The FLSA Permission Slip: Determining Whether FLSA Settlements and Voluntary Dismissals Require Approval

The Fair Labor Standards Act of 1938 (FLSA) seeks to protect the poorest, most vulnerable workers by requiring that they be paid a minimum wage and compensated for their overtime labor. When employers do not pay their workers minimum wage or overtime compensation and thereby violate the FLSA, workers have the power to sue their employers for remuneration. Like many other types of cases, most FLSA cases settle before going to trial. Unlike those other types of cases, however, most courts have held that settlements of FLSA cases must be approved to be enforceable. Even though Federal Rule of Civil Procedure 41 generally allows parties to settle lawsuits by voluntarily dismissing their lawsuits without approval, these courts have held that the FLSA should be an exception to Rule 41. Some courts, however, have held that settlements of FLSA cases should not require approval to be enforceable. This Note addresses and analyzes the differences between these approaches. It seeks to balance the protection the FLSA intends to provide workers and the ability of parties to freely settle disputes embodied in Rule 41. To strike this balance, this Note suggests that settlements of lawsuits brought under the FLSA should not require approval, because the Act should be subject to and not exempt from Rule 41. However, settlements of causes of action arising under the FLSA should require approval to ensure the necessary protection the Act was meant to provide to the workers it serves

Assessing AI output in legal decision-making with nearest neighbors

Artificial intelligence (“AI”) systems are widely used to assist or automate decision-making. Although there are general metrics for the performance of AI systems, there is, as yet, no well-established gauge to assess the quality of particular AI recommendations or decisions. This presents a serious problem in the emerging use of AI in legal applications because the legal system aims for good performance not only in the aggregate but also in individual cases. This Article presents the concept of using nearest neighbors to assess individual AI output. This nearest neighbor analysis has the benefit of being easy to understand and apply for judges, lawyers, and juries. In addition, it is fundamentally compatible with existing AI methodologies. This Article explains how the concept could be applied for probing AI output in a number of use cases, including civil discovery, risk prediction, and forensic comparison, while also presenting its limitations

Online VNF Scaling in Datacenters

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure

StreetlightSim: a simulation environment to evaluate networked and adaptive street lighting

Sustaining the operation of street lights incurs substantial financial and environmental cost. Consequently, adaptive lighting systems have been proposed incorporating ad-hoc networking, sensing, and data processing, in order to better manage the street lights and their energy demands. Evaluating the efficiency and effectiveness of these complex systems requires the modelling of vehicles, road networks, algorithms, and communication systems, yet tools are not available to permit this. This paper proposes StreetlightSim, a novel simulation environment combining OMNeT++ and SUMO tools to model both traffic patterns and adaptive networked street lights. StreetlightSim’s models are illustrated through the simulation of a simple example, and a more complex scenario is used to show the potential of the tool and the obtainable results. StreetlightSim has been made open-source, and hence is available to the community

BONNSAI: a Bayesian tool for comparing stars with stellar evolution models

Powerful telescopes equipped with multi-fibre or integral field spectrographs combined with detailed models of stellar atmospheres and automated fitting techniques allow for the analysis of large number of stars. These datasets contain a wealth of information that require new analysis techniques to bridge the gap between observations and stellar evolution models. To that end, we develop BONNSAI (BONN Stellar Astrophysics Interface), a Bayesian statistical method, that is capable of comparing all available observables simultaneously to stellar models while taking observed uncertainties and prior knowledge such as initial mass functions and distributions of stellar rotational velocities into account. BONNSAI can be used to (1) determine probability distributions of fundamental stellar parameters such as initial masses and stellar ages from complex datasets, (2) predict stellar parameters that were not yet observationally determined and (3) test stellar models to further advance our understanding of stellar evolution. An important aspect of BONNSAI is that it singles out stars that cannot be reproduced by stellar models through $\chi^{2}$ hypothesis tests and posterior predictive checks. BONNSAI can be used with any set of stellar models and currently supports massive main-sequence single star models of Milky Way and Large and Small Magellanic Cloud composition. We apply our new method to mock stars to demonstrate its functionality and capabilities. In a first application, we use BONNSAI to test the stellar models of Brott et al. (2011a) by comparing the stellar ages inferred for the primary and secondary stars of eclipsing Milky Way binaries. Ages are determined from dynamical masses and radii that are known to better than 3%. We find that the stellar models reproduce the Milky Way binaries well. BONNSAI is available through a web-interface at http://www.astro.uni-bonn.de/stars/bonnsai.Comment: Accepted for publication in A&A; 15 pages, 10 figures, 4 tables; BONNSAI is available through a web-interface at http://www.astro.uni-bonn.de/stars/bonnsa

Design, synthesis and in vitro degradation of a novel co-drug for the treatment of psoriasis

Psoriasis is a common, chronic and relapsing inflammatory skin disease. It affects approximately 2% of the western population and has no cure. Combination therapy for psoriasis often proves more efficacious and better tolerated than monotherapy with a single drug. Combination therapy could be administered in the form of a co-drug, where two or more therapeutic compounds active against the same condition are linked by a cleavable covalent bond. Similar to the pro-drug approach, the liberation of parent moieties post-administration, by enzymatic and/or chemical mechanisms, is a pre-requisite for effective treatment. In this study, a series of co-drugs incorporating dithranol in combination with one of several non-steroidal anti-inflammatory drugs, both useful for the treatment of psoriasis, were designed, synthesized and evaluated. An ester co-drug comprising dithranol and naproxen in a 1:1 stoichiometric ratio was determined to possess the optimal physicochemical properties for topical delivery. The co-drug was fully hydrolyzed in vitro by porcine liver esterase within four hours. When incubated with homogenized porcine skin, 9.5% of the parent compounds were liberated after 24 h, suggesting in situ esterase-mediated cleavage of the co-drug would occur within the skin. The kinetics of the reaction revealed first order kinetics, Vmax = 10.3 μM/min and Km = 65.1 μM. The co-drug contains a modified dithranol chromophore that was just 37% of the absorbance of dithranol at 375 nm and suggests reduced skin/clothes staining. Overall, these findings suggest that the dithranol-naproxen co-drug offers an attractive, novel approach for the treatment of psoriasis

Selectively Interfering With Intrusive but Not Voluntary Memories of a Trauma Film: Accounting for the Role of Associative Memory.

Intrusive memories of a traumatic event can be reduced by a subsequent interference procedure, seemingly sparing voluntary memory for that event. This selective-interference effect has potential therapeutic benefits (e.g., for emotional disorders) and legal importance (e.g., for witness testimony). However, the measurements of intrusive memory and voluntary memory typically differ in the role of associations between a cue and the emotional memory "hotspots." To test this, we asked participants to watch a traumatic film followed by either an interference procedure (reminder plus Tetris) or control procedure (reminder only). Measurement of intrusions (using a laboratory task) and voluntary memory (recognition for film stills) were crossed with the presence or absence of associative cues. The reminder-plus-Tetris group exhibited fewer intrusions despite comparable recognition memory, replicating the results of prior studies. Note that this selective interference did not appear to depend on associative cues. This involuntary versus voluntary memory dissociation for emotional material further supports separate-trace memory theories and has applied advantages

Multiple memory systems, multiple time points: how science can inform treatment to control the expression of unwanted emotional memories.

Memories that have strong emotions associated with them are particularly resilient to forgetting. This is not necessarily problematic, however some aspects of memory can be. In particular, the involuntary expression of those memories, e.g. intrusive memories after trauma, are core to certain psychological disorders. Since the beginning of this century, research using animal models shows that it is possible to change the underlying memory, for example by interfering with its consolidation or reconsolidation. While the idea of targeting maladaptive memories is promising for the treatment of stress and anxiety disorders, a direct application of the procedures used in non-human animals to humans in clinical settings is not straightforward. In translational research, more attention needs to be paid to specifying what aspect of memory (i) can be modified and (ii) should be modified. This requires a clear conceptualization of what aspect of memory is being targeted, and how different memory expressions may map onto clinical symptoms. Furthermore, memory processes are dynamic, so procedural details concerning timing are crucial when implementing a treatment and when assessing its effectiveness. To target emotional memory in its full complexity, including its malleability, science cannot rely on a single method, species or paradigm. Rather, a constructive dialogue is needed between multiple levels of research, all the way 'from mice to mental health'.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'.We are grateful to the Royal Society for their support of the costs of attending this meeting ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists' convened by Amy L. Milton and Emily A. Holmes. R.M.V. is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 705641 (SUAI/023/RG92025). A.L.-Z. was supported by a Cambridge International Scholarship awarded by the Cambridge Commonwealth, European and International Trust. R.N.H. is supported by the UK Medical Research Council Programme (SUAI/010/ RG91365). E.A.H. receives support from the Karolinska Institutet and the Lupina Foundation. Funding to pay the Open Access publication charges for this article was provided by the UK Medical Research Council (SUAI/013/ RG91365)