Quadratically-Regularized Optimal Transport on Graphs

Optimal transportation provides a means of lifting distances between points on a geometric domain to distances between signals over the domain, expressed as probability distributions. On a graph, transportation problems can be used to express challenging tasks involving matching supply to demand with minimal shipment expense; in discrete language, these become minimum-cost network flow problems. Regularization typically is needed to ensure uniqueness for the linear ground distance case and to improve optimization convergence; state-of-the-art techniques employ entropic regularization on the transportation matrix. In this paper, we explore a quadratic alternative to entropic regularization for transport over a graph. We theoretically analyze the behavior of quadratically-regularized graph transport, characterizing how regularization affects the structure of flows in the regime of small but nonzero regularization. We further exploit elegant second-order structure in the dual of this problem to derive an easily-implemented Newton-type optimization algorithm.Comment: 27 page

Measuring efficiency of Tunisian schools in the presence of quasi-fixed inputs: A bootstrap data envelopment analysis approach

The objective of this paper is to measure the efficiency of high schools in Tunisia. We use a statistical Data Envelopment Analysis (DEA)-bootstrap approach with quasi-fixed inputs to estimate the precision of our measure. To do so, we developed a statistical model serving as the foundation of the Data Generation Process (DGP). The DGP is constructed such that we can implement both smooth homogeneous and heterogeneous bootstrap methods. Bootstrap simulations were used to estimate and correct the bias, and to construct confidence intervals for the efficiency measures. The simulation results show that the efficiency measures are subject to sampling variations. The adjusted measure reveals that high schools with residence services would have to give up less than 12.1 percent of their resources on average to be efficient.Educational economics; Efficiency; Productivity; Data Envelopment Analysis; Bootstrap; Quasi-fixed inputs

Measuring efficiency of Tunisian schools in the presence of quasi-fixed inputs: A bootstrap data envelopment analysis approach

The objective of this paper is to measure the efficiency of high schools in Tunisia. We use a statistical Data Envelopment Analysis (DEA)-bootstrap approach with quasi-fixed inputs to estimate the precision of our measure. To do so, we developed a statistical model serving as the foundation of the Data Generation Process (DGP). The DGP is constructed such that we can implement both smooth homogeneous and heterogeneous bootstrap methods. Bootstrap simulations were used to estimate and correct the bias, and to construct confidence intervals for the efficiency measures. The simulation results show that the efficiency measures are subject to sampling variations. The adjusted measure reveals that high schools with residence services would have to give up less than 12.1 percent of their resources on average to be efficient.Educational economics; Efficiency; Productivity; Data Envelopment Analysis; Bootstrap; Quasi-fixed inputs

Cobwebs in the Sky : Reggio\u27s Koyaanisqatsi as Hypertext

As many participants know, the annual Computers & Writing Conference provides good ideas for our classrooms and research. At the 2000 conference in Florida, a group of us sat in a hallway excited about a film we had just watched, the documentary Fast, Cheap, and Out of Control. It seemed to us that consideration of cutting-edge film could become more than a single screening, an after-hours diversion during the conference. We agreed that many recent films incorporated elements of hypermedia in their sensibilities, even composition. An obvious example was the 2000 feature film Time Code, confronting the viewer with four separate stories presented simultaneously via a four-way split screen

The Global Minimum Tax Agreement: An End to Corporate Tax Havens?

The June 2021 OECD Global Tax Agreement between countries advocates for a Global Minimum Corporate Tax (GMT) rate of 15%. In this article, Colleen Essid argues that if the negotiating countries manage to overcome roadblocks from countries such as Ireland and the potential hurdle of U.S. congressional approval, the GMT could mean the end of the modern-day tax haven.https://scholarship.law.slu.edu/lawjournalonline/1070/thumbnail.jp

Fusion of Multimodal Information in Music Content Analysis

Music is often processed through its acoustic realization. This is restrictive in the sense that music is clearly a highly multimodal concept where various types of heterogeneous information can be associated to a given piece of music (a musical score, musicians\u27 gestures, lyrics, user-generated metadata, etc.). This has recently led researchers to apprehend music through its various facets, giving rise to "multimodal music analysis" studies. This article gives a synthetic overview of methods that have been successfully employed in multimodal signal analysis. In particular, their use in music content processing is discussed in more details through five case studies that highlight different multimodal integration techniques. The case studies include an example of cross-modal correlation for music video analysis, an audiovisual drum transcription system, a description of the concept of informed source separation, a discussion of multimodal dance-scene analysis, and an example of user-interactive music analysis. In the light of these case studies, some perspectives of multimodality in music processing are finally suggested

Extending An Alternative: Writing Centers and Curricular Change

When our Writing Center staked its reputation and perhaps its survival on a proposal to change our first-year curriculum, we entered territory that would have been unthinkable to those in our field a few decades ago. Writing center directors and peer tutors may not like it, but the climate now is very different from the salad days of the 1980s, when scholars such as Tilly and John Warnock argued “it is probably a mistake for centers to seek integration into the established institution” (22). In both the United States and EU nations, we face curricular change driven by emerging technologies, administrative fiat, austerity programs at the national level, American state-house “quality assurance,” local institutional assessment, and even outsourcing to private firms. In today’s universities, focused on measurable outcomes and fiscal solvency, unless one has an ongoing and secure source of funding, it would be foolhardy not to seek such integration

Writing Centers & the Dark Warehouse University: Generative AI, Three Human Advantages

Institutions are scrambling, at an unaccustomed pace, to adapt to generative artificial intelligence. While justified concerns focus on plagiarism, the nature of student learning, and changes to assignments, recent scholarship has largely ignored the potential for faculty and staff unemployment that may accompany acceptance and deployment of the new technology. As we ponder seismic changes in higher education, one voice should join, indeed lead, campus discussions. Writing center professionals have proven adept at weathering technological changes, budget cuts, administrative big ideas, and professional marginalization for more than half a century. Early on, centers were sometimes dismissed as mere “fix-it shops” for the least-competent writers of academic prose. Recent scholarship reveals, however, that centers have at last moved from the precariat to earn respect as practitioners of effective writing pedagogy. This article discusses how writing-center professionals, exemplifying Greenleaf’s model of servant leadership and Bruffee\u27s theory of collaborative learning, may help in steering campus policy on AI. Thus far three affordances critical to in-person work at writing centers–metacognitive questioning, active listening, principles of fair use—lie beyond the reach of generative AI. This gap reveals “reverse salients,” areas when a rapidly advancing technology cannot meet its advertised promises. Writing center leadership on this issue could also model adaptation to AI outside academia, in ways that benefit those whose livelihoods stand most at risk of being replaced by a set of algorithms