Our Brain “Kant” Tell Us? A Kantian Perspective of how Neuroscience Challenges our Notions of Moral Responsibility and the Legal Implications

Neuroscientific research has not only proved to be vital in our increasing understanding of human nature, but has also led to much normative discourse revolving around morality and law. There has been much work done regarding how neuroscience should inform us on issues regarding moral responsibility. In this paper, I propose to employ a Kantian moral framework to consider these issues more carefully. I argue that generally, neuroscience cannot undermine the concept that rational beings are morally responsible for their actions. However, within the same Kantian moral philosophical framework, I will consider how it is that certain individuals can be excused for their actions or have their responsibility mitigated. This will be done by focusing on the interaction between neuroscientific evidence and the capacity of certain individuals to engage with categorical and hypothetical imperatives. I will consider how it is that neuroscientific evidence can serve as an a priori excusing condition for moral responsibility, and equally importantly, when it cannot. I then go on to explore how our understanding of the workings of the brain can improve legal doctrine. I suggest that neuroscience can help to demonstrate that certain psychological criteria underlying particular legal doctrine might be inaccurate, and to improve our sentencing policies in order to better fulfil both retributive and rehabilitative aims and the criminal law

Making information accessible for the conservation and use of biodiversity. A novel initiative to facilitate access to information and use of agricultural and tree biodiversity

Poster presented at Science Week 2014 - Bioversity International HQ, Rome (Italy), 24-27 Feb 201

Deformation of the Fermi surface in the extended Hubbard model

The deformation of the Fermi surface induced by Coulomb interactions is investigated in the t-t'-Hubbard model. The interplay of the local U and extended V interactions is analyzed. It is found that exchange interactions V enhance small anisotropies producing deformations of the Fermi surface which break the point group symmetry of the square lattice at the Van Hove filling. This Pomeranchuck instability competes with ferromagnetism and is suppressed at a critical value of U(V). The interaction V renormalizes the t' parameter to smaller values what favours nesting. It also induces changes on the topology of the Fermi surface which can go from hole to electron-like what may explain recent ARPES experiments.Comment: 5 pages, 4 ps figure

pH-, thermo- and electrolyte-responsive polymer gels derived from a well-defined, RAFT-synthesized, poly(2-vinyl-4,4-dimethylazlactone) homopolymer via one-pot post-polymerization modification

Well-defined stimulus-responsive polymer gels were prepared from poly(2-vinyl-4,4-dimethylazlatone) (PVDMA) via one-pot post-polymerization modification. VDMA homopolymers were reacted with diamine crosslinking agents and functional 1° or 2° amines to form polymer gels that swelled in organic solvents and, in many cases, aqueous solutions. A series of functional amine reagents, including N,N-dimethylethylenediamine (DMEDA), N,N-diethylethylenediamine (DEEDA), morpholine, 3-morpholinopropylamine (MPPA) and tetrahydrofurfurylamine (THFA), were chosen as functional amines to produce polymer gels containing environmentally sensitive species. 13C solid-state NMR and FTIR spectroscopic measurements confirmed complete conversion of the reactive scaffolds. pH-dependent swelling behavior at ambient temperature was observed in DMEDA-, DEEDA- and MPPA-modified hydrogels. Kinetic studies showed the swelling behaviors of DMEDA-modified hydrogels were regulated by cross-linker type and concentration in acidic water (pH = 4) at ambient temperature. The swelling ratio of hydrogels modified by DEEDA, MPPA and THFA also depended strongly on temperature, indicating successful synthesis of thermoresponsive gels. Furthermore, the concentration of added sodium sulfate played a significant role with respect to the swelling properties of MPPA-modified hydrogels. These smart materials may be of interest in the biomedical field as well as in other applications

Mechano-responsive polymer solutions based on CO2 supersaturation: shaking-induced phase transitions and self-assembly or dissociation of polymeric nanoparticles

Mechanical stimulation of supersaturated aqueous CO2 solutions is accompanied by a pH increase within seconds. In solutions of tailored homo- and AB diblock copolymers this is exploited to induce micelle formation, or, taking advantage of an aqueous upper critical solution temperature transition, nanoparticle disassembly

Band Structure Mapping of Bilayer Graphene via Quasiparticle Scattering

A perpendicular electric field breaks the layer symmetry of Bernal-stacked bilayer graphene, resulting in the opening of a band gap and a modification of the effective mass of the charge carriers. Using scanning tunneling microscopy and spectroscopy, we examine standing waves in the local density of states of bilayer graphene formed by scattering from a bilayer/trilayer boundary. The quasiparticle interference properties are controlled by the bilayer graphene band structure, allowing a direct local probe of the evolution of the band structure of bilayer graphene as a function of electric field. We extract the Slonczewski-Weiss-McClure model tight binding parameters as $\gamma_0 = 3.1$ eV, $\gamma_1 = 0.39$ eV, and $\gamma_4 = 0.22$ eV.Comment: 12 pages, 4 figure

Semi-federated learning: convergence analysis and optimization of a hybrid learning framework

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks

Federated-Learning-Based Client Scheduling for Low-Latency Wireless Communications

Motivated by the ever-increasing demands for massive data processing and intelligent data analysis at the network edge, federated learning (FL), a distributed architecture for machine learning, has been introduced to enhance edge intelligence without compromising data privacy. Nonetheless, due to the large number of edge devices (referred to as clients in FL) with only limited wireless resources, client scheduling, which chooses only a subset of devices to participate in each round of FL, becomes a more feasible option. Unfortunately, the training latency can be intolerable in the iterative process of FL. To tackle the challenge, this article introduces update-importance-based client scheduling schemes to reduce the required number of rounds. Then latency-based client scheduling schemes are proposed to shorten the time interval for each round. We consider the scenario where no prior information regarding the channel state and the resource usage of the devices is available, and propose a scheme based on the multi-armed bandit theory to strike a balance between exploration and exploitation. Finally, we propose a latency-based technique that exploits update importance to reduce the training time. Computer simulation results are presented to evaluate the convergence rate with respect to the rounds and wall-clock time consumption

Point Defects and Localized Excitons in 2D WSe2

Identifying the point defects in 2D materials is important for many applications. Recent studies have proposed that W vacancies are the predominant point defect in 2D WSe2, in contrast to theoretical studies, which predict that chalcogen vacancies are the most likely intrinsic point defects in transition metal dichalcogenide semiconductors. We show using first principles calculations, scanning tunneling microscopy (STM) and scanning transmission electron microscopy experiments, that W vacancies are not present in our CVD-grown 2D WSe2. We predict that O-passivated Se vacancies (O_Se) and O interstitials (Oins) are present in 2D WSe2, because of facile O2 dissociation at Se vacancies, or due to the presence of WO3 precursors in CVD growth. These defects give STM images in good agreement with experiment. The optical properties of point defects in 2D WSe2 are important because single photon emission (SPE) from 2D WSe2 has been observed experimentally. While strain gradients funnel the exciton in real space, point defects are necessary for the localization of the exciton at length scales that enable photons to be emitted one at a time. Using state-of-the-art GW-Bethe-Salpeter-equation calculations, we predict that only Oins defects give localized excitons within the energy range of SPE in previous experiments, making them a likely source of previously observed SPE. No other point defects (O_Se, Se vacancies, W vacancies and Se_W antisites) give localized excitons in the same energy range. Our predictions suggest ways to realize SPE in related 2D materials and point experimentalists toward other energy ranges for SPE in 2D WSe2

Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems

Integrated sensing and communications (ISAC) is an emerging critical technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RIS) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.Comment: 30 pages, 8 figures. This paper has been submitted to IEEE Transactions on Communication