to be very sensitive to variations in the local electric field gradients (EFGs) about the ¹⁴N nucleus; hence, differentiation of the samples is accomplished through determination of the quadrupolar parameters CQ and ηQ, which are obtained from analytical simulations of the ¹⁴N SSNMR powder patterns of stationary samples (i.e., static NMR spectra). Additionally, differentiation of the polymorphs is also possible via the measurement of ¹⁴N effective transverse relaxation time constants, T^eff2(¹⁴N). Plane-wave density functional theory (DFT) calculations, which exploit the periodicity of crystal lattices, are utilized to confirm the experimentally determined quadrupolar parameters as well as to determine the orientation of the ¹⁴N EFG tensors in the molecular frames. Several signal-enhancement techniques are also discussed to help improve the sensitivity of the ¹⁴N SSNMR acquisition method, including the use of selective deuteration, the application of the BRoadband Adiabatic INversion Cross-Polarization (BRAIN-CP) technique, and the use of variable-temperature (VT) experiments. Finally, we examine several cases where ¹⁴N VT experiments employing Carr-Purcell-Meiboom-Gill (CPMG) refocusing are used to approximate the rotational energy barriers for RNH3⁺ groups.

Graph-induced restricted Boltzmann machines for document modeling

Discovering knowledge from unstructured texts is a central theme in data mining and machine learning. We focus on fast discovery of thematic structures from a corpus. Our approach is based on a versatile probabilistic formulation – the restricted Boltzmann machine (RBM) –where the underlying graphical model is an undirected bipartite graph. Inference is efficient document representation can be computed with a single matrix projection, making RBMs suitable for massive text corpora available today. Standard RBMs, however, operate on bag-of-words assumption, ignoring the inherent underlying relational structures among words. This results in less coherent word thematic grouping. We introduce graph-based regularization schemes that exploit the linguistic structures, which in turn can be constructed from either corpus statistics or domain knowledge. We demonstrate that the proposed technique improves the group coherence, facilitates visualization, provides means for estimation of intrinsic dimensionality, reduces overfitting, and possibly leads to better classification accuracy.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams

The need to estimate a particular quantile of a distribution is an important problem that frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semiautomatic surveillance analytics systems that detect abnormalities in close-circuit television footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We make the following several major contributions: 1) we highlight the limitations of approaches previously described in the literature that make them unsuitable for nonstationary streams; 2) we describe a novel principle for the utilization of the available storage space; 3) we introduce two novel algorithms that exploit the proposed principle in different ways; and 4) we present a comprehensive evaluation and analysis of the proposed algorithms and the existing methods in the literature on both synthetic data sets and three large real-world streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that both of the proposed methods are highly successful and vastly outperform the existing alternatives. We show that the better of the two algorithms (data-aligned histogram) exhibits far superior performance in comparison with the previously described methods, achieving more than 10 times lower estimate errors on real-world data, even when its available working memory is an order of magnitude smaller.

Novel approach for information discovery in autonomous wireless sensor networks

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Online video recommendation in sharing community

The creation of sharing communities has resulted in the astonishing increasing of digital videos, and their wide applications in the domains such as entertainment, online news broadcasting etc. The improvement of these applications relies on effective solutions for social user access to video data. This fact has driven the recent research interest in social recommendation in shared communities. Although certain effort has been put into video recommendation in shared communities, the contextual information on social users has not been well exploited for effective recommendation. In this paper, we propose an approach based on the content and social information of videos for the recommendation in sharing communities. Specifically, we first exploit a robust video cuboid signature together with the Earth Mover's Distance to capture the content relevance of videos. Then, we propose to identify the social relevance of clips using the set of users belonging to a video. We fuse the content relevance and social relevance to identify the relevant videos for recommendation. Following that, we propose a novel scheme called sub-community-based approximation together with a hash-based optimization for improving the efficiency of our solution. Finally, we propose an algorithm for efficiently maintaining the social updates in dynamic shared communities. The extensive experiments are conducted to prove the high effectiveness and efficiency of our proposed video recommendation approach.