On-Line Load Balancing with Task Buffer

On-line load balancing is one of the most important problems for applications with resource allocation. It aims to assign tasks to suitable machines and balance the load among all of the machines, where the tasks need to be assigned to a machine upon arrival. In practice, tasks are not always required to be assigned to machines immediately. In this paper, we propose a novel on-line load balancing model with task buffer, where the buffer can temporarily store tasks as many as possible. Three algorithms, namely LPTCP1_α, LPTCP2_α, and LPTCP3_β, are proposed based on the Longest Processing Time (LPT) algorithm and a variety of planarization algorithms. The planarization algorithms are proposed for reducing the difference among each element in a set. Experimental results show that our proposed algorithms can effectively solve the on-line load balancing problem and have good performance in large scale experiments

A critical discussion of calculated modulated structures, Fermi surface nesting and phonon softening in magnetic shape memory alloys Ni2_2Mn(Ga, Ge, Al) and Co2_2Mn(Ga, Ge)

A series of first principles calculations have been carried out in order to discuss electronic structure, phonon dynamics, structural instabilities and the nature of martensitic transformations of the Heusler alloys Ni$_2$Mn(Ga, Ge, Al) and Co$_2$Mn(Ga, Ge). The calculations show that besides electronic pecularities like Fermi--surface nesting, hybridizing optical and acoustic phonon modes are important for the stabilization of the modulated martensitic structures.Comment: 3 pages, 4 figures, JEMS-200

How Does Strict Parallelism Affect Security? A Case Study on the Side-Channel Attacks against GPU-based Bitsliced AES Implementation

Parallel cryptographic implementations are generally considered to be more advantageous than their non-parallel counterparts in mitigating side-channel attacks because of their higher noise-level. So far as we know, the side-channel security of GPU-based cryptographic implementations have been studied in recent years, and those implementations then turn out to be susceptible to some side-channel attacks. Unfortunately, the target parallel implementations in their work do not achieve strict parallelism because of the occurrence of cached memory accesses or the use of conditional branches, so how strict parallelism affects the side-channel security of cryptographic implementations is still an open problem. In this work, we make a case study of the side-channel security of a GPU-based bitsliced AES implementation in terms of bit-level parallelism and thread-level parallelism in order to show the way that works to reduce the side-channel security of strict parallel implementations. We present GPU-based bitsliced AES implementation as the study case because (1) it achieves strict parallelism so as to be resistant to cache-based attacks and timing attacks; and (2) it achieves both bit-level parallelism and thread-level parallelism (a.k.a. task-level parallelism), which enables us to research from multiple perspectives. More specifically, we first set up our testbed and collect electro-magnetic (EM) traces with some special techniques. Then, the measured traces are analyzed in two granularity. In bit-level parallelism, we give a non-profiled leakage detection test before mounting attacks with our proposed bit-level fusion techniques like multi-bits feature-level fusion attacks (MBFFA) and multi-bits decision-level fusion attacks (MBDFA). In thread-level parallelism, a profiled leakage detection test is employed to extract some special information from multi-threads leakages, and with the help of those information our proposed multi-threads hybrid fusion attack (MTHFA) method takes effect. Last, we propose a simple metric to quantify the side-channel security of parallel cryptographic implementations. Our research shows that the secret key of our target implementation can be recovered with less cost than expected, which suggests that the side-channel security of parallel cryptographic implementations should be reevaluated before application

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations. Conversational recommender systems (CRSs) learn user representation and provide accurate recommendations based on dialogue context, but rely on external knowledge. Large language models (LLMs) generate responses that mimic pre-sales dialogues after fine-tuning, but lack domain-specific knowledge for accurate recommendations. Intuitively, the strengths of LLM and CRS in E-commerce pre-sales dialogues are complementary, yet no previous work has explored this. This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of two collaborative approaches with two CRSs and two LLMs on four tasks of Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM can be very effective in some cases.Comment: EMNLP 2023 Finding

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.Comment: Accepted at COLING 202

Multiscale examination of cytoarchitectonic similarity and human brain connectivity

The human brain comprises an efficient communication network, with its macroscale connectome organization argued to be directly associated with the underlying microscale organization of the cortex. Here, we further examine this link in the human brain cortex by using the ultrahigh-resolution BigBrain dataset; 11,660 BigBrain profiles of laminar cell structure were extracted from the BigBrain data and mapped to the MRI based Desikan–Killiany atlas used for macroscale connectome reconstruction. Macroscale brain connectivity was reconstructed based on the diffusion-weighted imaging dataset from the Human Connectome Project and cross-correlated to the similarity of laminar profiles. We showed that the BigBrain profile similarity between interconnected cortical regions was significantly higher than those between nonconnected regions. The pattern of BigBrain profile similarity across the entire cortex was also found to be strongly correlated with the pattern of cortico-cortical connectivity at the macroscale. Our findings suggest that cortical regions with higher similarity in the laminar cytoarchitectonic patterns have a higher chance of being connected, extending the evidence for the linkage between macroscale connectome organization and microscale cytoarchitecture. The human brain connectome organization has been suggested to associate with cytoarchitecture similarity. Here, we utilize the state-of-the-art ultrahigh-resolution BigBrain dataset and diffusion-weighted imaging dataset to examine this association. Our results show that cortical regions with higher cytoarchitecture similarity are more likely to be connected, as well as connected by stronger white matter tracts. This work further extends our understanding of the interaction between macroscale cortico-cortical connectivity organization and microscale cortical cytoarchitecture

Achilles\u27 Heel: the Unbalanced Mask Sets May Destroy a Masking Countermeasure

Low Entropy Masking Scheme (LEMS) has attracted wide attention for its low-cost feature of small fixed mask sets in Side-Channel-Analysis (SCA). To achieve the expected side channel security, it is necessary to find a balanced mask set to reduce the correlations between key dependent variables and their corresponding leakages. However, the security proof of LEMS, based on an inadequate assumption, might lead to consequent mask sets proposed without balance property, which could cause vulnerable LEMS implementations. This paper focusing on correcting and improving this scheme, first gives the formal definitions of univariate balance property on mask sets and extends it to multivariate settings. From these definitions, we propose three fundamental properties to analyze the balance of mask sets in Rotating Sbox Masking (RSM), the most popular LEMS implementations. To demonstrate the definitions and properties, three state-of-the-art RSM mask sets were selected as research objects. The corresponding attacks when any properties violated distinctly indicate the necessity of evaluating the balance property of the mask set in advance (during the design phase). However, it is found impossible to get a mask set for the RSM with all three properties satisfied, which means the vulnerabilities of RSM scheme in its unbalanced mask set are unavoidable. Thus, this promising masking scheme may be broken for its unqualified mask set

Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia

Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder