Aquinas and Education for a Just Technological Society

There remains in the heart a yearning to return home to the medieval synthesis. Yet whole-hearted acceptance would be inauthentic. At the same time however, it is possible to gain enlightenment from Aquinas. Merleau-Ponty “assumes that we can clarify the choices of others through our own and ours through theirs, that we adjust one by the other and finally arrive at the truth.” This assumption seems valid. It is an opportune time to examine “the choices” of Aquinas. Not only because it is the 700th anniversary of his death, but also because of the time that has elapsed since many people in this country and Europe began to seriously question the relevance of Thomism for the complexities of the mid-20th century

Gauge Consistent Wilson Renormalization Group II: Non-Abelian Case

We give a wilsonian formulation of non-abelian gauge theories explicitly consistent with axial gauge Ward identitities. The issues of unitarity and dependence on the quantization direction are carefully investigated. A wilsonian computation of the one-loop QCD beta function is performed.Comment: 34 pages, 1 eps figure, latex2e. Minor changes, version to appear in Int. J. Mod. Phy

Universal decay law in charged-particle emission and exotic cluster radioactivity

A linear universal decay formula is presented starting from the microscopic mechanism of the charged-particle emission. It relates the half-lives of monopole radioactive decays with the $Q$-values of the outgoing particles as well as the masses and charges of the nuclei involved in the decay. This relation is found to be a generalization of the Geiger-Nuttall law in $\alpha$ radioactivity and explains well all known cluster decays. Predictions on the most likely emissions of various clusters are presented.Comment: 2 figure

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

What Does an Exemplary Middle School Mathematics Teacher Look Like? The Use of a Professional Development Rubric

A School University Research Network (SURN) committee composed of current mathematics teachers, central office math supervisors, building administrators, mathematicians, and mathematics educators researched numerous sources regarding best practices in mathematics instruction. The resulting professional development rubric synthesizes their findings and can serve a professional development role by providing teachers and administrators with a tool to develop clarity and consensus on best mathematics instructional practices, and how these practices are implemented in the classroom. It is also being used as a tool for cooperating teachers in their supervision of student teachers and as a reflective method for self-evaluation

Abrupt changes in alpha decay systematics as a manifestation of collective nuclear modes

An abrupt change in $\alpha$ decay systematics around the N=126 neutron shell closure is discussed. It is explained as a sudden hindrance of the clustering of the nucleons that eventually form the $\alpha$ particle. This is because the clustering induced by the pairing mode acting upon the four nucleons is inhibited if the configuration space does not allow a proper manifestation of the pairing collectivity.Comment: 6 pages, 3 figures, submitted to Phys. Rev. C, a few new references adde

Virtual Meson Cloud of the Nucleon and Intrinsic Strangeness and Charm

We have applied the Meson Cloud Model (MCM) to calculate the charm and strange antiquark distribution in the nucleon. The resulting distribution, in the case of charm, is very similar to the intrinsic charm momentum distribution in the nucleon. This seems to corroborate the hypothesis that the intrinsic charm is in the cloud and, at the same time, explains why other calculations with the MCM involving strange quark distributions fail in reproducing the low x region data. From the intrinsic strange distribution in the nucleon we have extracted the strangeness radius of the nucleon, which is in agreement with other meson cloud calculations.Comment: 9 pages RevTex, 4 figure

PlanT: Explainable Planning Transformers via Object-Level Representations

Planning an optimal route in a complex environment requires efficientreasoning about the surrounding scene. While human drivers prioritize importantobjects and ignore details not relevant to the decision, learning-basedplanners typically extract features from dense, high-dimensional gridrepresentations containing all vehicle and road context information. In thispaper, we propose PlanT, a novel approach for planning in the context ofself-driving that uses a standard transformer architecture. PlanT is based onimitation learning with a compact object-level input representation. On theLongest6 benchmark for CARLA, PlanT outperforms all prior methods (matching thedriving score of the expert) while being 5.3x faster than equivalentpixel-based planning baselines during inference. Combining PlanT with anoff-the-shelf perception module provides a sensor-based driving system that ismore than 10 points better in terms of driving score than the existing state ofthe art. Furthermore, we propose an evaluation protocol to quantify the abilityof planners to identify relevant objects, providing insights regarding theirdecision-making. Our results indicate that PlanT can focus on the most relevantobject in the scene, even when this object is geometrically distant.<br

Effect of gluon-exchange pair-currents on the ratio G(E(P))/G(M(P))

The effect of one-gluon-exchange (OGE) pair-currents on the ratio $\mu_p G_E^p/G_M^p$ for the proton is investigated within a nonrelativistic constituent quark model (CQM) starting from $SU(6) \times O(3)$ nucleon wave functions, but with relativistic corrections. We found that the OGE pair-currents are important to reproduce well the ratio $\mu_p G_E^p/G_M^p$. With the assumption that the OGE pair-currents are the driving mechanism for the violation of the scaling law we give a prediction for the ratio $\mu_n G_E^n/G_M^n$ of the neutron.Comment: 5 pages, 4 figure